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Using Text Data to Improve Industrial Statistics in the UK

Author

Listed:
  • Alex Bishop
  • Juan Mateos-Garcia
  • George Richardson

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.

Suggested Citation

  • Alex Bishop & Juan Mateos-Garcia & George Richardson, 2022. "Using Text Data to Improve Industrial Statistics in the UK," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-01, Economic Statistics Centre of Excellence (ESCoE).
    • Handle: RePEc:nsr:escoed:escoe-dp-2022-01
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    Citations

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    Cited by:

    1. Occhini, Giulia & Tranos, Emmanouil & Wolf, Levi John, 2023. "Measuring a country’s digital industrial structure: commercial websites and weakly supervised classification to the rescue," SocArXiv h572n, Center for Open Science.
    2. Macedoni, Luca & Morrow, John & Tyazhelnikov, Vladimir, 2024. "Firms in Product Space: Adoption, Growth and Competition," CEPR Discussion Papers 18800, C.E.P.R. Discussion Papers.
    3. Juan Mateos-Garcia & George Richardson, 2022. "A Bottom Up Industrial Taxonomy for the UK. Refinements and an Application," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-29, Economic Statistics Centre of Excellence (ESCoE).
    4. Josh Martin & Rebecca Riley, 2023. "Productivity measurement - Reassessing the production function from micro to macro," Working Papers 033, The Productivity Institute.

    More about this item

    Keywords

    emerging industries; industrial policy; industrial taxonomy; machine learning; web data;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • L52 - Industrial Organization - - Regulation and Industrial Policy - - - Industrial Policy; Sectoral Planning Methods
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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