IDEAS home Printed from https://ideas.repec.org/a/spr/metron/v81y2023i1d10.1007_s40300-023-00242-7.html
   My bibliography  Save this article

Multi-source statistics on employment status in Italy, a machine learning approach

Author

Listed:
  • Roberta Varriale

    (Directorate for methodology and statistical process design, Istat - Italian National Institute of Statistics
    Sapienza University of Rome)

  • Marco Alfo’

    (Sapienza University of Rome)

Abstract

In recent decades, National Statistical Institutes have started to produce official statistics by exploiting multiple sources of information (multi-source statistics) rather than a single source, usually a statistical survey. In this context, one of the research projects addressed by the Italian National Statistical Institute (Istat) concerned methods for producing estimates on employment in Italy using survey data and administrative sources. The former are drawn from the Labour Force survey conducted by Istat, the latter from several administrative sources that Istat regularly acquires from external bodies. We use machine learning methods to predict the individual employment status. This approach is based on the application of decision tree and random forest techniques, that are frequently used to classify large amounts of data. We show how to construct a “new” response variable denoting agreement of the data sources: this approach is shown to maximise the information we may derive by machine learning approach in some problematic cases. The methods have been applied using the R software.

Suggested Citation

  • Roberta Varriale & Marco Alfo’, 2023. "Multi-source statistics on employment status in Italy, a machine learning approach," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 37-63, April.
    • Handle: RePEc:spr:metron:v:81:y:2023:i:1:d:10.1007_s40300-023-00242-7
    DOI: 10.1007/s40300-023-00242-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40300-023-00242-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40300-023-00242-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ton de Waal & Arnout van Delden & Sander Scholtus, 2020. "Multi‐source Statistics: Basic Situations and Methods," International Statistical Review, International Statistical Institute, vol. 88(1), pages 203-228, April.
    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. M. Giovanna Ranalli & Jean-François Beaumont & Gaia Bertarelli & Natalie Shlomo, 2023. "Foreword to the special issue on “Survey Methods for Statistical Data Integration and New Data Sources”," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 1-3, April.

    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. Camboni, Riccardo & Corsini, Alberto & Miniaci, Raffaele & Valbonesi, Paola, 2021. "Mapping fuel poverty risk at the municipal level. A small-scale analysis of Italian Energy Performance Certificate, census and survey data," Energy Policy, Elsevier, vol. 155(C).
    2. Szymkowiak Marcin & Wilak Kamil, 2021. "Repeated weighting in mixed-mode censuses," Economics and Business Review, Sciendo, vol. 7(1), pages 26-46, March.
    3. Rocci Fabiana & Varriale Roberta & Luzi Orietta, 2022. "Total Process Error: An Approach for Assessing and Monitoring the Quality of Multisource Processes," Journal of Official Statistics, Sciendo, vol. 38(2), pages 533-556, June.

    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:spr:metron:v:81:y:2023:i:1:d:10.1007_s40300-023-00242-7. 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.

    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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.