Version 1
: Received: 30 December 2023 / Approved: 2 January 2024 / Online: 3 January 2024 (02:01:44 CET)
How to cite:
Resta, E.; Resta, O.; Costantiello, A.; Leogrande, A. The Hospital Emigration to Another Region in the Light of the Environmental, Social and Governance Model in Italy During the Period 2004-2021. Preprints2024, 2024010034. https://doi.org/10.20944/preprints202401.0034.v1
Resta, E.; Resta, O.; Costantiello, A.; Leogrande, A. The Hospital Emigration to Another Region in the Light of the Environmental, Social and Governance Model in Italy During the Period 2004-2021. Preprints 2024, 2024010034. https://doi.org/10.20944/preprints202401.0034.v1
Resta, E.; Resta, O.; Costantiello, A.; Leogrande, A. The Hospital Emigration to Another Region in the Light of the Environmental, Social and Governance Model in Italy During the Period 2004-2021. Preprints2024, 2024010034. https://doi.org/10.20944/preprints202401.0034.v1
APA Style
Resta, E., Resta, O., Costantiello, A., & Leogrande, A. (2024). The Hospital Emigration to Another Region in the Light of the Environmental, Social and Governance Model in Italy During the Period 2004-2021. Preprints. https://doi.org/10.20944/preprints202401.0034.v1
Chicago/Turabian Style
Resta, E., Alberto Costantiello and Angelo Leogrande. 2024 "The Hospital Emigration to Another Region in the Light of the Environmental, Social and Governance Model in Italy During the Period 2004-2021" Preprints. https://doi.org/10.20944/preprints202401.0034.v1
Abstract
The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Results show that HEAR is negatively associated to E, positively to S and negatively associated to the G within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.
Keywords
Analysis of Health Care Markets; Health Behaviors; Health Insurance; Public and Private; Health and Inequality; Health and Economic Development; Government Policy; Regulation; Public Health
Subject
Business, Economics and Management, Econometrics and Statistics
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.
