The Future of Skills: Employment in 2030

This report presents a novel mixed-methods approach for predicting the demand for skills in the US and UK economies by 2030. It combines expert human judgment with machine learning to generate directional predictions for occupation growth, identify skills likely to experience growth or decline, and determine human capital investments that could boost future demand.

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The research method combines expert judgment from foresight workshops with a machine-learning classifier trained on O*NET data. It uses Gaussian processes and heteroskedastic ordinal regression to predict the future demand for occupations and the skills associated with them. The method also assesses complementarities between skills and identifies potential new occupations based on high-demand locations in the feature space.

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The research utilizes foresight workshops and a machine-learning algorithm to analyze the impact of structural changes on employment. It employs the O*NET database, which details skills, knowledge, and abilities for over 1,000 occupations. The study finds that 9.6% of the US workforce and 8.0% of the UK workforce are in occupations likely to grow, while 18.7% (US) and 21.2% (UK) are in occupations likely to shrink. The results emphasize the importance of 21st-century skills, particularly interpersonal and cognitive skills, and suggest that future workforce demand will be shaped by a combination of factors including technology, globalization, and demographic changes.

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Categories: United States geographic scope | employment trends | 2017 publication year | 2030 time horizon | 2030s time horizon | English publication language | United Kingdom geographic scope | demographic shifts | education requirements | employment | expert judgment | globalization | machine learning | occupational growth | skills | skills demand | structural change | technology impact | work