IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v58y2024i4d10.1007_s11135-023-01818-1.html
   My bibliography  Save this article

Top–down disaggregation of life expectancy up to municipal areas, using linear self-regressive spatial models

Author

Listed:
  • Vincenzo Basile

    (Federico II University of Naples)

  • Stefano Cervellera

    (University of Bari “Aldo Moro”)

  • Carlo Cusatelli

    (University of Bari “Aldo Moro”)

  • Massimiliano Giacalone

    (University of Campania “Luigi Vanvitelli”)

Abstract

The paper aims to analyse a statistical procedure for the definition of territorial indicators, such as the biometric function of life expectancy of citizens of a territory e0, applying a methodology of Top–Down spatial disaggregation, using census data from 2011 in Italy. Through spatial regressions with the methodology proposed in 1971 by Chow and Lin with the use of ISTAT elaborations of annual mortality tables, which are structured from the national level to the regional level, up to the smallest details of the main level, as a dependent variable and predictors a number k of census variables plus accidents in regression models, life expectancy can also be defined at municipal levels (not elaborated by ISTAT) and even at sub-municipal levels (Census Area). The structure of the 2011 census was characterized by 152 variables, collected with CAPI and universal CAWI survey on all the survey units, throughout the national territory, divided into administrative areas of competence and 402,677 more granular areas in census sections. This structure represents a very relevant and useful information asset for applying a spatial disaggregation of indicators, based on three fundamental assumptions: 1. Structural similarity, whereby the aggregate model and the disaggregate model are structurally similar, i.e., the relationships between the variables are valid both at the aggregate (Top) and at the disaggregate (Down) level, with the consequence that the regression parameters are the same in the two models. 2. Error similarity: for spatially correlated errors they present the same structure at both aggregate (Top) and disaggregate (Down) levels, significantly testing for zero spatial correlation; 3. Reliable indicators: the variables in the regression models are efficient predictors at both aggregates (Top) and disaggregate (Down) levels, estimable from model efficiency tests. As we see in the following, compared to others, the best predictors of the census and income variables show us a good interaction in terms of active regressors on the estimation variable.

Suggested Citation

  • Vincenzo Basile & Stefano Cervellera & Carlo Cusatelli & Massimiliano Giacalone, 2024. "Top–down disaggregation of life expectancy up to municipal areas, using linear self-regressive spatial models," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3703-3724, August.
    • Handle: RePEc:spr:qualqt:v:58:y:2024:i:4:d:10.1007_s11135-023-01818-1
    DOI: 10.1007/s11135-023-01818-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-023-01818-1
    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/s11135-023-01818-1?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.

    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:qualqt:v:58:y:2024:i:4:d:10.1007_s11135-023-01818-1. 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: 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.