Using Text Data to Improve Industrial Statistics in the UK

Alex Bishop, Juan Mateos-Garcia () and George Richardson

Economic Statistics Centre of Excellence (ESCoE) Discussion Papers from Economic Statistics Centre of Excellence (ESCoE)

Abstract: We use business website data to explore the limitations of the Standard Industrial Classification taxonomy and develop a prototype for a bottom-up industrial taxonomy based on semantic similarities between company descriptions. This prototype makes it possible to decompose uninformative SIC codes into granular industries, build user-driven industry groups which might be of interest to policymakers (e.g. 'green economy') and build indices of local economic composition that are more strongly associated with local economic performance than those based on the SIC taxonomy. We consider potential avenues to combine official and bottom-up taxonomies in order to improve our understanding the economy and inform economic policy.

Keywords: emerging industries; industrial policy; industrial taxonomy; machine learning; web data (search for similar items in EconPapers)
JEL-codes: C81 L52 R12 (search for similar items in EconPapers)
Date: 2022-01
New Economics Papers: this item is included in nep-big, nep-dem and nep-geo
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Citations: View citations in EconPapers (4)

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