IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/111sti2020.html
   My bibliography  Save this paper

Prospective Model Of Official Statistics For The Digital Age

Author

Listed:
  • Leonid Gokhberg

    (National Research University Higher School of Economics)

  • Tatiana Kuznetsova

    (National Research University Higher School of Economics)

  • Gulnara Abdrakhmanova

    (National Research University Higher School of Economics)

  • Konstantin Fursov

    (National Research University Higher School of Economics)

  • Elena Nechaeva

    (National Research University Higher School of Economics)

  • Sergey Shashnov

    (National Research University Higher School of Economics)

  • Anton Suslov

    (National Research University Higher School of Economics)

Abstract

This paper describes key aspects of the structural and functional transformation of the Russian state statistics system as a core element of the future National Data Management System. Issues such as establishing a dialogue between the statistics service and users, integrating data from various sources, and intelligent data processing in the context of the digitalization of the economy are considered. New approaches and mechanisms should integrate and advance all of the previously achieved best results in methodology, observation areas, metrics, and other domains. Improvement areas include providing higher-quality information for policy shaping, businesses, individuals, and external partners. National statistics is expected to present an interconnected, objectively measurable model of socioeconomic processes and phenomena based on relevant theoretical concepts. In addition to using various sources of data, a necessary feature of the new system will be its reliance on a consistent conceptual framework and approaches to interpreting data, which will make it possible to integrate various data sources in the first place. Users of intelligent statistics are becoming not only active participants in primary data collection, accumulation and application processes, but are turning into “smart” consumers who develop statistical thinking and are able to derive the greatest possible benefits from the use of statistical data. Such skills should become an inherent (and possibly mandatory) component of any

Suggested Citation

  • Leonid Gokhberg & Tatiana Kuznetsova & Gulnara Abdrakhmanova & Konstantin Fursov & Elena Nechaeva & Sergey Shashnov & Anton Suslov, 2020. "Prospective Model Of Official Statistics For The Digital Age," HSE Working papers WP BRP 111/STI/2020, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:111sti2020
    as

    Download full text from publisher

    File URL: https://wp.hse.ru/data/2020/07/21/1596923773/111STI2020.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    statistics; digital age; data; Russia;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

    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:hig:wpaper:111sti2020. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.