Nathan, Max;
Rosso, Anna;
Bouet, Francois;
(2014)
Mapping Information Economy Business with Big Data: Findings from the UK.
(NIESR Discussion Papers
).
NIESR: London, UK.
Text
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Abstract
Governments around the world want to develop their ICT and digital industries. Policymakers thus need a clear sense of the size and characteristics of digital businesses, but this is hard to do with conventional datasets and industry codes. This paper uses innovative ‘big data’ resources to perform an alternative analysis at company level, focusing on ICT-producing firms in the UK (which the UK government refers to as the ‘information economy’). Exploiting a combination of public, observed and modelled variables, we develop a novel ‘sector-product’ approach and use text mining to provide further detail on the activities of key sector-product cells. On our preferred estimates, we find that counts of information economy firms are 42% larger than SIC-based estimates, with at least 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.
Type: | Working / discussion paper |
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Title: | Mapping Information Economy Business with Big Data: Findings from the UK |
Publisher version: | https://www.niesr.ac.uk/publications/mapping-infor... |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | quantitative methods, firm-level analysis, Big Data, text mining, ICTs, digital economy, industrial policy |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis |
URI: | https://discovery.ucl.ac.uk/id/eprint/10175134 |
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