Neuenfeldt S., Gocht A., Heckelei T., Ciaian P. (2019). Explaining farm structural change in the European agriculture: a novel analytical framework. European review of agricultural economics, 01/12/2019, vol. 46, n. 5, p. 1-56.
https://doi.org/10.1093/erae/jby037
https://doi.org/10.1093/erae/jby037
Titre : | Explaining farm structural change in the European agriculture: a novel analytical framework (2019) |
Auteurs : | S. Neuenfeldt ; A. Gocht ; T. Heckelei ; P. Ciaian |
Type de document : | Article |
Dans : | European review of agricultural economics (vol. 46, n. 5, December 2019) |
Article en page(s) : | p. 1-56 |
Langues : | Anglais |
Langues du résumé : | Anglais |
Catégories : |
Thésaurus IAMM STRUCTURE AGRICOLE ; CHANGEMENT STRUCTUREL ; MODELE DE SIMULATION ; SPECIALISATION ; TAILLE DE L'EXPLOITATION AGRICOLE ; RESEAU D'INFORMATION COMPTABLE AGRICOLE ; PAYS DE L'UNION EUROPEENNECatégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.5 - Gestion des Exploitations |
Résumé : | In this paper, we analyse the drivers of farm structural change in the EU-27, applying a novel analytical framework in the field of agricultural economics known as the multiplicative competitive interaction (MCI) model. MCI offers a more parsimonious specification for estimating models of regional farm group shares compared to the often-applied Markov approach. The MCI framework enables farm group-specific equations, which are used to account for drivers specific to certain farm groups. The MCI framework explains farm group shares at the regional level taken from the Farm Structure Survey (FSS) using socio-economic variables from the Farm Accountancy Data Network (FADN) and other databases for the period 19892013. We consider eight production specialisations and two size classes at the NUTS 2 regional level. The results indicate that the past farm structure explains approximately 36 per cent of the EU farm structure variation across regions and time, followed by natural conditions (16 per cent), agricultural prices (14 per cent), macroeconomic variables (9 per cent), subsidies (7 per cent), population (6 per cent) and agricultural income (6 per cent). Further, we have run a simulation experiment where we derived elasticities of structural change with respect to time-varying variables. The structural change appears to be the most elastic with respect to income and macroeconomic variables. |
Cote : | En ligne |
URL / DOI : | https://doi.org/10.1093/erae/jby037 |