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Citizen-Generated Data and Official Statistics: an application to SDG indicators

Author

Listed:
  • Monica Pratesi
  • Claudio Ceccarelli
  • Stefano Menghinello

Abstract

Official statistics are collected and produced by national statistical institutions (NSIs) based upon standardized questionnaire forms and a priori designed survey frame. Although the response to NSIs' surveys is mandatory for respondent units, increasing disaffection in replying to official surveys is a common trend across many advanced countries. This work explores the possibility to use Citizen-Generated Data (CGD) as a new information source for the compilation of official statistics. CGD represent a unique and still unexploited data source that share some key characteristics with Big Data, while they present some specific features in terms of information relevance and data generating process. Given the relevance of CGD to reduce the information gap between the demand and supply of new or more robust Sustainable Development Goals (SDG) indicators, the experimental setting to assess the data quality of CGD refers to different ways to integrate official statistics and CGD. Istat collects CGD within the framework of a pilot survey focused on key SDG indicators, and the appropriate methodological approach to assess data quality for official statistics is defined according to different data integration modalities.

Suggested Citation

  • Monica Pratesi & Claudio Ceccarelli & Stefano Menghinello, 2021. "Citizen-Generated Data and Official Statistics: an application to SDG indicators," Discussion Papers 2021/274, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    • Handle: RePEc:pie:dsedps:2021/274
    Note: ISSN 2039-1854
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    File URL: https://www.ec.unipi.it/documents/Ricerca/papers/2021-274.pdf
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    References listed on IDEAS

    as
    1. Gertler, Paul J, 1988. "A Latent-Variable Model of Quality Determination," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 97-104, January.
    2. Di Zio, Marco & Guarnera, Ugo & Rocci, Roberto, 2007. "A mixture of mixture models for a classification problem: The unity measure error," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2573-2585, February.
    3. Vermunt, Jeroen K. & Magidson, Jay, 2003. "Latent class models for classification," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 531-537, January.
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    More about this item

    Keywords

    Citizen-Generated Data (CGD); National statistical Institutions (NSIs); Sustainable Development Goals (SDG); Official statistics (OS); Data Science; Latent variables models; civil society organizations (CSOs);
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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