Competition Policy in Europe – Temporary or Long-Lasting Changes?
During the first phase of the global crisis, the scale and scope of state aid increased at an unprecedented rate all over the world. Although their approval is – uniquely – part of competition policy in the European Union, the Commission and DG Competition did not oppose the unprecedented expansion of state aid. On the contrary, they played an active role in the modification and enforcement of frameworks. The paper analyses how EU regulation, prohibiting state aid for distorting competition, might be compatible with recent developments in this field. It comes to the conclusion that the European Commission proved to be strong enough to keep the regulation of distribution methods under control and to try to enforce the basic principles, but was unable to stem the tide of aid. By doing so, it contributed to the accumulation and growth of state deficits, the repeated destabilisation of financial markets and sovereign debts, and thus to the escalation of the second wave of the financial crisis in Europe in 2011.
The EMU sovereign-debt crisis: Fundamentals, expectations and contagion
We offer a detailed empirical investigation of the European sovereign debt crisis based on the theoretical model by Arghyrou and Tsoukalas (2010). We find evidence of a marked shift in market pricing behaviour from a ‘convergence-trade’ model before August 2007 to one driven by macro-fundamentals and international risk thereafter. The majority of EMU countries have experienced contagion from Greece. There is no evidence of significant speculation effects originating from CDS markets. Finally, the escalation of the Greek debt crisis since November 2009 is confirmed as the result of an unfavourable shift in country specific market expectations. Our findings highlight the necessity of structural, competitiveness-inducing reforms in periphery EMU countries and institutional reforms at the EMU level enhancing intra-EMU economic monitoring and policy co-ordination.
Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification
Traditional image texture measure usually allows a texture description of a single band of the spectrum, characterizing the spatial variability of gray-level values within the singleband image. A problem with the approach while applied to multispectral images is that it only uses the texture information from selected bands. In this paper, we propose a new multivariate texture measure based on the multivariate variogram. The multivariate texture is computed from all bands of a multispectral image, which characterizes the multivariate spatial autocorrelation among those bands. In order to evaluate the performance of the proposed texture measure, the derived multivariate texture image is combined with the spectral data in image classification. The result is compared to classifications using spectral data alone and plus traditional texture images. A machine learning classifier based on Support Vector Machines (SVMs) is used for image classification. The experimental results demonstrate that the inclusion of multivariate texture information in multispectral image classification significantly improves the overall accuracy, with 5 to 13.5 percent of improvement, compared to the classification with spectral information alone. The results also show that when incorporated in image classification as an additional band, the multivariate texture results in high overall accuracy, which is comparable with or higher than the best results from the existing single-band and two-band texture measures, such as the variogram, cross variogram and Gray-Level Co-occurrence Matrix (GLCM) based texture. Overall, the multivariate texture provides the useful spatial information for land-cover classification, which is different from the traditional single band texture. Moreover, it avoids the band selection procedure which is prerequisite to traditional texture computation and would help to achieve high accuracy in the most classification tasks.