25,642 research outputs found

    The taxation of multinationals: Firm level evidence for Belgium.

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    This paper provides empirical evidence of a more favorable tax treatment for foreign multinationals compared to similar domestic firms in a small open economy. Using treatment effects to control for self-selection of foreign firms into low tax firms, we find that foreign multinationals have substantially lower effective tax rates compared to domestic firms. In our estimations we also control for firm size, sector membership and business-cycle effects. A simple theoretical framework is used to explain our empirical findings and rests on the notion that multinational firms are in a better position to bargain for lower taxes with governments as a result of their 'footloose' nature and outside location options.Belgium; Corporate taxation; Domestic; Economy; Effective tax rates; Effects; Firm level data; Firm size; Firms; Framework; Multinational firms; Multinationals; Open; Options; Sector; Self-selection; Size; Tax rates;

    EPR spectrum via entangled states for an Exchange-Coupled Dimer of Single-Molecule Magnets

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    Multi-high-frequency electron paramagnetic resonance(EPR) spectrum for a supermolecular dimer [Mn4]2[ Mn_4]_2 of single-molecule magnets recently reported [S. Hill, R. S. Edwards, N. Aliaga-Alcalde and G. Christou(HEAC), Science 302, 1015 (2003)] is studied in terms of the perturbation method in which the high-order corrections to the level splittings of degenerate states are included. It is shown that the corresponding eigenvectors are composed of entangled states of two molecules. The EPR-peak positions are calculated in terms of the eigenstates at various frequencies. From the best fit of theoretical level splittings with the measured values we obtain the anisotropy constant and exchange coupling which are in agreement with the corresponding values of experimental observation. Our study confirms the prediction of HEAC that the two Mn4Mn_4 units within the dimer are coupled quantum mechanically by the antiferromagnetic exchange interaction and the supermolecular dimer behaviors in analogy with artificially fabricated quantum dots.Comment: 16 pages,2 figures, 2 table

    Modelling spatial weight matrices and lags in spatial panel models

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    Although each variable in a spatial econometric model can have its own spatial weight matrix, practitioners generally adopt one common pre-specified spatial weight matrix for all of them. This thesis breaks this practice for commonly used spatial econometric models with controls for fixed effects in space and time and different panel data settings, using both pre-specified and parameterized spatial weight matrices. The proposed quasi-maximum likelihood estimators of the parameters of these models are proven to be identified, consistent, and asymptotically normal. Three results in this thesis stand out. First, spatial autoregressive errors tend to go together with a sparse matrix and spatial moving average errors with a dense matrix. Second, indirect spillover effects, the focus of many empirical studies, can be severely biased when one common pre-specified spatial weight matrix is used. Third, identification problems that plagued the empirical literature trying to estimate general nesting spatial models are diminished if the spatial weight matrices are parameterized

    A multi-task learning CNN for image steganalysis

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    Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN

    An ecosystem approach to knowledge management: Case studies of two Australian SMEs

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    This study is centred on the premise that knowledge is personalised information which can be enriched through the process of learning, then shared and applied to practical situations to attain value. To highlight the complex nature of knowledge management (KM) as a set of practices and aimed to enhance collaboration, the concept of a Collaborative Leaning Ecosystem (CLES) is presented as holistic approach toward improving practical learning environments. In view of the pressing need for better KM in small-to-medium (SME) enterprises, the CLES framework is used to examine the KM positions of two Australian SMEs. Viewing each case as an 'organisational ecosystem', the holistic assessment of each SME exposes certain KM inefficiencies unique to the firm, which are addressed through a set of actionable KM strategies for improving the relationships among the components interacting within each organisational ecosystem
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