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Explaining farm structural change in the European agriculture: a novel analytical framework
European Review of Agricultural Economics ( IF 3.3 ) Pub Date : 2018-11-15 , DOI: 10.1093/erae/jby037
Sebastian Neuenfeldt 1 , Alexander Gocht 1 , Thomas Heckelei 2 , Pavel Ciaian 3
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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 1989–2013. 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.

中文翻译:

解释欧洲农业中的农场结构变化:新颖的分析框架

在本文中,我们分析了欧盟27国农场结构变化的驱动因素,并在农业经济学领域应用了一种称为乘性竞争相互作用(MCI)模型的新颖分析框架。与通常采用的马尔可夫方法相比,MCI提供了一个更为简化的规范来估算区域农场集团的份额模型。MCI框架启用特定于服务器场组的方程式,该方程式用于说明特定于某些服务器场组的驱动程序。MCI框架使用1989-2013年期间的农业会计数据网络(FADN)和其他数据库中的社会经济变量,解释了从农场结构调查(FSS)中获得的区域性农场集团份额。我们在NUTS 2区域级别上考虑了八个生产专业化和两个规模级别。结果表明,过去的农场结构解释了欧盟不同地区和不同时间的农场结构变化的36%,其次是自然条件(16%),农产品价格(14%),宏观经济变量(9%),补贴(7%),人口(6%)和农业收入(6%)。此外,我们进行了模拟实验,得出了时变变量的结构变化弹性。在收入和宏观经济变量方面,结构变化似乎最具弹性。人口(6%)和农业收入(6%)。此外,我们进行了仿真实验,得出了时变变量的结构变化弹性。在收入和宏观经济变量方面,结构变化似乎最具弹性。人口(6%)和农业收入(6%)。此外,我们进行了仿真实验,得出了时变变量的结构变化弹性。在收入和宏观经济变量方面,结构变化似乎最具弹性。
更新日期:2020-01-04
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