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Alternative Specifications of Reference Income Levels in the Income Stabilization Tool

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

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Fig. 4.1
Fig. 4.2

Notes

  1. 1.

    See El Benni et al. (2013) for discussions on the Swiss case.

  2. 2.

    For instance, the ACRE program in the US uses a 2-year ahead specification (Zulauf et al. 2008).

  3. 3.

    See, e.g., Huber (1972) for details on trade-offs with respect to robustness and efficiency for trimmed means.

  4. 4.

    Though Switzerland is not member of the European Union, we assume that an IST would be similar to specifications used in other European countries (El Benni 2012).

  5. 5.

    We use non-parametric bootstrap based on 9,999 replicates.

  6. 6.

    The definition of regions (valley, hill, mountain) is based on climatic and topographic conditions (LZV 2008) and is provided with the FADN data.

  7. 7.

    We did not increase the time period considered as the number of observations would have decreased considerably. For instance, increasing the time period to 10 years (2000–2009) would have reduced the available farm observations by more than the 50 %.

  8. 8.

    For instance, the average income in our sample was 71,569 for the year 2008 and 62,298 for the year 2009.

  9. 9.

    The expected probability of indemnification was 4.95 % and the expected average indemnification was 724 CHF/year.

  10. 10.

    Alternative robust regression approaches such as the MM-estimator may perform better than the M-estimator if trends approach or are equal to zero (You 1999). Considering alternative estimation techniques may thus contribute to overcome disadvantages of the regression approach in this situation.

  11. 11.

    Note that higher data availability is not relevant for the average-based approaches because only the previous 3 or 5 years of observations matter.

  12. 12.

    (Almost) all reported differences are significant. This is, however, also due to the large number of (simulated) observations underlying every point estimate (N = 9,999).

  13. 13.

    Higher income losses in 2009 are caused by an increase in cost levels, but in particular by decreasing output price levels, which was especially distinct for the milk market where the abandonment of the quota regime led to sharp decreases in milk prices (Chavaz 2010; Mann and Gairing 2011; El Benni and Finger 2013).

  14. 14.

    At this step, we removed farms with negative income observations. This step reduced the sample size from initially 1,274–1,242 for 2008 and to 1,227 for 2009.

  15. 15.

    This difference across zones is, however, only significant (at the 10 % level) if comparing the mountain and the valley region based on the difference between the regression and the 3-year average approach for the 1-year-ahead specification (not shown).

  16. 16.

    Clearly, no technique will dominate all others in all situations. However, the choice of the regression technique may be based on the characteristics underlying the situation at hand, e.g., with respect to income variability. For instance, following You (1999), the MM-estimator may allow to overcome problems of the regression approach if income trends approach zero.

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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.

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Correspondence to Robert Finger .

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Appendix

Appendix

Table 4.6 (Appendix)

Table A1 Correlation coefficients (Pearson/Spearman) between farm-level indemnities based on different methods of specification of the reference income level

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Finger, R., El Benni, N. (2014). Alternative Specifications of Reference Income Levels in the Income Stabilization Tool. In: Zopounidis, C., Kalogeras, N., Mattas, K., van Dijk, G., Baourakis, G. (eds) Agricultural Cooperative Management and Policy. Cooperative Management. Springer, Cham. https://doi.org/10.1007/978-3-319-06635-6_4

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