Breschi, Marco and Fornasin, Alessio and Gonano, Giovanna and Manfredini, Matteo and Seghieri, Chiara (2010) Demographic responses to short-term economic stress in North East Italy: Friuli, 18th-19th century. In: Kurosu, Satomi and Bengtsson, Tommy and Campbell, Cameron (eds). Demographic responses to economic and environmental crises, Kashiwa, Japan, Reitaku University. p. 65-78. Book Section.
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Friuli is a region of Northeastern Italy characterized by profound geographic and climatic differences. The role of agriculture was strictly connected with the geographic characteristics, getting more and more important going from north to south. In the plain, mixed agriculture was common, based partly on the binomium wheat-maize, partly on the cultivation of mulberry and vine. On the other hand, in the mountains the most common activity was cattle-breeding, while corn production was rather poor. Environmental context, forms of land tenure, types of cultivation, dependence or independence from the market of primary goods were all key factors in determining the complex relationship between population and resources. As we have demonstrated in a previous work, the most fragile populations in years of crisis were those with high levels of maize production and also consumption. Mountain populations were conversely the less affected in terms of mortality. A strong preventive check operated to limit access to marriage, which in turn brought to a drastic drop of births, therefore re-balancing the relationship between population and resources. This paper aims at analyzing the interdependence between economic crisis and demographic behaviors in Friuli. Our approach is here based on individual-level data. We have reconstructed the life-histories of the inhabitants of two communities of Friuli― one belonging to a maize-production area, the other situated on the mountains―for the period 1834-1900. We have therefore the opportunity to study much more in depth the consequences of economic crises on individuals and families. In this work we use two new historical time series: weekly series of price of indispensable goods, and daily series of meteorological data.
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