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Spatial Approach to Contribution of Public Policies to the Dynamization of the Rural Population
Applied Spatial Analysis and Policy ( IF 2.043 ) Pub Date : 2021-07-30 , DOI: 10.1007/s12061-021-09398-z
Oscar Luis Alonso Cienfuegos 1 , Ana Isabel Otero Sánchez 2
Affiliation  

In this article we will analyze the results, in terms of population, of the Common Agricultural Policy of the European Union, in a small European region, of one million inhabitants, with geographical characteristics typical of mountain agriculture. We will use spatial econometric techniques to verify whether the hypothesis that public spending destined for direct subsidization contributes positively to the territorial dynamics of certain relevant economic variables is fulfilled, specifically we will study in our case the population variable. From a methodological point of view, we will use several complementary approaches that give solidity to the results, always from the focus of spatial econometrics, essential when working with territorial data at a low level of disaggregation. On the one hand, we will carry out an exploratory spatial data analysis, which will allow us to detect possible patterns of spatial dependence, and then move on to a confirmatory analysis that will consider both, autocorrelation (models of lag and spatial error) and spatial heterogeneity (switching regressions). In addition to this cross-sectional data approach, which is based on a method of estimating the particular to the general, we will also use the estimation of spatial models of panel data, to include a temporal approach, with a method of estimating the general to the particular. The best results are obtained with a Spatial Durbin Model.



中文翻译:

公共政策对农村人口活力贡献的空间研究

在本文中,我们将在人口方面分析欧盟共同农业政策在欧洲一个小地区的结果,该地区拥有 100 万居民,具有典型的山区农业地理特征。我们将使用空间计量经济学技术来验证用于直接补贴的公共支出对某些相关经济变量的地域动态有积极贡献的假设是否得到满足,特别是我们将在我们的案例中研究人口变量。从方法论的角度来看,我们将使用几种互补的方法来确保结果的可靠性,始终以空间计量经济学为重点,这在处理低级别分解的领土数据时必不可少。一方面,我们将进行探索性空间数据分析,这将使我们能够检测可能的空间依赖性模式,然后继续进行验证性分析,将同时考虑自相关(滞后和空间误差模型)和空间异质性(切换回归) )。除了这种基于从特殊到一般估计的方法的横截面数据方法外,我们还将使用面板数据的空间模型估计,包括时间方法,用估计一般的方法到特定的。使用 Spatial Durbin 模型可获得最佳结果。自相关(滞后和空间误差模型)和空间异质性(切换回归)。除了这种基于从特殊到一般估计的方法的横截面数据方法外,我们还将使用面板数据的空间模型估计,包括时间方法,用估计一般的方法到特定的。使用 Spatial Durbin 模型可获得最佳结果。自相关(滞后和空间误差模型)和空间异质性(切换回归)。除了这种基于从特殊到一般估计的方法的横截面数据方法外,我们还将使用面板数据的空间模型估计,包括时间方法,用估计一般的方法到特定的。使用 Spatial Durbin 模型可获得最佳结果。

更新日期:2021-07-30
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