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The ESG Determinants of Mental Health Index Across Italian Regions: A Machine Learning Approach

Author

Listed:
  • Resta, Emanuela
  • Logroscino, Giancarlo
  • Tafuri, Silvio
  • Peter, Preethymol
  • Noviello, Chiara
  • Costantiello, Alberto
  • Leogrande, Angelo

Abstract

The following article analyses the relationship between the mental health index and the variables of the Environment, Social and Governance-ESG model in the Italian regions between 2004 and 2023. First of all, a static analysis is proposed aimed at identifying trends relating to mental health in the Italian regions with indication of the regional gaps. Subsequently, a clustering with k-Means algorithm is proposed. Below is a comparison of 11 machine learning algorithms for predicting the performance of the mental health index. Finally, the article offers some economic policy suggestions. The results are critically discussed in light of the scientific literature

Suggested Citation

  • Resta, Emanuela & Logroscino, Giancarlo & Tafuri, Silvio & Peter, Preethymol & Noviello, Chiara & Costantiello, Alberto & Leogrande, Angelo, 2024. "The ESG Determinants of Mental Health Index Across Italian Regions: A Machine Learning Approach," MPRA Paper 121204, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:121204
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    References listed on IDEAS

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    More about this item

    Keywords

    Mental Health Index; Machine Learning; ESG; Regional Inequalities;
    All these keywords.

    JEL classification:

    • 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

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