The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool
Author(s)
Biagini, Luigi
Date Issued
April 27, 2021
Type
Doctoral Thesis
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 first issue is investigated considering the dynamic dimension of income: the previous year has an undoubted impact on the current economic result. This aspect is a crucial point in this analysis because, differently from other studies, it is hypothesized that the present choice is fundamental sticky. The tools used for static modelling cannot be used under these circumstances because they remain affected by endogeneity, simultaneity bias, and omitted variables. To overcome these issues, the Generalised Method of Moments (GMM) is used together with a system of Instrumental Variables (IV) taken in level and first difference (à la Blundell-Bond).
The outcomes are in line with previous studies and economic expectations. Decoupled direct payments provide the highest contribution to agricultural incomes, followed by agri-environmental payments and on-farm investment subsidies. Coupled payments, indeed, have no significant impacts on farmers’ income. For the first time, we found the efficiency of CAP measures for three different economic dimensions of farms, to the best of our knowledge. We have obtained that large farms benefit from greater transfer efficiency than medium and small farms. These differences among instruments and across farms suggest that policy-participation costs may play a pivotal role, together with farms' economic structure, in determining the income transfer efficiency of CAP policies.
The second part of this thesis investigates the potential impact of the IST, currently introduced in the European CAP, to reduce farmers’ income risk. The analysis is motivated by the fact that this tool, while potentially useful, is not implemented yet apart few cases. One of the reasons is that, as is the case of all new insurance schemes, it is not easy to define the structure of the premiums (i.e., to develop a correct ratemaking). The main objectives are to assess the potential effects of introducing this tool on the Italian farms and assess whether Machine Learning procedures can develop adequate ratemaking.
The first objective has been pursued by developing a simulation based on a FADN panel data set of 3421 farms over seven years. This allowed investigating the effects of the introduction of the IST on (a) farm-level income variability, (ii) the expected level and variability of indemnifications at the level of mutual funds and (iii) the distribution of net benefits from this policy instrument across the farm population. We find that IST's introduction would lead to a significant reduction of income variability in Italian agriculture. Our results support establishing a national M.F. due to the high volatility of indemnification levels at more disaggregated (e.g. regional or sectoral) levels. Besides, our results propose that farmers’ contribution to mutual funds, i.e. premiums paid, should be modulated according to farm size. This reduces the inequality of the distribution of benefits of such tool within the farm population.
The assessment of the potential use of Machine Learning procedures to develop an adequate ratemaking is based on the following. 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.
This thesis fills some gaps in the analysis of “farm problem” and, in particular, by assessing the role of agricultural policies in enhancing and stabilizing farm income. While the first part of the analysis refers to past measures, the second refers to an innovative not-yet implemented instrument.
Thus, the thesis uses a relatively large set of methodologies that have not been applied so far in these areas of analysis, providing preliminary results regarding their possible pros and cons.
Furthermore, because the analyses rely on a widely available database, the proposed approaches could be used in future similar applications and other E.U. countries and consider different insurance tools.
Additional information
Dottorato di ricerca in Economia, management e metodi quantitativi
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