In 2030 the world economy is showing steady growth, following a period of sluggish productivity in the previous ten years. Demographic trends in developed economies have dampened labor supply, however new cohorts of more educated workers from developing countries have now entered the global workforce and are contributing to improved productivity and global income equality. The world’s skilled workers has increased by approximately 20 percent since 2018 and overall, developing countries will contribute most of the additional skilled workers in this period. Because of aging, however, the overall number of skilled workers in advanced economies is projected to decline by 2030.
The shift in global economic power away from established advanced economies towards emerging economies has closed global inequality between countries, however the relative importance of inequality within countries has steadily increased. In this environment, governments focus on improving their local situations and solving issues unique to their economic position.
Traditional education institutions remain the trusted source of learning and the most effective vehicle for jobs and prosperity. However, chronic skills shortages in advanced economies due to demographic change, automation and changing industry needs, which require the re-skilling of a vast number of displaced workers, has placed enormous pressure on traditional institutions, most of which are neither structured nor able to service these needs to the scale, at the speed, and in the way that learners expect. As a consequence, structural reform in the post secondary sector occurs, including amalgamations and closures of smaller, niche universities who are unable to keep up with changing learner needs and who do not have the infrastructure or breadth to deliver on new skill and disciplinary demands.
In advanced economies, the rise of a new breed of post secondary vocational providers, who are better able to deliver faster and more flexibly for the needs of the ‘new vocations’, emerge to meet re-skilling needs. Government funding is flowing to these providers and ‘light touch’ regulation focuses mainly on job outcomes, rather than internal quality processes. Companies and entire industries collaborate with education providers to deliver industry specific skills training to secure their human capital pipeline.
Global talent flows
An ‘education wave’ in developing countries now sees millions of skilled workers entering the global workforce. Globalization and technology allows these workers to engage in high value work across borders, while competition for jobs in their own country intensifies. Platforms that aggregate opportunities and match worker skills and availability with employer needs help to mitigate uneven global demand and supply of ‘new’ skills in developed and developing economies. Demand for English language learning remains high in developing countries, where more of their skilled workers are likely to be undertaking work for english-speaking organizations from developed economies.
Verify me, verify my skills
As a global, virtual labour force emerges, issues of authentication, security and verification becomes paramount. Technology advancement and significant cost reductions have made biometric authentication is commonplace for verifying both individuals and their skills. Distributed ledger technology such as blockchain, is used widely to provide a trusted record of skills and qualifications.
Investment and innovation
Governments remain the core funding source for K-12 and formal post-secondary education, which retains a high-cost structure. Fragmented efforts have not led to significant innovation in the traditional sector, also hampered by conservative regulatory environments. Private investment and venture capital has turned its focus to new models and disruptive alternatives, which are showing returns based on urgent market needs for scalability, flexibility and relevance, and which result in productive economic outcomes.
Comparing Scenarios
Scenarios do not predict the future, but present snapshots of a range of possible futures. They should paint a picture of ‘what could be’ with enough depth to be plausible, but not be too exact.
Each scenario represents the different ways in which key drivers have developed and interacted, and so they are not meant to be compared against each other. However, there are common aspects, implied in all scenarios which help to explain underlying thinking that supported their construction.
Where is this data coming from?
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