4 resultados para Spatial Point Pattern analysis

em Archive of European Integration


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The March 2014 European Council could enter the history books as a turning point, not only in the EU’s relations with Russia but also in its role as a foreign policy actor. Events in Ukraine inevitably dominated the Summit, with EU leaders adopting a balanced approach aimed at achieving three key objectives – de-escalation, containment/deterrence and cooperation – based on political and economic support for Ukraine, increased but limited pressure on Russia, and moves to strengthen ties with other EU neighbours. The Summit also discussed a range of economic and environmental policy issues, with the situation in Ukraine casting a long shadow over the discussion on energy policy, but failed to reach agreement on the EU’s climate goals to 2030, or to put more flesh on the bones of calls for a European “industrial renaissance”. However, two other developments were particularly significant: the creation of the second pillar of the future banking union, establishing a single regime for winding down failing banks; and changes to the savings tax directive, bringing years of negotiation to an end.

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This paper empirically analyses a dataset of more than 7,300 agricultural land sales transactions from 2001 and 2007 to identify the factors influencing agricultural land prices in Bavaria. We use a general spatial model, which combines a spatial lag and a spatial error model, and in addition account for endogeneity introduced by the spatially lagged dependent variable as well as other explanatory variables. Our findings confirm the strong influence of agricultural factors such as land productivity, of variables describing the regional land market structure, and of non-agricultural factors such as urban pressure on agricultural land prices. Moreover, the involvement of public authorities as a seller or buyer increases sales prices in Bavaria. We find a significant capitalisation of government support payments into agricultural land, where a decrease of direct payments by 1% would decrease land prices in 2007 and 2001 by 0.27% and 0.06%, respectively. In addition, we confirm strong spatial relationships in our dataset. Neglecting this leads to biased estimates, especially if aggregated data is used. We find that the price of a specific plot increases by 0.24% when sales prices in surrounding areas increase by 1%.