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.
Mapping digital businesses with big data: Some early findings from the UK / M. Nathan, A.C. Rosso. - In: RESEARCH POLICY. - ISSN 0048-7333. - 44:9(2015 Nov), pp. 1714-1733. [10.1016/j.respol.2015.01.008]
Mapping digital businesses with big data: Some early findings from the UK
A.C. RossoUltimo
2015
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.File | Dimensione | Formato | |
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