IDEAS home Printed from https://ideas.repec.org/p/cep/cepops/44.html
   My bibliography  Save this paper

Mapping Information Economy Businesses with Big Data: Findings for the UK

Author

Listed:
  • Max Nathan
  • Anna Rosso

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.

Suggested Citation

  • Max Nathan & Anna Rosso, 2014. "Mapping Information Economy Businesses with Big Data: Findings for the UK," CEP Occasional Papers 44, Centre for Economic Performance, LSE.
  • Handle: RePEc:cep:cepops:44
    as

    Download full text from publisher

    File URL: http://cep.lse.ac.uk/pubs/download/occasional/op044.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    2. Gilles Duranton & Diego Puga, 2001. "Nursery Cities: Urban Diversity, Process Innovation, and the Life Cycle of Products," American Economic Review, American Economic Association, vol. 91(5), pages 1454-1477, December.
    3. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
    4. Mendonca, Sandro & Pereira, Tiago Santos & Godinho, Manuel Mira, 2004. "Trademarks as an indicator of innovation and industrial change," Research Policy, Elsevier, vol. 33(9), pages 1385-1404, November.
    5. Harrison, Ann & Rodríguez-Clare, Andrés, 2010. "Trade, Foreign Investment, and Industrial Policy for Developing Countries," Handbook of Development Economics, in: Dani Rodrik & Mark Rosenzweig (ed.), Handbook of Development Economics, edition 1, volume 5, chapter 0, pages 4039-4214, Elsevier.
    6. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    7. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    8. Karl Aiginger, 2007. "Industrial Policy: A Dying Breed or A Re-emerging Phoenix," Journal of Industry, Competition and Trade, Springer, vol. 7(3), pages 297-323, December.
    9. Erik Brynjolfsson & Lorin M. Hitt, 2003. "Computing Productivity: Firm-Level Evidence," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 793-808, November.
    10. Max Nathan & Henry Overman, 2013. "Agglomeration, clusters, and industrial policy," Oxford Review of Economic Policy, Oxford University Press, vol. 29(2), pages 383-404, SUMMER.
    11. Harrison, Ann E. & Rodriguez-Clare, Andres, 2009. "Trade, Foreign Investment, and Industrial Policy," MPRA Paper 15561, University Library of Munich, Germany.
    12. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    13. Jeremiah E. Dittmar, 2011. "Information Technology and Economic Change: The Impact of The Printing Press," The Quarterly Journal of Economics, Oxford University Press, vol. 126(3), pages 1133-1172.
    14. Timothy Besley & Miguel Coelho & John Van Reenen, 2013. "Investing for Prosperity: Skills, Infrastructure and Innovation," National Institute Economic Review, National Institute of Economic and Social Research, vol. 224(1), pages 1-13, May.
    15. Sandner, Philipp G. & Block, Joern, 2011. "The market value of R&D, patents, and trademarks," Research Policy, Elsevier, vol. 40(7), pages 969-985, September.
    16. William Lehr, 2012. "Measuring the Internet: The Data Challenge," OECD Digital Economy Papers 194, OECD Publishing.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stathoulopoulos, Kostas & Mateos-Garcia, Juan, 2017. "Mapping without a map: Exploring the UK business landscape using unsupervised learning," SocArXiv ryxdk, Center for Open Science.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
    2. 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.
    3. Crass, Dirk & Schwiebacher, Franz, 2013. "Do trademarks diminish the substitutability of products in innovative knowledge-intensive services?," ZEW Discussion Papers 13-061, ZEW - Leibniz Centre for European Economic Research.
    4. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    5. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    6. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
    7. Shahid Yusuf, 2014. "There was once a Korean Model," Asian-Pacific Economic Literature, Asia Pacific School of Economics and Government, The Australian National University, vol. 28(2), pages 88-96, November.
    8. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    9. Block, Jörn H. & Fisch, Christian O. & Hahn, Alexander & Sandner, Philipp G., 2015. "Why do SMEs file trademarks? Insights from firms in innovative industries," Research Policy, Elsevier, vol. 44(10), pages 1915-1930.
    10. Max Nathan & Anna Rosso, 2017. "Innovative events," Development Working Papers 429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
    11. Jorge M. Agüero, 2019. "Information and Behavioral Responses with More than One Agent: The Case of Domestic Violence Awareness Campaigns," Working papers 2019-04, University of Connecticut, Department of Economics.
    12. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    13. Vu, Khuong & Hartley, Kris, 2018. "Promoting smart cities in developing countries: Policy insights from Vietnam," Telecommunications Policy, Elsevier, vol. 42(10), pages 845-859.
    14. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    15. Lopez Cordova,Jose Ernesto, 2020. "Digital Platforms and the Demand for International Tourism Services," Policy Research Working Paper Series 9147, The World Bank.
    16. Godart, Olivier N. & Görg, Holger, 2013. "Suppliers of multinationals and the forced linkage effect: Evidence from firm level data," Journal of Economic Behavior & Organization, Elsevier, vol. 94(C), pages 393-404.
    17. Michaud, Amanda & Rothert, Jacek, 2014. "Optimal borrowing constraints and growth in a small open economy," Journal of International Economics, Elsevier, vol. 94(2), pages 326-340.
    18. Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, Open Access Journal, vol. 12(3), pages 1-10, January.
    19. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 2-12, April.
    20. Nemlioglu, Ilayda & Mallick, Sushanta K., 2020. "Do innovation-intensive firms mitigate their valuation uncertainty during bad times?," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 913-940.

    More about this item

    Keywords

    Quantitative methods; firm-level analysis; Big Data; text mining; ICTs; digital economy; industrial policy;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cep:cepops:44. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://cep.lse.ac.uk/_new/publications/series.asp?prog=CEPOP .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (email available below). General contact details of provider: http://cep.lse.ac.uk/_new/publications/series.asp?prog=CEPOP .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.