Articles
Specific costs and gross margins of wheat in Europe: innovative estimation practices for the analysis of regional or structural scale changes
Published : 1 September 2023
Abstract
This paper presents innovative practices for the regionalized estimation and structural analysis of specific costs for wheat, based on the European Farm Accountancy Data Network (FADN). This paper presents innovative practices for the regionalized estimation and structural analysis of specific costs for wheat, based on the European Farm Accountancy Data Network
(FADN). When implementing public policies, it is necessary to generate not only the central estimates of the distribution of the parameter of interest but also its lower or upper quantiles to select appropriate regulatory thresholds. By considering the individual heterogeneity and asymmetry of farm specific costs, this paper introduces methodologies for estimating specific costs in conditional quantiles, dual estimations of gross margins and pseudo-barycentric imputations of partial estimates. The projection of additional elements on factorial graphs allows analyzing changes of scale, from national to regional or structural, by carrying out a comparative study of the results. In order to test its relevance, this innovative procedure is applied to the estimation and analysis of specific costs and gross margins for the regions of the main wheat producing countries in the European Union based on the FADN in 2006.
(FADN). When implementing public policies, it is necessary to generate not only the central estimates of the distribution of the parameter of interest but also its lower or upper quantiles to select appropriate regulatory thresholds. By considering the individual heterogeneity and asymmetry of farm specific costs, this paper introduces methodologies for estimating specific costs in conditional quantiles, dual estimations of gross margins and pseudo-barycentric imputations of partial estimates. The projection of additional elements on factorial graphs allows analyzing changes of scale, from national to regional or structural, by carrying out a comparative study of the results. In order to test its relevance, this innovative procedure is applied to the estimation and analysis of specific costs and gross margins for the regions of the main wheat producing countries in the European Union based on the FADN in 2006.
References
- Butault J.P. (1991). Coûts, prix et revenus selon les produits dans les agricultures européennes en 1984, 1985 et 1986: résultats généraux du modèle. Actes et Communications n°5, INRA-INSEE, Paris, pp. 13-31.
- Butault J.P., Hassan C.R., Reignier E. (1988). Les coûts de production des principaux produits agricoles dans la CEE. Luxembourg, Office of Official Publications of the European Communities.
- Commission européenne (2013). Présentation de la réforme de la PAC 2014-2020, Brief : Les perspectives de la politique agricole, n°5, décembre, 11 p.
- Commission européenne (2017). The Future of Food and Farming, COM 713, 29 novembre, 26 p.
- Cameron A.C., Trivedi P.K. (2006). Microeconometrics. Methods and Applications. Cambridge University Press, New-York.
- Cazes, P., Chouakria, A., Diday, E., Schektman Y. (1997) Extensions de l’analyse en composantes principales à des données de type intervalle. Revue de Statistique Appliquée, n°24, pp. 5-24.
- Desbois D. (2015). Estimation des coûts de production agricoles : approches économétriques. Thèse de doctorat ABIES-AgroParisTech, dirigée par J.C. Bureau et Y. Surry, 249 p.
- Desbois D., Butault J.-P., Surry Y. (2013). Estimation des coûts de production en phytosanitaires pour les grandes cultures. Une approche par la régression quantile, Economie Rurale, n° 333. pp. 27-49.
- Desbois D., Butault J.-P., Surry Y. (2017). Distribution des coûts spécifiques de production dans l’agriculture de l’Union européenne : une approche reposant sur la méthode de régression quantile, Economie rurale, n° 361, pp. 3-22.
- Desgraupes B. (2017). Clustering Indices, Vignette R, CRAN.
- D’Haultfoeuille X., Givord P. (2014). La régression quantile en pratique. Economie et statistique, n°471, pp. 85-111.
- Eurostat (2012). Agriculture, fishery and forestry statistics, Main results – 2010-11, 221 p.
- Fuentes M., Chavent M. (2015). Clustering divisif monothétique, Vignette R, 4e Rencontre R.
- He X., Hu F. (2002). Markov Chain Marginal Bootstrap. Journal of the American Statistical Association, vol. 97, pp. 783-795.
- Koenker R., Bassett G. (1978). Regression quantiles. Econometrica, n°46, pp. 33-50.
- Koenker R., Bassett G. (1982). Robust tests for heteroscedasticity based on regression quantiles. Econometrica, vol. 50, n°1, pp. 43-61.
- Koenker, R., Machado, J. A. F. (1999). Goodness of Fit and Related Inference Processes for Quantile Regression. Journal of the American Statistical Association, vol. 94, n°448, pp. 1296-1310.
- Koenker R., Zhao Q. (1994). L-estimation for linear heteroscedastic models. Journal of Nonparametric Statistics, n° 3, pp. 223-235.
- SAS Institute (2008) SAS/STAT 9.2 User’s Guide. The QUANTREG Procedure, Chapter 72, SAS, pp. 5352-5425, Institute Cary NC, USA.
- SODAS (2004) Analysis System of Symbolic Official Data, release 2.5, https://www.info.fundp.ac.be/asso/sodaslink.htm
- Surry Y., Desbois D., Butault J.-P. (2013). Quantile Estimation of Specific Costs of Production. FACEPA, D8.2.
- Trouvé A., Berriet-Solliec M. (2008). 2nd pilier de la Politique Agricole Commune et régionalisation : vers plus de cohésion ? Revue d’Économie Régionale & Urbaine, 2008/1, pp. 87-108.
- Ward, J. H., Jr. (1963), Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, n° 58, pp. 236–244.
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