This paper seeks to contribute to these different strands of literature, by providing empirical evidence of the determinants of the decision to insure and its implications on farmers’ welfare under uncertainty. We test for the role of crop biodiversity on the probability of adopting an insurance scheme. We then analyze the role of insurance and diversification on farmers welfare under risk. To this end we estimate a stochastic production function a’ la Antle (1983) where biodiversity and insurance are regressed against the three moments of the distribution of revenues. To take into account of the potential endogeneity of the variable insurance we estimate a treatment-effects model by using a full maximum likelihood estimator. The treatment-effects model considers the effect of an endogenously chosen insurance binary treatment on another endogenous continuous variable, conditional on two sets of independent variables. Data are drawn from a very rich panel data form Italy. We have access to 8500 farmers from 2004 to 2007 (more than 25000 observations). To our knowledge this is the first empirical analysis, in this strand of literature, that make use of such large set of information.
F. Adinolfi, F. Capitanio, S. Di Falco (2011). Natural Vs Financial Insurance in the Management of Weather Risk Exposure in the Italian Agriculture. EAAE.
Natural Vs Financial Insurance in the Management of Weather Risk Exposure in the Italian Agriculture
ADINOLFI, FELICE;
2011
Abstract
This paper seeks to contribute to these different strands of literature, by providing empirical evidence of the determinants of the decision to insure and its implications on farmers’ welfare under uncertainty. We test for the role of crop biodiversity on the probability of adopting an insurance scheme. We then analyze the role of insurance and diversification on farmers welfare under risk. To this end we estimate a stochastic production function a’ la Antle (1983) where biodiversity and insurance are regressed against the three moments of the distribution of revenues. To take into account of the potential endogeneity of the variable insurance we estimate a treatment-effects model by using a full maximum likelihood estimator. The treatment-effects model considers the effect of an endogenously chosen insurance binary treatment on another endogenous continuous variable, conditional on two sets of independent variables. Data are drawn from a very rich panel data form Italy. We have access to 8500 farmers from 2004 to 2007 (more than 25000 observations). To our knowledge this is the first empirical analysis, in this strand of literature, that make use of such large set of information.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.