4 resultados para Spatial points patterns analysis

em Archive of European Integration


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The March 2015 European Council might not enter history books, but the outcome of an informal ‘mini summit’ between seven EU leaders has the potential to prepare the grounds for a breakthrough in the negotiations between Athens and its lenders. In this post-summit analysis, Janis A. Emmanouilidis argues that the search for a compromise promises to be a cumbersome, time-consuming and nerveracking exercise. But a solution now seems possible, proving all those doomsayers who have been predicting a ‘Grexit’ or ‘Graccident’ wrong. On other topics, EU leaders committed their countries to build an Energy Union, although questions remain about whether member states will agree to cede sovereignty on a number of significant points. This analysis looks also at the economic issues dealt with at the Spring Summit, with a focus on the perspectives for the European Semester and the Juncker Investment Plan. It ends with a summary of decisions taken on a number of other topics, including relations with Russia and Ukraine, the upcoming Eastern Partnership summit, developments in Libya and in Tunisia, and the endorsement of the Council’s new Secretary General.

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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%.