Ahead of his plenary session at the COBIS Annual Conference 2019, Owen Henkel shared key insights from the report ‘Future of Skills: Employment in 2030’.
The pace of economic change all but guarantees that a single degree or qualification earned in your teens or 20s will no longer be sufficient for your whole working life. Students graduating from high school today will have many, many jobs in their professional lives, some predictions place this number as high as 15. More troubling, is that we have no idea what many of those jobs will be: imagine trying to explain to someone 20 years ago the skills necessary to be an SEO specialist or to be the system administrator for a crypto-currency exchange.
So what does structural change, including but not limited to automation, mean for the future of work? And what does it mean for the skills that individuals will need to thrive in this emerging labour market? These are the questions that we have addressed in our research “Future of Skills: Employment in 2030”.
The implications for learning
The power of skills: investing in your future
First, we reviewed what the literature says about long-running trends impacting on UK and US labour markets. Then, armed with this information, we asked experts to debate the future of 30 occupations and label whether they expected them to rise or fall in demand by 2030. In addition, they were asked to state how certain or uncertain they were in making these judgements. Finally, we fed these results into an algorithm that generated predictions for all occupations. Specifically, we exploited a rich data set of 120 skills, abilities and knowledge requirements.
As a result, our model not only predicts which occupations are most likely to grow or decline, but which skills are most likely to be in demand as well; and skills, whatever job you’re in, are something that you can do something about. If you invest in the right skills, you can leave yourself in a better place to benefit from the opportunities of the future.
The rise and fall of occupations
First, let’s look at our predictions about employment. The below graph shows the results of thousands of simulations predicting that a specific occupation will employ more (or less) than it does today. The x-axis estimates the probability that each occupation group will experience higher relative demand and the y-axis shows the number of predicted jobs in that occupation for each simulation. Thus the areas on the right of the distribution are occupations where we expect rising demand; those on the left are expected to decline.
The model forecasts that only one in five workers are in occupations that will shrink. This figure is much lower than recent studies of automation have suggested. Occupations related to agriculture, trades and construction, which in other studies have been forecast to decline, exhibit more interesting and heterogeneous patterns in our research, suggesting that there may be pockets of opportunity throughout the skills ladder.
One in ten workers are actually in occupations that are likely to grow. These jobs are in sectors such as education and healthcare, where the overriding effect of technology is likely to be an improvement in outcomes, not a reduction in workforce. Therefore, as trends such as demographic change raise demand for these services, the prospect for employment is also likely to rise. Finally, the model forecasts that seven in ten workers are in jobs where there is great uncertainty about the future.
Skills for the future
This uncertainty suggests that results aren’t inevitable, raising the prospect that individuals in different occupations can improve their labour market chances if they invest in the right skills. Which leads us to what makes this approach really interesting: the ability to identify specific skills most likely associated with employability. Below is a list of the 20 skills most associated with job growth and employment. What’s striking is the strong emphasis on higher order cognitive competencies, such as creative and critical thinking.
Occupations are complex, and their skill requirements are not set in stone, but adapt to changes in the economic environment. In the retail banking sector, the spread of the ATM in the 1980s saw the role of bank tellers evolve into relationship managers to ensure their continued employability.
A forward-looking example is Customer Service occupations, which according to our model are likely to see a fall in future demand. However, skills that we typically associated with customer services, such as active listening, service orientation, and oral expression, are all skills that are likely to be in demand. This is likely because new jobs will require these skills in addition to other ones.
So while the advance of automation and artificial intelligence may feel like a losing battle to some, individuals will need to focus on developing the uniquely human skills identified in this research.
This underscores a deeper point: while disparaging the liberal arts has become a blood sport in some quarters, our results suggest that they will be more valuable in the future, not least in tech-based parts of the economy where the democratization of tools means a technical degree is no longer a decisive barrier to entry. This means that education systems will need to support better understanding, teaching practice, and assessment of the granular skills that will be in greater demand, moving beyond generic definitions of 21st century skills. Educational institutions for their part will need to provide support to educators as they are asked to teach these new skills. This could require significant retooling of teacher education or faculty incentives in educational institutions.
About the author
Owen Henkel is the Director of Pearson Affordable Learning Fund (PALF), which invests ‘patient capital’ in independently run, for-profit, education start-ups using innovative approaches to improving learning outcomes and increasing access, at scale. By investing in new educational ventures, Pearson helps to increase the quality of education for millions of learners and to identify what’s next in education, measuring, reporting on and improving student learning outcomes.
Prior to PALF, Owen worked as a consultant to ed-tech start-ups in Latin America, as an associate at McKinsey & Co., and as a Teach for America corps member in post-Katrina New Orleans.
Owen holds a dual MBA/MA at the University of Michigan where he focused on statistics, education technology, and impact investing. He is currently pursuing a PhD at the University of Oxford, focusing on Artificial Intelligence in Education, while continuing his role at PALF.
Owen’s research focuses on the potential that recent advances in ‘data science’ have for improving the analysis of student-level educational performance, specifically how large-scale assessments of early-stage literacy in low-and-middle-income countries and how recent advances in communication technology and artificial intelligence can be used to improve them.
