In previous research, we used web data and machine learning methods to assess the limitations of the Standard Industrial Taxonomy (SIC) that measures the industrial structure of the UK, and developed a prototype taxonomy based on a bottom-up analysis of business website descriptions that could complement the SIC taxonomy and address some of its limitations. Here, we refine and improve that prototype taxonomy by doubling the number of SIC4 codes it covers, implementing a consequential evaluation strategy to select its clustering parameters, and generating measures of confidence about a company’s assignment to a text sector based on the distribution of its neighbours and its distance in semantic (text) space. We deploy the resulting taxonomy to segment UK local economies based on their sectoral, similarities and differences and analyse the geography, sectoral composition and comparative performance in a variety of secondary indicators recently compiled to inform the UK Government’s Levelling Up agenda. This analysis reveals significant links between the industrial composition of a local economy based on our taxonomy and a variety of social and economic outcomes, suggesting that policymakers should play strong attention to the industrial make-up of economies across the UK as they design and implement levelling-up strategies to reduce disparities between them.
