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Official Statistics 4.0: The Era of Digitisation and Globalisation

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Official Statistics 4.0

Abstract

What the future has in store for us is, not easily predictable. Certainly, foresight does not lie in the very nature of statisticians, who usually look in the rear-view mirror. Nevertheless, there are some trends or megatrends, the effects of which are not yet known in detail, to which one will undoubtedly have to adapt. Above all, because official statistics have the characteristics of an ocean liner whose course and speed can only be manoeuvred slowly, all trends must be interpreted in a forward-looking manner. If official statistics are to be sustained in their current position in five years’ time, then the necessary strategy must be established now. A simple continuation of the previous way of doing things, but including some ‘softer’ changes, is therefore not an option, even if this smooth manner of adaptation has been successful in the past. This chapter addresses the two megatrends of digitisation and globalisation. Obviously, it is not the purpose of this work to deal with their methodological, conceptual or technical aspects in this regard. Rather, it is about the consequences in terms of the statistical policy due to the changed statistical environment and conditions.

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Notes

  1. 1.

    See, for example, the data revolution group, established by the UN (http://www.undatarevolution.org/).

  2. 2.

    See the report ‘For a meaningful artificial intelligence—Towards a French and European Strategy’ (Villani 2018).

  3. 3.

    See Chap. 2.

  4. 4.

    See, for example, Haklay “Citizen Science and Policy: A European Perspective” (Haklay 2015).

  5. 5.

    See ECSA (2016).

  6. 6.

    See Soma et al. (2016a).

  7. 7.

    See Chap. 3.

  8. 8.

    The principles were presented in the Conference of European Statistical Stakeholders CESS 2016 in Budapest (Radermacher and Baldacci 2016).

  9. 9.

    See the “Bucharest Memorandum” (European Statistical System Committee 2018) and in particular the paper on a reference architecture for smart statistics (Ricciato et al. 2018).

  10. 10.

    See ESRG (2016), Stapel-Weber and Verrinder (2016).

  11. 11.

    See, for example, the Guide to Measuring Global Production (https://www.unece.org/index.php?id=42106).

  12. 12.

    See Moulton and Ven (2018).

  13. 13.

    See Stapel-Weber et al. (2018).

  14. 14.

    See https://www.oecd.org/sti/ind/inter-country-input-output-tables.htm or http://www.wiod.org/home.

  15. 15.

    See https://unstats.un.org/unsd/trade/globalforum/trade-value-added.asp or https://www.oecd.org/sti/ind/measuring-trade-in-value-added.htm.

  16. 16.

    See http://ec.europa.eu/eurostat/statistics-explained/index.php/Greenhouse_gas_emission_statistics_-_carbon_footprints.

  17. 17.

    For both indicators see the definition here https://www.un.org/esa/sustdev/natlinfo/indicators/methodology_sheets/consumption_production/domestic_material_consumption.pdf.

  18. 18.

    See http://ec.europa.eu/eurostat/statistics-explained/index.php/Business_registers.

  19. 19.

    See http://ec.europa.eu/eurostat/web/structural-business-statistics/structural-business-statistics/eurogroups-register.

  20. 20.

    See http://ec.europa.eu/eurostat/web/ess/esbr.

  21. 21.

    See https://ec.europa.eu/eurostat/cros/content/profiling-esbrs_en.

  22. 22.

    ‘However, till very recently, very few studies have questioned the figures they used, as if these figures were simply measuring a pre-existing reality. To prevent this “realist epistemology”, Alain Desrosières, who is the founder of a new way of thinking about statistics, proposed to talk not about “measurement” but about “quantifying process”: “The use of the verb ‘to measure’ is misleading because it overshadows the conventions at the foundation of quantification. The verb ‘quantify’, in its transitive form (‘make into a number’, ‘put a figure on’, ‘numericize’), presupposes that a series of prior equivalence conventions has been developed and made explicit […]. Measurement, strictly understood, comes afterwards […]. From this viewpoint, quantification splits into two moments: convention and measurement.”’ (Eyraud 2018, p. 103).

  23. 23.

    See for example, Roser (2018): ‘Most of us are wrong about how the world has changed (especially those who are pessimistic about the future)’.

  24. 24.

    Hans Rosling was a physician and statistician who, with his passion and his gift for explanation, managed to portray statistics completely new ways and use completely new dimensions of communication; he died in February 2017 (https://www.theguardian.com/global-development/2017/feb/07/hans-rosling-obituary and https://www.gapminder.org/).

  25. 25.

    See Haklay (2015): Citizen Science and Policy: A European Perspective (Haklay 2015).

  26. 26.

    See, for example, Chris Arnade’s blog ‘Why Trump voters are not “complete idiots”’ (Arnade 2016).

  27. 27.

    See, for example, http://www.teuro.de/focus/focus.html.

  28. 28.

    See “Dem Teuro auf der Spur” FOCUS Magazin | Nr. 22 (2002) https://www.focus.de/finanzen/news/wirtschaft-dem-teuro-auf-der-spur_aid_203686.html.

  29. 29.

    Brachinger, Der Euro als Teuro? Die wahrgenommene Inflation in Deutschland (Brachinger 2005).

  30. 30.

    1. Principle of citizen science Citizen science projects actively involve citizens in scientific endeavour that generates new knowledge or understanding. https://ecsa.citizen-science.net/sites/default/files/ecsa_ten_principles_of_citizen_science.pdf.

  31. 31.

    See https://www.weobserve.eu/ (WeObserve 2018).

  32. 32.

    http://webarchive.nationalarchives.gov.uk/20120104115644, http://www.ons.gov.uk/ons/about-ons/consultations/open-consultations/measuring-national-well-being/index.html.

  33. 33.

    Joost and Unteidig (2015), Gericke et al. (2018), Hisschemöller and Cuppen (2015).

  34. 34.

    Lehtonen, The multiple roles of sustainability indicators in informational governance: Between intended use and unanticipated influence (Lehtonen et al. 2016).

  35. 35.

    For example Jasanoff (2003).

  36. 36.

    See also J. Gray ‘Quand les mondes de données sont redistribués: Open Data, infrastructures de données et démocratie’ (Gray 2017).

  37. 37.

    For a more detailed introduction of governance, see Chap. 3.

  38. 38.

    See Soma et al. (2016b, p. 132).

  39. 39.

    A critical reflection on governance can be found in: Brown (2015, p. 122).

  40. 40.

    See Seltzer (1994).

  41. 41.

    See the “Manual on Government Deficit and Debt” (Eurostat 2016).

  42. 42.

    See https://unfccc.int/node/184700.

  43. 43.

    For data protection in the EU see https://ec.europa.eu/info/law/law-topic/data-protection/data-protection-eu_en.

  44. 44.

    EU policy: https://ec.europa.eu/digital-single-market/en/policies/building-european-data-economy.

  45. 45.

    Synopsis report of the public consultation on the revision of the Directive on the reuse of public sector information https://ec.europa.eu/digital-single-market/en/news/synopsis-report-public-consultation-revision-directive-reuse-public-sector-information.

  46. 46.

    See the report ‘Joining up Data for Better Statistics’ by the UK Statistics Authority (Office for Statistics Regulation 2018).

  47. 47.

    See the SIMSTAT approach in the revised regulation for EU business statistics (Eurostat 2015b).

  48. 48.

    ‘The concept of informational governance has emerged to capture these new challenges of environmental governance in the context of the Information Age. The logic of informational governance stems from the observation that information is not only a source for environmental governance arrangements, but also that it contributes to transformation of environmental governance institutions. Such societal transformation refers to how the raise of information technology, flows and networks leads to a fundamental restructuring of governance processes, structures, practices and power relations’ (Soma et al. 2016b, p. 131).

  49. 49.

    Wigglesworth, Can Big Data revolutionise policymaking by governments? (Wigglesworth 2018); Heubl, Night light images paint accurate picture of China GDP (Heubl 2018).

  50. 50.

    See in particular William Seltzer’s paper ‘Politics and Statistics: Independence, Dependence, or Interaction?” (Seltzer 1994).

  51. 51.

    See for example the Quality Framework of European Statistics here https://ec.europa.eu/eurostat/web/quality/european-statistics-code-of-practice.

  52. 52.

    See, for example, the European Statistical Trainings Programme (ESTP) here https://ec.europa.eu/eurostat/cros/content/european-statistical-tranings-programme-estp_en.

  53. 53.

    https://ec.europa.eu/eurostat/web/european-statistical-system/emos.

  54. 54.

    See the current learning outcomes of EMOS here https://ec.europa.eu/eurostat/cros/system/files/emos_learning_outcomes_2018.pdf.

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Radermacher, W.J. (2020). Official Statistics 4.0: The Era of Digitisation and Globalisation. In: Official Statistics 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-31492-7_4

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