Titolo: Metodologie matematico-statistiche per la valutazione delle partnership per la riqualificazione dei beni confiscati alla criminalità organizzata-

Sviluppo di modelli matematici e statistici per la valutazione economica e sociale degli investimenti che coinvolgono il settore pubblico e il terzo settore. Tali investimenti servono sia a soddisfare bisogni pubblici e sociali, sia a consentire al terzo settore coinvolto di percepire un ritorno economico-sociale.

SETTORE ERC: PE1_18 - SH1_5 -SH1_3

SSD coinvolti: STAT-01/A–Statistica; STAT-03/B–Statistica sociale; STAT-04/A– Matematica; Scienza delle Finanze ECON-03/A

Metodi matematici dell’economia e delle scienze attuariali e finanziarie.

N. Altri componenti afferenti al Dipartimento: 5 membri (Maria Rosaria Alfano , Ida Camminatiello , Antonella D'Ambra, Massimiliano Giacalone e Rosaria Lombardo)

 

This project aims to develop mathematical and statistical models for the economic and social evaluation of investments involving the public and third sector. Such investments serve both to meet public and social needs, and to allow the third sector involved to perceive an economic social return. Some examples include the transformation of dilapidated areas or buildings into areas or structures that generate socio-economic benefits by creating social value while ensuring economic feasibility and third sector engagement. An important and growing area of investment involving both the public and third sectors is the utilization of assets confiscated from organized crime, especially in countries like Italy, where Testo Unico Antimafia D.lgs 159/2011 (art. 48 comma 3) regulates the social reuse of such property. The proposed model uses mathematical and statistical tools for the evaluation of these projects considering uncertainty about benefits, possible strategic interactions between the public and third sectors, and the quantification of the socio-economic benefits for the government. These tools are based on the real options approach, game theory, and on statistical models such as generalized linear model, as well as Machine Learning methods for assessing willingness to pay (WTP), defined as the maximum amount an individual is willing to sacrifice to obtain a good or avoid something undesirable.