The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool

TL;DR

This paper evaluates the effectiveness of the EU's CAP in stabilizing and increasing farm income in Italy, focusing on income transfer efficiency and the potential of Machine Learning to improve income stabilization tools.

Contribution

It introduces a novel analysis of CAP's income transfer efficiency and applies Machine Learning to enhance the Income Stabilisation Tool's ratemaking accuracy in Italy.

Findings

CAP support translates to a significant increase in farm income.

Machine Learning models improve indemnity prediction accuracy.

The proposed models ensure financial stability of the income stabilization scheme.

Abstract

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.