Version 1
: Received: 4 March 2024 / Approved: 4 March 2024 / Online: 5 March 2024 (09:09:48 CET)
How to cite:
Leogrande, A.; Resta, E.; Costantiello, A. The Renunciation of Healthcare Services in the Italian Regions in the ESG Context. Preprints2024, 2024030203. https://doi.org/10.20944/preprints202403.0203.v1
Leogrande, A.; Resta, E.; Costantiello, A. The Renunciation of Healthcare Services in the Italian Regions in the ESG Context. Preprints 2024, 2024030203. https://doi.org/10.20944/preprints202403.0203.v1
Leogrande, A.; Resta, E.; Costantiello, A. The Renunciation of Healthcare Services in the Italian Regions in the ESG Context. Preprints2024, 2024030203. https://doi.org/10.20944/preprints202403.0203.v1
APA Style
Leogrande, A., Resta, E., & Costantiello, A. (2024). The Renunciation of Healthcare Services in the Italian Regions in the ESG Context. Preprints. https://doi.org/10.20944/preprints202403.0203.v1
Chicago/Turabian Style
Leogrande, A., Emanuela Resta and Alberto Costantiello. 2024 "The Renunciation of Healthcare Services in the Italian Regions in the ESG Context" Preprints. https://doi.org/10.20944/preprints202403.0203.v1
Abstract
In the following article, we estimate the Renunciation of Healthcare Services-RHS in Italian regions in the context of the Environmental, Social and Governance-ESG model during the period 2004-2022. The data were acquired from the ISTAT-BES dataset. The data were analyzed using the following econometric techniques: Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Square-WLS,. Results show that RHS tends to growth with the E-Component, is negatively associated to the S-Component, and positively associate with the G-Component within the ESG model. Furthermore, a clusterization with the unsupervised k-Means algorithm is presented and the results are discussed with a confrontation between optimal and suboptimal k values optimized with the Silhouette Coefficient. Finally, a confrontation among eight different machine-learning algorithms is performed to predict the future value of RHS. Outcomes show that the Simple Regression Tree is the best predictive algorithm and that the level of RHS is predicted to growth on average of 4.4% for the Italian regions. Results are critically discussed.
Keywords
Analysis of Health Care Markets; Health Behaviours; Health Insurance; Public and Private; Health and Inequality; Health and Economic Development; Government Policy; Regulation; Public Health
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
