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Drawing on socio-psychology and economics literature contributions, this paper provides an original conceptual framework examining whether and how natural disasters exert any influence on nuptiality. Scholars suggest that disasters remind people about the transience of life and mortality. This feature triggers shifting towards intrinsic meaningful goals — such as commitment to relationships; at the same time, it fosters people's proximity and support-seeking from their security providing attachment figures and leads to accelerate life transition choices. In this view, disasters are expected to positively impact on people's intentions to marry. Such an effect is evident in case of natural hazards determining slight damage to physical capital (buildings, productive locations, etc.) which have limited economic consequences that do not hamper marriage intentions. Meanwhile, in the case of highly disruptive disasters, these positive psychological pushes are counterbalanced or even exceeded by the negative economic consequences of the disaster, which inhibit marriage intentions. These predictions are supported by an empirical analysis that relies on a difference-in-differences investigation of municipality-level nuptiality data collected in the Abruzzo region of Italy before and after the major 2009 L'Aquila earthquake. [ABSTRACT FROM AUTHOR]

On April 6, 2009, a strong earthquake (6.1 Mw) struck the city of L'Aquila, which was severely damaged as well as many neighboring towns. After this event, a digital model of the region affected by the earthquake was built and a large amount of data was collected and made available. This allowed us to obtain a very detailed dataset that accurately describes a typical historic city in central Italy. Building on this work, we propose a study that employs machine learning (ML) tools to predict damage to buildings after the 2009 earthquake. The used dataset, in its original form, contains 21 features, in addition to the target variable which is the level of damage. We are able to differentiate between light, moderate and heavy damage with an accuracy of 59%, by using the Random Forest (RF) algorithm. The level of accuracy remains almost stable using only the 12 features selected by the Boruta algorithm. In both cases, the RF tool showed an excellent ability to distinguish between moderate-heavy and light damage: around the 3% of the buildings classified as seriously damaged were labeled by the algorithm as minor damage. [ABSTRACT FROM AUTHOR]