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Alternative Specifications of Reference Income Levels in the Income Stabilization Tool

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

Abstract

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.

Keywords

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.

Notes

Acknowledgments

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.

References

  1. Atwood, J., Shaik, S., & Watts, M. (2003). Are crop yields normally distributed? A reexamination. American Journal of Agricultural Economics, 85(4), 888–901.CrossRefGoogle Scholar
  2. Barnett, B. J., & Coble, K. H. (2012). Understanding regional differences in farm policy preferences. American Journal of Agricultural Economics, 94(2), 528–534.CrossRefGoogle Scholar
  3. Bielza Diaz-Caneja, M., Conte, C. G., Dittmann, C., Gallego Pinilla, J., & Stroblmair, J. (2008). Agricultural insurance schemes. ispra, Italy: European Commission, Joint Research Center Institute for the Protection and Security of Citizens JRC.Google Scholar
  4. Chavaz, J. (2010). Summary: The swiss milk market. panel of the high level experts group on milk. Brussels, Belgium: Deputy Director General, Federal Office for Agriculture, Bern, Switzerland.Google Scholar
  5. Cooper, J. C. (2010). Average crop revenue election: A revenue-based alternative to price-based commodity payment programs. American Journal of Agricultural Economics, 92(4), 1214–1228.CrossRefGoogle Scholar
  6. dell’Aquila C., & Cimino O. (2012). Stabilization of farm income in the new risk management policy of the EU: A preliminary assessment for Italy through FADN data, Paper presented at the 126th EAAE Seminar: New challenges for EU agricultural sector and rural areas. Which role for public policy?, Italy: Capri.Google Scholar
  7. EC. (2009). Income variability and potential cost of income insurance for EU. Brussels, Belgium: Directorate-General for Agriculture and Rural Development, European Commission.Google Scholar
  8. EC. (2011). Proposal for a regulation of the european parliament and of the council on support for rural development by the European agricultural fund for rural development (EAFRD). Brussels, Belgium: European Commission.Google Scholar
  9. El Benni, N. (2012). Einkommensrisiko in der Schweizer Landwirtschaft und Risikomanagementinstrumente. Bern, Switzerland: Report on behalf of the Swiss Federal Office for Agriculture.Google Scholar
  10. El Benni, N., & Finger, R. (2013). Gross revenue risk in swiss dairy farming. Journal of Dairy Science, 96(2), 936–948.CrossRefGoogle Scholar
  11. El Benni, N., Finger, R., & Meuwissen, M. (2013). Potential effects of the income stabilization tool in swiss agriculture, Paper presented at the 133rd EAAE Seminar. Developing integrated and reliable modeling tools for agricultural and environmental policy analysis, Chania, Greece, 15–16 June, 2013.Google Scholar
  12. Finger, R. (2013). Investigating the performance of different estimation techniques for crop yield data analysis in crop insurance applications. Agricultural Economics, 44(2), 217–230.CrossRefGoogle Scholar
  13. Finger, R., & El Benni, N. (2014). A note on the effects of the Income Stabilisation Tool on income inequality in agriculture. Journal of Agricultural Economics. (in Press).Google Scholar
  14. Glauber, J. W. (2013). The growth of the federal crop insurance program, 1990–2011. American Journal of Agricultural Economics, 95(2), 482–488.CrossRefGoogle Scholar
  15. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust statistics. New York, USA: Wiley.Google Scholar
  16. Harri, A., Coble, K. H., Erdem, C., & Knight, T. O. (2009). Crop yield normality: A reconciliation of previous research. Review of Agricultural Economics, 31(1), 163–182.CrossRefGoogle Scholar
  17. Hausheer Schnider, J. (2011). Glossar der zentralen auswertung von buchhaltungsdaten, research station. Ettenhausen, Switzerland: Agroscope Reckenholz-Tänikon ART.Google Scholar
  18. Hennessy, D. A. (2009). Crop yield skewness and the normal distribution. Journal of Agricultural and Resource Economics, 34(1), 34–52.Google Scholar
  19. Huber, P. J. (1972). The 1972 Wald lecture robust statistics: A review. The Annals of Mathematical Statistics, 43(4), 1041–1067.CrossRefGoogle Scholar
  20. Just, R. E., & Weninger, Q. (1999). Are crop yields normally distributed? American Journal of Agricultural Economics, 81(2), 287–304.CrossRefGoogle Scholar
  21. Ker, A. P., & Goodwin, B. K. (2000). Nonparametric estimation of crop insurance rates revisited. American Journal of Agricultural Economics, 82(2), 463–478.CrossRefGoogle Scholar
  22. Ker, A. P., & Coble, K. H. (2003). Modeling conditional yield distributions. American Journal of Agricultural Economics, 85(2), 291–304.CrossRefGoogle Scholar
  23. Liesivaara P., Myyrae, S., & Jaakkola, A. (2012). Feasibility of the income stabilisation tool in finland, Paper presented at the 123rd EAAE Seminar: Price volatility and farm income stabilisation-modelling outcomes and assessing market and policy based responses, Dublin, Ireland.Google Scholar
  24. LZV (2008). Verordnung über den landwirtschaftlichen Produktionskataster und die Ausscheidung von Zonen, Stand 1. Januar 2008. http://www.admin.ch/ch/d/sr/912_1/index.html
  25. Mann, S., & Gairing, M. (2011). Post milk quota experiences in Switzerland. EuroChoices, 10, 16–21.CrossRefGoogle Scholar
  26. Maronna, R. A., Martin, R. D., & Yohai, V. J. (2006). Robust statistics—theory and methods. New York, USA: Wiley.CrossRefGoogle Scholar
  27. Mary, S., Santini, F., & Boulanger, P. (2013). An ex-ante assessment of CAP income stabilisation payments using a farm house hold model, paper presented at the 87th annual conference of the agricultural economics society, UK: University of Warwick 8–10 April, 2013.Google Scholar
  28. Meier, B. (2000). neue methodik fuer die zentrale auswertung von buchhaltungsdaten an der FAT. Ettenhausen, Switzerland: Research Station Agroscope Reckenholz-Taenikon ART.Google Scholar
  29. Meuwissen, M. P. M., Huirne, R. B. M., & Skees, J. R. (2003). Income insurance in European agriculture. EuroChoices, 2, 12–17.CrossRefGoogle Scholar
  30. Meuwissen M. P. M., van Asseldonk, M., Pietola, K., Hardaker, B., & Huirne, R. (2011). Income insurance as a risk management tool after 2013 CAP reforms?, Paper presented at the EAAE 2011 Congress, Zurich, Switzerland: Change and Uncertainty August 30–September 2, 2011.Google Scholar
  31. Payton, M. E., Greenstone, M. H., & Schenker, N. (2003). Overlapping confidence intervals or standard error intervals: What do they mean in terms of statistical significance? Journal of Insect Science, 3(34), 1–6.Google Scholar
  32. Pigeon, M., Henry de Frahan, B., Denuit, M. (2012). Actuarial evaluation of the EU proposed farm income stabilisation tool, Paper presented at the 123rd EAAE Seminar: Price Volatility and Farm Income Stabilisation-Modelling Outcomes and Assessing Market and Policy Based Responses, Dublin, Ireland, 2012.Google Scholar
  33. Ramirez, O. A., Misra, S., & Field, J. (2003). Crop-yield distributions revisited. American Journal of Agricultural Economics, 85(1), 108–120.CrossRefGoogle Scholar
  34. Turvey, C. G. (2012). Whole farm income insurance. Journal of Risk and Insurance, 79(2), 515–540.CrossRefGoogle Scholar
  35. You, J. (1999). A monte carlo comparison of several high breakdown and efficient estimators. Computational Statistics and Data Analysis, 30(2), 205–219.CrossRefGoogle Scholar
  36. Zulauf, C. R., Dicks, M. R., & Vitale, J. D. (2008). Average Crop Revenue Election (ACRE) farm program: Provisions, policy background, and farm decision analysis. Choices, 23, 29–35.Google Scholar

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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