Alternative Specifications of Reference Income Levels in the Income Stabilization Tool

  • Robert FingerEmail author
  • Nadja El Benni
Part of the Cooperative Management book series (COMA)


In this chapter, different approaches for the specification of reference income levels in the income stabilization tool (IST) are analyzed. The current proposal of the European Commission suggests a 3-year average or a 5-year Olympic average to specify the farm-level reference income that is used to identify if and to what extent a farmer is indemnified in a specific year. Using Monte Carlo simulations, we investigate the impact of income trends on indemnification if these average-based methods are used in the IST. In addition, we propose and investigate a regression-based approach that considers observed income trends to specify reference income levels. Furthermore, we apply these three different approaches to farm-level panel data from Swiss agriculture for the period 2003–2009. We find that average-based approaches cause lower than expected indemnification levels for farmers with increasing incomes, and higher indemnifications if farm incomes are decreasing over time. Small income trends are sufficient to cause substantial biases between expected (fair) and realized indemnification payments at the farm level. In the presence of income trends, average-based specifications of reference income levels will thus cause two major problems for the IST. First, differences between expected and realized indemnification levels can lead to significant mismatches between expected and real costs of the IST. Second, indemnity levels that do not reflect farm-level income losses do not allow achieving the actual purpose of the IST of securing farm incomes. Our analysis shows that a regression-based approach to specify reference income levels can contribute to bound potential biases in cases of decreasing or increasing income levels.


Regression Approach Income Data Farm Income Farm Type Reference Income 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Swiss Federal Office for Agriculture and the Swiss National Science Foundation. We would like to thank the Agroscope Reckenholz-Tanikon Research Station for providing the FADN data.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Wageningen UniversityWageningenThe Netherlands
  2. 2.Bonn UniversityBonnGermany
  3. 3.University of Applied Sciences HTW ChurChurSwitzerland
  4. 4.ETH ZurichZurichSwitzerland

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