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The impact of single farm payments on technical inefficiency of French crop farms

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Abstract

This paper analyses the effect of single farm payments (SFPs) introduced by the Luxembourg agreements (2003) of the Common Agricultural Policy, on the performance of crop farms in Eure-et-Loir, France, over the period 2005–2008. Technical inefficiency scores of these crop farms are first estimated. Then, the estimated technical inefficiency scores are regressed on SFPs received by farmers following a standard two-step procedure. The analysis shows a negative effect of SFPs on the technical inefficiency of Eure-et-Loir farms. This implies that subsidies granted to farms without production restrictions seem to reduce technical inefficiency.

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Notes

  1. One reason could be that farmers replace farming income by subsidies (Skevas et al. 2012).

  2. This ‘pessimistic’ view is one of the main arguments for the changes introduced in the CAP subsidy programs; these changes would reduce the incentives leading to inefficiency.

  3. This reform must not be mistaken with the reform that introduced the concept of decoupling (i.e. the Mac Sharry reform of 1992) where compensatory payments (or direct payments) were introduced to offset the reduction of the guaranteed prices. These compensatory payments shifted from the quantity produced to the sown area (and livestock heads), through the application of a common reference yield.

  4. Due to the fact that the 2003 reform came into effect in 2006 in France, data on SFPs were not available at the time the study was conducted.

  5. See Shepherd (1953, 1970) for more information on this distance function. In an output-oriented model, the inefficiency score is always greater or equal to one. This inefficiency score is the radial factor that, when multiplied by the observed output, gives the maximum output that can be reached, given the inputs used. For example, if θ is equal to 1.2, one can increase the quantity of output by 20% by keeping inputs unchanged.

  6. The 24 crops are wheat, durum wheat, spring barley, winter barley, irrigated corn, corn, oat, spring wheat, other cereals, protein pea, beet, potato, rapeseed, sunflower, flax, poppy, lucerne, other industrial crops, fodder, fruits, beans, peas, other vegetables and horticulture.

  7. We define largest crops as surfaces representing more than 5% of the UAA on average for at least 1 year in our sample.

  8. As opposed to intermediate inputs such as pesticide, fertiliser or energy, the total stock of capital is not fully consumed over the span of a year. In other words, we need to measure the service of capital or the user cost of capital transferred to the production during the production period. Since we do not have this exact value, it is customary to proxy it with capital depreciation (i.e. the ‘consumption of capital’). Moreover, when there is a proportional relationship between depreciation and the total asset value, using the former or the latter variable in linear program (5) does not affect the estimated technical inefficiency scores.

  9. The composition of these aggregate outputs is detailed in the Appendix.

  10. This assumption is supported by Butault et al. (2010) who show that France is divided into eight large regions to cover the diversity of soils, climates and pest pressure. One of these homogenous regions is ‘Centre-Poitou’ which includes the Eure-et-Loir département.

  11. This component is usually referred to as technological change as it captures the frontier shift. Under the assumption of no technological change, the frontier shift is entirely attributed to climatic condition changes.

  12. To compute the aggregate frontier and the resulting distance functions for each year, we use the Eure-et-Loir data in our sample (four inputs and three outputs). That is, instead of estimating for each year n distance functions (n being the number of farms), we estimate one frontier that aggregates all the farms. The various combinations of r and s (used to denote the years) allow to produce all the needed distance functions for the estimation of the Malmquist index and its decomposition.

  13. We chose 2007 for our base year because it is in the middle of the period. Note however that the results are not sensitive to the choice of the base year.

  14. Coupled subsidies are not considered here since they are endogenous (they are conditional on the output) and would introduce a bias in the estimation.

  15. The reference gives a url for a specific location on the INSEE website. A click on this link displays the exact series used in the paper.

References

  • Agreste (2007) Mémento de la statistique agricole. Centre-Val de Loire, 28 p.

  • Agreste (2011) Résultats-Données chiffrées. Centre-Val de Loire, no. 186, 4 p.

  • Agreste (2015). Mémento de la statistique agricole. Centre-Val de Loire, 32 p.

  • Arellano, M., & Bond, S. R. (1991). Some tests of specifications for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.

    Article  Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.

    Article  Google Scholar 

  • Benjamin, D. (1992). Household composition, labor markets, and labor demand: testing for separation in agricultural household models. Econometrica, 60(2), 287–322.

    Article  Google Scholar 

  • Bilodeau, D., Crémieux, P.-Y., & Ouellette, P. (2000). Hospital cost function in a non-market health care system. The Review of Economics and Statistics, 82(3), 498–498.

    Google Scholar 

  • Blancard, S., Boussemart, J. P., & Leleu, H. (2011). Measuring potential gains from specialization under non-convex technologies. Journal of the Operational Research Society, 62(10), 1871–1880.

    Article  Google Scholar 

  • Bojnec, Š., & Latruffe, L. (2013). Farm size, agricultural subsidies and farm performance in Slovenia. Land Use Policy, 32, 207–217.

    Article  Google Scholar 

  • Briec, W., Dervaux, B., & Leleu, H. (2003). Aggregation of directional distance functions and industrial efficiency. Journal of Economics, 79(3), 237–261.

    Article  Google Scholar 

  • Butault, J.-P., Dedryver, C., Gary, C., Guichard, L., Jacquet, F., Meynard, J., Nicot, P., Pitrat, M., Reau, R., Sauphanor, B., Savini, I. & Volay, T. (2010). Quelles voies pour réduire l’usage des pesticides. Synthèse du rapport d’étude. Ecophyto R&D, INRA, France, 90 p.

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Chau, N. H., & De Gorter, H. (2005). Disentangling the consequences of direct payment schemes in agriculture on fixed costs, exit decisions, and output. American Journal of Agricultural Economics, 87(5), 1174–1181.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., & Norris, M. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Reply. The American Economic Review, 87(5), 1040–1044.

    Google Scholar 

  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society A (General), 120(3), 253–290.

    Article  Google Scholar 

  • Hennessy, D. A. (1998). The production effects of agricultural income support policies under uncertainty. American Journal of Agricultural Economics, 80(1), 46–57.

    Article  Google Scholar 

  • INSEE (2015a). Cereal crops price indexes. Available from http://www.bdm.insee.fr/bdm2/affichageSeries?request_locale=en&recherche=criteres&idbank=001570814&idbank=001570817&idbank=001570818&idbank=001570819&idbank=001570822&anneeFin=2008&anneeDebut=2005&codeGroupe=1123. Accessed 22 Sept 2015.

  • INSEE (2015b). Cereal crops price indexes. Available from http://www.bdm.insee.fr/bdm2/affichageSeries?request_locale=en&anneeDebut=2005&codeGroupe=1123&recherche=criteres&idbank=001570813&anneeFin=2008. Accessed 22 Sept 2015.

  • INSEE (2015c). Intermediate consumption price index. Available from http://www.bdm.insee.fr/bdm2/affichageSeries?anneeDebut=2005&anneeFin=2008&recherche=criteres&codeGroupe=1124&idbank=001571010. Accessed 22 Sept 2015.

  • INSEE (2015d). Depreciation price index. Available from http://www.bdm.insee.fr/bdm2/affichageSeries?request_locale=en&recherche=criteres&idbank=001570847&anneeFin=2008&anneeDebut=2000&codeGroupe=1124. Accessed 22 Sept 2015.

  • INSEE (2015e). Industrial crops price indexes. Available from http://www.bdm.insee.fr/bdm2/affichageSeries?anneeDebut=2005&anneeFin=2008&recherche=criteres&codeGroupe=1123&idbank=001570452&idbank=001570474&idbank=001570497&idbank=001570499&idbank=001570823&idbank=001570839&idbank=001570840. Accessed 22 Sept 2015.

  • INSEE (2015f). Industrial crops price indexes. Available from http://www.bdm.insee.fr/bdm2/affichageSeries?anneeDebut=2005&anneeFin=2008&recherche=criteres&codeGroupe=1124&idbank=001571089. Accessed 22 Sept 2015.

  • INSEE (2015g). Other crops price indexes. Available from http://www.bdm.insee.fr/bdm2/affichageSeries?anneeDebut=2005&anneeFin=2008&recherche=criteres&codeGroupe=1123&idbank=001570449&idbank=001570475&idbank=001570563&idbank=001570611. Accessed 22 Sept 2015.

  • Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. American Economic Review, 76(2), 323–329.

    Google Scholar 

  • Karagiannis, G. & Sarris, A. (2002). Direct subsidies and technical efficiency in Greek agriculture. 10th EAAE Congress, Zaragoza (Spain), 28–31 August, 17 p.

  • Kleinhanß, W., Murillo, C., San Juan, C., & Sperlich, S. (2007). Efficiency, subsidies, and environmental adaptation of animal farming under CAP. Agricultural Economics, 36(1), 49–65.

    Article  Google Scholar 

  • Kneip, A., Park, B. U., & Simar, L. (1998). A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econometric Theory, 14(6), 783–793.

    Article  Google Scholar 

  • Kumbhakar, S. C. & Lien, G. (2010). Impact of subsidies on farm productivity and efficiency. In: The Economic Impact of Public Support to Agriculture, 109–124. Springer, New York.

  • Lambarraa, F., Stefanou, S., Serra, T., & Gil, J. M. (2009). The impact of the 1999 CAP reforms on the efficiency of the COP sector in Spain. Agricultural Economics, 40(3), 355–364.

    Article  Google Scholar 

  • Latruffe, L., & Desjeux, Y. (2015). Common agricultural policy support, technical efficiency and productivity change in French agriculture. Review of Agricultural, Food and Environmental Studies, 97(1), 1–14.

    Google Scholar 

  • Latruffe, L. & Sauer, J. F. (2010). Subsidies, production structure and technical change: a cross-country comparison. Agricultural and Applied Economics Association, Denver, Colorado, July, 25–27, 18 p.

  • Latruffe, L., Balcombe, K., Davidova, S., & Zawalinska, K. (2004). Determinants of technical efficiency of crop and livestock farms in Poland. Applied Economics, 36(12), 1255–1263.

    Article  Google Scholar 

  • Li, S. K., & Ng, Y. C. (1995). Measuring the productive efficiency of a group of firms. International Advances in Economic Research, 1(4), 377–390.

    Article  Google Scholar 

  • Llewelyn, R. V., & Williams, J. R. (1996). Nonparametric analysis of technical, pure technical, and scale efficiencies for food crop production in East Java, Indonesia. Agricultural Economics, 15(2), 113–126.

    Article  Google Scholar 

  • Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadística y de Investigación Operativa, 4(2), 209–242.

    Article  Google Scholar 

  • Mary, S. (2013). Assessing the impacts of pillar 1 and 2 subsidies on TFP in French crop farms. Journal of Agricultural Economics, 64(1), 133–144.

    Article  Google Scholar 

  • Minviel, J. J., & Latruffe, L. (2016). Effect of public subsidies on farm technical efficiency: a meta-analysis of empirical results. Applied Economics, 49(2), 213–226.

    Article  Google Scholar 

  • Ouellette, P., Vigeant, S. & Zhang, L. (2014). Inference in Aigner and Chu cost frontier estimation: the impact of infrastructure on bus transportation in France. Wim Meeusem workshop, Cantabria, 29–30 October.

  • Phimister, E. (1995). Farm household production in the presence of restrictions on debt: theory and policy implications. Journal of Agricultural Economics, 46(3), 371–380.

    Article  Google Scholar 

  • Rizov, M., Pokrivcak, J., & Ciaian, P. (2013). CAP subsidies and productivity of the EU farms. Journal of Agricultural Economics, 64(3), 537–557.

    Article  Google Scholar 

  • Shepherd, R. W. (1953). Theory of cost and production functions. Princeton University Press.

  • Shepherd, R. W. (1970). Theory of cost and production functions. Princeton University Press.

  • Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64.

    Article  Google Scholar 

  • Sipiläinen, T. & Kumbhakar, S. C. (2010). Effects of direct payments on farm performance: the case of dairy farms in northern Europe countries. Working Paper no. 43, University of Helsinki, 26 p.

  • Skevas, T., Lansink, A. O., & Stefanou, S. E. (2012). Measuring technical efficiency in the presence of pesticide spillovers and production uncertainty: the case of Dutch arable farms. European Journal of Operational Research, 223(2), 550–559.

    Article  Google Scholar 

  • Sotnikov, S. (1998). Evaluating the effects of price and trade liberalisation on the technical efficiency of agricultural production in a transition economy: the case of Russia. European Review of Agricultural Economics, 25(3), 412–431.

    Article  Google Scholar 

  • Weersink, A., Turvey, C. G., & Godah, A. (1990). Decomposition measures of technical efficiency for Ontario dairy farms. Canadian Journal of Agricultural Economics/Revue Canadienne d'Agroeconomie, 38(3), 439–456.

    Article  Google Scholar 

  • Zhu, X., & Lansink, A. O. (2010). Impact of CAP subsidies on technical efficiency of crop farms in Germany, the Netherlands and Sweden. Journal of Agricultural Economics, 61(3), 545–564.

    Article  Google Scholar 

  • Zhu, X., Demeter, R. M., & Lansink, A. O. (2012). Technical efficiency and productivity differentials of dairy farms in three EU countries: the role of CAP subsidies. Agricultural Economics Review, 13(1), 66–92.

    Google Scholar 

Download references

Acknowledgements

We would like to thank participants at the 30th European Workshop on Efficiency and Productivity Analysis and the 29ièmes journées de microéconomie appliquée for helpful comments. The authors would also thank the editor and two anonymous referees for the very helpful comments on previous versions of the paper. All remaining errors are the sole responsibility of the authors. We gratefully acknowledge that the construction of the database used in this article was funded by Agence Nationale de la Recherche project ANR-08-STRA-12-05 (POPSY).

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Correspondence to Kassoum Ayouba.

Appendix: Deflation of nominal variables

Appendix: Deflation of nominal variables

In this appendix, we present the methodology we used to compute the price indexes used in this paper. The first part is concerned with the output, while the second shows how we have dealt with the input deflators.

Output deflators

To conduct our study, it would have been ideal to have full information on the 24 crops that are surveyed and produced by the farms in Eure-et-Loir. Unfortunately, we only have information on three aggregate outputs, their composition and the UAA of each crop used to construct the three outputs. The information we have on these three aggregate outputs is the value in Euros of cereal crops, industrial crops and other crops. Since we use data for 4 years, we have to deflate the production to obtain comparable quantities over time. The methodology we use to deflate these aggregate outputs makes use of the UAA and crop price indexes to construct farm-specific price indexes.

Cereal crops price index

The variable ‘cereal crops’ in our sample is made of the following components: wheat, durum wheat, spring barley, winter barley, irrigated corn, corn, oat, spring wheat and other cereals. The INSEE publishes the price indexes for the following crops: wheat, durum wheat, spring barley, corn and oat (see INSEE 2015a).Footnote 15 However, the INSEE does not have price index for winter barley, irrigated corn, spring wheat and other cereals. Instead, we use the price index of spring barley, the price index of corn, the price index of wheat and the price index of cereal respectively (see INSEE 2015b) since these cereal varieties are relatively close.

The index we used is a weighted sum of the individual crop deflators given by:

$$ {I}_{cer}=\sum_{l=1}^{nc}{w}_l\times {p}_l $$

where w l  = UAA l /UAAcer, UAA l is the UAA of cereal crop l, UAAcer is the UAA for all cereal crops, and p l is the price index of cereal crop l. As mentioned above, there are nine cereals, so nc = 9. We generate farm-specific price indexes by using farm-specific weights in the calculation of the index. Obviously, this is well defined only for farms producing cereals. This composite index is the deflator of the variable ‘cereal crops’. The price indexes used are reported in Table 8.

Table 8 Price indexes of cereal crops

Industrial crops price index

The variable ‘industrial crops’ includes the following produces: protein pea, beet, potato, rapeseed, sunflower, flax, poppy, lucerne and other industrial crops. The INSEE publishes the price indexes for the following crops: protein pea, beet, potato, rapeseed, sunflower, poppy, flax and lucerne (see INSEE 2015e, f). We do not have a price index for ‘other industrial crops’. However, its contribution to the variable industrial crops is negligible (the share of the utilised agricultural area for this produce is 1.2349% of the total utilised agricultural area of industrial crops and 0.5970% of the whole utilised agricultural area). Consequently, we have decided to ignore it in the computation of the price index of industrial crops. To compute the composite index, we use the formula similar to the one for the cereal crop composite price index. This composite price index is the deflator of the variable ‘industrial crops’. The price indexes used are reported in Table 9.

Table 9 Price indexes of industrial crops

Other crops price index

‘Other crops’ is the last output variable. It is made of fodder, fruits, vegetables (beans, peas and other vegetables) and horticulture. Price indexes for these produce are available from the INSEE (see INSEE 2015g). The computation of the composite price index follows the process presented above for cereal and industrial crops. The composite index obtained is the deflator of the variable other crops. The price indexes used are reported in Table 10:

Table 10 Price indexes of other crops

Inputs deflators

The variable ‘materials’ is deflated using its corresponding price index, obtained from the INSEE (see INSEE 2015c). This price index captures the price of purchasing ‘intermediate consumption’ (e.g. energy, seeds, fertilisers, plant protection products). The variable ‘capital’ is deflated using the price index of fixed capital consumption (harvesting equipment, tractors, farm buildings etc.). This index is also available from the INSEE (see INSEE 2015d). Note that the variable ‘decoupled subsidies’ is also deflated using the price index of intermediate consumption. This index is chosen because these subsidies are generally used to cover farms’ operational costs. These indexes are reported in Table 11:

Table 11 Input price indexes

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Ayouba, K., Boussemart, JP. & Vigeant, S. The impact of single farm payments on technical inefficiency of French crop farms. Rev Agric Food Environ Stud 98, 1–23 (2017). https://doi.org/10.1007/s41130-017-0049-2

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