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Beds in Health Facilities in the Italian Regions: A Socio-Economic Approach

Author

Listed:
  • Angelo Leogrande

    (LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro)

  • Alberto Costantiello

    (LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro)

  • Domenico Leogrande

    (UNIBA - Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro)

  • Fabio Anobile

Abstract

In this article, we consider the determinants of the beds in healthcare facilities-BEDS in the Italian regions between 2004 and 2022. We use the ISTAT-BES database. We use different econometric techniques i.e.: Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. The results show that the level of BEDS is positively associated, among others, to "General Doctors with a Number of Clients over the Threshold" and "Life Satisfaction", and negatively associated among others, to "Trust in Parties" and "Positive Judgment on Future Prospects". Furthermore, we apply a clusterization with the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters in terms of BEDS. Finally, we make a confrontation among eight machine-learning algorithms and we find that the best predictor is the ANN-Artificial Neural Network.

Suggested Citation

  • Angelo Leogrande & Alberto Costantiello & Domenico Leogrande & Fabio Anobile, 2023. "Beds in Health Facilities in the Italian Regions: A Socio-Economic Approach," Working Papers hal-04227495, HAL.
    • Handle: RePEc:hal:wpaper:hal-04227495
    Note: View the original document on HAL open archive server: https://hal.science/hal-04227495v1
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    Cited by:

    1. is not listed on IDEAS
    2. Costantiello, Alberto & Drago, Carlo & Arnone, Massimo & Leogrande, Angelo, 2024. "Regional Research Intensity and ESG Indicators in Italy: Insights from Panel Data Models and Machine Learning," MPRA Paper 124185, University Library of Munich, Germany.

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    Keywords

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    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I19 - Health, Education, and Welfare - - Health - - - Other

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