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Official Statistics 4.0 pp 11–52Cite as

Official Statistics—Public Informational Infrastructure

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Abstract

This chapter is about the ‘making of’ official statistics. The processes, structures and actors that are crucial for the high quality are to be presented. Official statistics are understood as industry that produces information. Consequently, in the presentation, the terms and concepts of modern management are used throughout. The first section starts with the business model of statistics with its dimensions of the processes (‘how’), the products (‘what’) and the producers (‘who’). It then deals with important overarching topics, such as quality management, national and international statistics and statistical confidentiality. With a look at the recent modernisation of the business model, the current status of Statistics 3.0 is summarised.

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Notes

  1. 1.

    See more detailed comments on the legal provisions in Radermacher/Bischoff “Article 338” (Radermacher and Bischoff 2018 forthcoming).

  2. 2.

    The European Free Trade Association (http://www.efta.int/about-efta/european-free-trade-association) and the European Economic Area (http://www.efta.int/eea) and for statistics http://www.efta.int/statistics.

  3. 3.

    This circular flow chart corresponds to widely accepted standards on the producer’s side, such as the Generic Statistical Business Process Model (GSBPM) (UNECE 2013) or the Generic Statistical Information Model (GSIM) (UNECE 2017).

  4. 4.

    This stovepipe approach in the organisation of work is further strengthened if the financing of statistical branches is separated in different budget lines.

  5. 5.

    This way of producing statistics in parallel but only weakly coordinated processes could be called a ‘vertical organisation’.

  6. 6.

    This way of producing statistics with exchangeable modules could be called a ‘horizontal organisation’.

  7. 7.

    See for example Bundesstatistikgesetz, § 5a Nutzung von Verwaltungsdaten (https://www.destatis.de/DE/Methoden/Rechtsgrundlagen/Statistikbereiche/Inhalte/010_BStatG.pdf?__blob=publicationFile).

  8. 8.

    See for example “Good practices when combining some selected administrative sources” https://ec.europa.eu/eurostat/cros/system/files/good_practices_administrative_data.pdf.

  9. 9.

    See for example Darabi (2017) or Kyi et al. (2012).

  10. 10.

    See the European statistical programme 2013–2017 (European Union 2011, p. 20).

  11. 11.

    This consideration and distinction are only recent. It results from the modernisation process of the last 20 years, which will be presented in a later section.

  12. 12.

    See for example Eurostat (2016b).

  13. 13.

    https://ec.europa.eu/eurostat/cros/content/what-emos_en.

  14. 14.

    See for example the case of income, consumption and wealth (Brandolini 2016).

  15. 15.

    See Deming (2000).

  16. 16.

    See OECD https://stats.oecd.org/glossary/ (Quality).

  17. 17.

    For the complexity inherent to multidisciplinarity see (Klein 2004).

  18. 18.

    EU Regulation 223: “The statistical principles set out in this paragraph are further elaborated in the Code of Practice” (European Union 2015: Art 12).

  19. 19.

    The 2017 edition of the CoP is based on 16 principles.

  20. 20.

    See ESGAB (2018).

  21. 21.

    See Eurostat (2018c).

  22. 22.

    ISI (2010).

  23. 23.

    Source United Nations (2014).

  24. 24.

    See for example ISTAT (2018) or INE Portugal (INE 2018).

  25. 25.

    See for example Destatis (2018) or Eurostat (2018d).

  26. 26.

    See for example Statistics Netherlands (CBS 2016a).

  27. 27.

    See EFQM (2013, p. 9).

  28. 28.

    See for example IMF (2017, 2018) or OECD (2015).

  29. 29.

    See for example Eurostat on Greece (European Commission 2010) or IMF on Argentina (IMF 2016).

  30. 30.

    See in particular “Towards robust quality management for European Statistics” (European Commission 2011).

  31. 31.

    See Eurostat (2018c).

  32. 32.

    See for example Foucault (1991).

  33. 33.

    ESA 2010 (Eurostat 2013b, pp. 273–74).

  34. 34.

    See definition here http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Labour_force_survey_(LFS).

  35. 35.

    See definition here http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Harmonised_index_of_consumer_prices_(HICP).

  36. 36.

    Definition: http://ec.europa.eu/eurostat/web/european-statistical-system.

  37. 37.

    Definition: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Intrastat.

  38. 38.

    Definition: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Excessive_deficit_procedure_(EDP).

  39. 39.

    Definition: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Harmonised_index_of_consumer_prices_(HICP).

  40. 40.

    Definition: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:EU_statistics_on_income_and_living_conditions_(EU-SILC).

  41. 41.

    Definition: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Labour_force_survey_(LFS).

  42. 42.

    Such as the German Data Forum RatSWD (https://www.ratswd.de/en/ratswd/development).

  43. 43.

    For further insight see https://www.unece.org/stats/mos/meth.html.

  44. 44.

    See the overview in http://www.q2018.pl/previous-q-conferences/; the first one was the quality conference 2001 in Stockholm, Sweden initiated by the LEG Quality [recommendation 14 (Lyberg et al. 2001)].

  45. 45.

    see in particular “The legacy of Rayner” (Guy and Levitas 2005, p. 7).

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  1. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Walter J. Radermacher

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Radermacher, W.J. (2020). Official Statistics—Public Informational Infrastructure. In: Official Statistics 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-31492-7_2

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