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Public-private insurance for the management of natural disasters

Perazzini, Selene (2020) Public-private insurance for the management of natural disasters. Advisor: Pammolli, Prof. Fabio. Coadvisor: Gnecco, Prof. Giorgio Stefano . pp. 158. [IMT PhD Thesis]

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Abstract

Natural disasters can compromise the economy, solidity, and social well-being of entire nations. To cope with natural risks, some countries have established public-private partnerships with the insurance industry, with generally satisfactorily outcomes. The aim of this thesis is to investigate the role of these partnerships. Chapter 2 reviews the international experience and investigates the main weaknesses of the public-private insurance systems currently in force, that can be traced back to poor risk understanding and inadequate governance. Including risk management into development plans can help ensuring effectiveness of risk reduction, while a more inclusive approach can achieve a better risk understanding. The following three chapters are devoted to the Italian case study and define a public-private insurance scheme for earthquakes and floods. As a first step, Chapter 3 estimates expected losses per individual and municipality through risk-modeling. Chapter 4 defines the insurance model, that departs from the existing literature by describing a public-private insurance intended to relieve the financial burden that natural events place on governments, while at the same time assisting individuals and protecting the insurance business. Though earthquakes generate expected losses that are almost six times greater than floods, we found that the amount of public funds needed to manage the two perils is almost the same. Lastly, Chapter 5 tests whether jointly managing the two perils can counteract the negative impact of spatial correlation. Some benefit from risk diversification emerged, though the probability of the government having to inject further capital is still considerable.

Item Type: IMT PhD Thesis
Subjects: H Social Sciences > HB Economic Theory
PhD Course: Economics management and data science
Identification Number: 10.6092/imtlucca/e-theses/321
Date Deposited: 09 Dec 2020 08:03
URI: http://e-theses.imtlucca.it/id/eprint/321

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