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Mapping digital businesses with big data: Some early findings from the UK

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  • Nathan, Max
  • Rosso, Anna

Abstract

Governments around the world want to develop their ICT industries. Researchers and policymakers thus need a clear picture of digital businesses, but conventional datasets and typologies tend to lag real-world change. We use innovative ‘big data’ resources to perform an alternative analysis for all active companies in the UK, focusing on ICT-producing firms. Exploiting a combination of observed and modelled variables, we develop a novel ‘sector-product’ approach and use text mining to provide further detail on key sector-product cells. We find that the ICT production space is around 42% larger than SIC-based estimates, with around 70,000 more companies. We also find ICT employment shares over double the conventional estimates, although this result is more speculative. Our findings are robust to various scope, selection and sample construction challenges. We use our experiences to reflect on the broader pros and cons of frontier data use.

Suggested Citation

  • Nathan, Max & Rosso, Anna, 2015. "Mapping digital businesses with big data: Some early findings from the UK," Research Policy, Elsevier, vol. 44(9), pages 1714-1733.
  • Handle: RePEc:eee:respol:v:44:y:2015:i:9:p:1714-1733
    DOI: 10.1016/j.respol.2015.01.008
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    Cited by:

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    2. Cassetta, Ernesto & Marra, Alessandro & Pozzi, Cesare & Antonelli, Paola, 2017. "Emerging technological trajectories and new mobility solutions. A large-scale investigation on transport-related innovative start-ups and implications for policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 1-11.
    3. Turrell, Arthur & Thurgood, James & Djumalieva, Jyldyz & Copple, David & Speigner, Bradley, 2018. "Using online job vacancies to understand the UK labour market from the bottom-up," Bank of England working papers 742, Bank of England.
    4. Max Nathan & Anna Rosso, 2017. "Innovative events," Development Working Papers 429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
    5. Ben Spigel & Fumi Kitagawa & Colin Mason, 2020. "A manifesto for researching entrepreneurial ecosystems," Local Economy, London South Bank University, vol. 35(5), pages 482-495, August.
    6. Neil Lee & Stephen Clarke, 2017. "Who gains from high-tech growth? High-technology multipliers, employment and wages in Britain," SPRU Working Paper Series 2017-14, SPRU - Science Policy Research Unit, University of Sussex Business School.

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    More about this item

    Keywords

    Big Data; Text mining; ICTs; Digital economy; Industrial policy; Firm-level analysis;
    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
    • L63 - Industrial Organization - - Industry Studies: Manufacturing - - - Microelectronics; Computers; Communications Equipment
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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