L’evoluzione dei fattori di rischio di povertà in Italia e l’impatto della crisi finanziaria

Il lavoro ha come obiettivo quello di analizzare le dinamiche dei fattori di rischio di povertà in Italia, focalizzando l'attenzione sulla liquidità delle famiglie, utilizzando tre livelli differenti di analisi. Il primo è un livello descrittivo che consiste nella costruzione delle serie storiche sull'incidenza della povertà e sulle abitudini di consumo delle famiglie, individuando i fattori di rischio di povertà tra le variabili socio-demografiche. Il secondo livello di analisi è la costruzione di un modello econometrico sui comportamenti di consumo delle famiglie in funzione della liquidità, facendo riferimento a uno studio relativo al mercato degli Stati Uniti, che individua le famiglie wealthy hand-to-mouth come famiglie soggette alla vulnerabilità alla povertà a causa di un investimento che ne ha limitato la liquidità. Il terzo livello è l'analisi del rischio di cadere in condizione di povertà, nel periodo delle due più recenti crisi, utilizzando la latent transition analysis, un modello a classi latenti che permette di stimare una variabile multidimensionale con più stati latenti e la probabilità di cambiare lo stato latente da un periodo all'altro. In tal senso, il modello permette di analizzare la vulnerabilità, intesa come la probabilità di cadere in condizione di povertà nel periodo successivo, tenendo conto dei risultati delle serie storiche e del modello econometrico

Effects of Uncertainty on Household Saving Rate

This PhD thesis attempts to investigate the role of economic uncertainty in driving the behaviour of household savings for six European countries. Focusing on three main sources of economic uncertainty Unemployment Risk, Fiscal Policy Uncertainty and Financial Crisis-Investment risk, I construct a Structural Vector Autoregressive (SVAR) model comprising of the Household Saving Rate, main variable of interest; the unemployment rate, to proxy labour income uncertainty and the risk of an income loss; the volatility of financial stock prices per each country, to detect for the presence of financial uncertainty/crisis; a policy uncertainty indicator, using alternatively the Policy Uncertainty Index devised by Baker, Bloom, and Davis (2012), the Debt to GDP ratio or the Government Surplus/Deficit to GDP ratio. A comparison among country-specific cumulative impulse response functions suggests that: 1. Household saving rate’s response to a change in investment risk is ambiguous, due to two counterbalancing effects: higher risk increases the volatility of future consumption and thus stimulates the accumulation of savings, while a more uncertain rate of return reduces the attractiveness of saving since it increases the risk of capital losses. 2. A labour uncertainty shock is detrimental or a booster for saving depending on whether the downward pressures on saving rate due to lower saving from unemployed people, prevails or not over the higher households propensity to save for precautionary reasons. 3. Fiscal policy instruments and related uncertainty influence the savings pattern of the private sector: private saving falls when governments reduce deficits (or the debt level) or run large budget surpluses and vice versa, as suggested by the Ricardian paradigm. I then propose another possible approach to the analysis, a Bayesian estimation of the reduced form VARs for the panel of European countries, as a Hierarchical Linear Model, with the future aim of improving estimation results

Disruption of Life Insurance Profitability in the Aftermath of the COVID-19 Pandemic

5noopenLife insurance profitability depends on reliable mortality risk projections and pricing. While the COVID-19 pandemic has caused disruptions around the world, this is a temporary mortality shock likely to dissipate. In this paper, we investigate the long-run impact of COVID-19 on life insurance profitability. Due to the long-run dynamics of the mortality characterised by a decreasing effect of the COVID-19 mortality acceleration, we suggest proactive mortality risk management by implementing prompt premium adjustments, in order to increase the resilience of the business.openCarannante, M.; D’Amato, V.; Fersini, P.; Forte, S.; MelisiCarannante, M.; D’Amato, V.; Fersini, P.; Forte, S.; Melisi

Machine learning due diligence evaluation to increase NPLs profitability transactions on secondary market

In this paper, we contribute to the topic of the non-performing loans (NPLs) business proftability on the secondary market by developing machine learning-based due diligence. In particular, a loan became non-performing when the borrower is unlikely to pay, and we use the ability of the ML algorithms to model complex relationships between predictors and outcome variables, we set up an ad hoc dependent random forest regressor algorithm for projecting the recovery rate of a portfolio of the secured NPLs. Indeed the proftability of the transactions under consideration depends on forecast models of the amount of net repayments expected from receivables and related collection times. Finally, the evaluation approach we provide helps to reduce the ”lemon discount” by pricing the risky component of informational asymmetry between better-informed banks and potential investors in particular for higher quality, collateralised NPLs

Characterisation of Neisseria meningitidis C strains causing two clusters in the north of Italy in 2007 and 2008.

Two clusters of invasive meningococcal disease in the north of Italy both due to serogroup C/ST-11 clonal complex are here described. The objective of the investigation was to analyse the phenotype and the genotype of meningococci involved in the two clusters which were of national relevance due to the fatal outcome of the majority of cases (six of the total of 10 cases). All the strains were C:2a:P1.5 ST-11/ET-37 clonal complex. Two pulsed field gel electrophoresis (PFGE) and variable number tandem repeats (VNTR) profiles were identified, one for each cluster. VNTRs were different from those detected in Italy for C/ST-11 strains isolated from sporadic cases in the same period. This laboratory surveillance report highlights the importance and the crucial role of molecular characterisation to confirm the relatedness among meningococci responsible for clusters of cases

Molecular characterization of Neisseria gonorrhoeae on non-cultured specimens from multiple anatomic sites

Introduction. The aim was to characterize Neisseria gonorrheae collected from multiple sites in the same patient without a cultured strain. The use of N. gonorrhoeae multiantigen sequence typing (NG-MAST) together with the gene sequence analysis of antimicrobial resistance (AMR) target genes permit to depict these samples.Materials and methods. Seventeen genital and extra-genital samples from eight patients (7 were men who have sex with men, MSM, and 1 women who have sex with men, WSM) with gonorrhoea symptoms were analyzed.For 7, of the 8 patients, conventional culture method has been used to identify gonorrhoea. All the samples were tested with the rapid molecular method CEPHEID. Amplification and sequencing of porB and tbpB, to identify the Sequence Type (ST) by NG-MAST, and penA, mtrR, porB1b, ponA genes were also performed.Antimicrobial susceptibility by Etest, for the available culture positive samples, was carried out.Results. For 7 patients it was obtained the ST (Sequence Type) and for 6 the complete sequence analysis of the antimicrobial resistance target genes. For the majority of them, samples collected from multiple sites confirm the presence of the same gonorrhoea strain. In particular, for 5 patients the same STs and changes in the AMR target genes were identified.Conclusion. Molecular characterization on non-cultured or culture negative specimens for gonorrhoea permit to predict the presence of the same strain in the patients with infection in multiple anatomic sites and the genetic antimicrobial susceptibility pattern

Temporary dense seismic network during the 2016 Central Italy seismic emergency for microzonation studies

In August 2016, a magnitude 6.0 earthquake struck Central Italy, starting a devastating seismic sequence, aggravated by other two events of magnitude 5.9 and 6.5, respectively. After the first mainshock, four Italian institutions installed a dense temporary network of 50 seismic stations in an area of 260 km2. The network was registered in the International Federation of Digital Seismograph Networks with the code 3A and quoted with a Digital Object Identifier ( https://doi.org/10.13127/SD/ku7Xm12Yy9 ). Raw data were converted into the standard binary miniSEED format, and organized in a structured archive. Then, data quality and completeness were checked, and all the relevant information was used for creating the metadata volumes. Finally, the 99 Gb of continuous seismic data and metadata were uploaded into the INGV node of the European Integrated Data Archive repository. Their use was regulated by a Memorandum of Understanding between the institutions. After an embargo period, the data are now available for many different seismological studies.Publishedid 1825T. Sismologia, geofisica e geologia per l'ingegneria sismicaJCR Journa