  Since its inception, the E.U.'s Common Agricultural Policy (CAP) aimed at
ensuring an adequate and stable farm income. While recognizing that the CAP
pursues a larger set of objectives, this thesis focuses on the impact of the
CAP on the level and the stability of farm income in Italian farms. It uses
microdata from a high standardized dataset, the Farm Accountancy Data Network
(FADN), that is available in all E.U. countries. This allows if perceived as
useful, to replicate the analyses to other countries. The thesis first assesses
the Income Transfer Efficiency (i.e., how much of the support translate to farm
income) of several CAP measures. Secondly, it analyses the role of a specific
and relatively new CAP measure (i.e., the Income Stabilisation Tool - IST) that
is specifically aimed at stabilising farm income. The assessment of the
potential use of Machine Learning procedures to develop an adequate ratemaking
in IST. These are used to predict indemnity levels because this is an essential
point for a similar insurance scheme. The assessment of ratemaking is
challenging: indemnity distribution is zero-inflated, not-continuous,
right-skewed, and several factors can potentially explain it. We address these
problems by using Tweedie distributions and three Machine Learning procedures.
The objective is to assess whether this improves the ratemaking by using the
prospective application of the Income Stabilization Tool in Italy as a case
study. We look at the econometric performance of the models and the impact of
using their predictions in practice. Some of these procedures efficiently
predict indemnities, using a limited number of regressors, and ensuring the
scheme's financial stability.
