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This study assesses the potential impact of the European Income Stabilization Tool (IST – a whole farm income risk management [RM] tool) within a farm cooperative specializing in vineyards and operating in a small area of production. The authors assess the conditions under which IST could improve the well-being of the associated farmers and, at the same time, improve financial sustainability. Financial aspects are of particular relevance since the characteristics of the cooperative cause the management of the tool to become potentially risky.

The analysis relies on a balanced panel dataset to report the production and economic characteristics of individual associated farms. This is the basis for simulating the implementation of the IST as described in the current European regulation. The expected utility approach is then used to assess the potential impact on farmers' well-being under different levels of risk aversion and premiums. The analysis of the IST annual cash flow allows for an accurate assessment of its financial sustainability.

The results suggest that the IST can improve farmers' well-being under plausible levels of risk aversion and premiums, making most farmers willing to support its implementation. Furthermore, the tool could be financially sustainable even if implemented in a specialized and geographically concentrated group of farms. In addition, the results suggest that the use of strategies such as the IST could help cope with negative annual balances by treating the financial sustainability of the fund.

The analysis adds to previous research on the IST by accounting for farmers' risk aversion. Furthermore, it is the first analysis that simulates the implementation of this tool in a sector-specific and concentrated group of farms. The results provide useful evidence for those subjects planning to implement the IST in small and specialized farming systems.

The purpose of this paper is to assess how direct payments (DPs) of the Common Agricultural Policy affect income and revenue variability faced by Italian farmers.

Balanced farm-level panel data are used to construct coefficients of variation over the period 2003-2012. Nonlinear robust regression techniques are used to measure the effect of DP, farm size, fixity in resources, labor intensity, farm production orientation, and specialization on the variability of farm income (FI) and farm revenue. This is done on the overall sample as well as on subsamples of farms located in different regions and belonging to different types of farming.

DPs have mixed effects on the variability of FI. While a negative and significant relationship is found on the whole national sample, this is not generally the case when models are run on the considered subsamples. On the contrary, DPs have always significant variability increasing effects on revenue. This suggests that DPs reduce the degree of risk that farmers face allowing them to engage in riskier activities. Thus, DPs are less effective than expected in terms of income stabilization because these distort farmers’ risk management behavior. Because of this, DPs could constrain the development of markets for risk management instruments and reduce the effectiveness of policies supporting the use of these instruments.

The analysis is inspired by El Benni et al. (2012) but uses a different approach, applies it to a different country, and yields different results. Volatility measures are calculated over more years, and the paper accounts for differences in farm production orientation and is not based on an unbalanced panel of farms. Because of these differences, the authors obtained different results regarding the correlation between DP and income and, even more, revenue variability. Finally, comparing the results of models referring to FI and farm revenue improves the author’s understanding of the impact of DP on farmers’ risk management behavior and allows interesting policy considerations.