This paper provides an economic assessment of export credit guarantee commitments by the Austrian export credit agency, using firm-level data on a cross-section of 178 Austrian exporting firms for the year 2008. In a first step, we estimate the relative importance of various determinants of export guarantee usage. Results suggest that the most crucial determinants are: firm size, whether or not the firm is part of a multinational enterprise, exposure to revenue risk, and R&D intensity. In a second step, we investigate the effects of export guarantees on export performance. Identification is achieved by using as instruments the exogenous determinants of export guarantee usage identified in the first step. We find that there are economically and statistically significant effects of export credit guarantee usage on firm-specific export performance ranging from some 80 to 100 percent compared with the control group of non-users.

This paper considers the most important aspects of model uncertainty for spatial regression models, namely the appropriate spatial weight matrix to be employed and the appropriate explanatory vari- ables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models used in the regional growth literature, and develop a simple Bayesian model averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on the work by LeSage and Fischer (2008) by reducing the computational costs through the use of Bayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial Durbin matrix exponential model has theoretical and computational advantages over the spatial autoregressive specification due to the ease of inversion, differentiation and integration of the matrix expo- nential. In particular, the matrix exponential has a simple matrix determinant which vanishes for the case of a spatial weight matrix with a trace of zero (LeSage and Pace 2007). This allows for a larger domain of spatial growth regression models to be analysed with this approach, including models based on different classes of spatial weight matrices. The working of the approach is illustrated for the case of 32 potential determinants and three classes of spatial weight matrices (contiguity-based, k-nearest neighbor and distance-based spatial weight matrices), using a dataset of income per capita growth for 273 European regions. (authors' abstract)

This paper empirically compares the contribution of the two major wealth accumulation factors - earned income and inheritances - to the net wealth position of households in the Eurozone. The elasticities of both wealth sources differ considerably across countries and are overly non-linear. Depending on the position in the wealth distribution, an increase of one percentile in the income distribution corresponds to 0.1-0.6 percentiles in the net wealth distribution. We find substantially stronger effects for inheritances vis-á-vis income. In Greece, Portugal, and Austria, households have to climb around three percentiles in the income distribution to compensate a one percentile increase in the inheritance distribution. The findings clearly suggest that bequests play a stronger role in wealth accumulation than earned income. / Series: INEQ Working Paper Series

Bayesian Model Averaging (BMA) is a common econometric tool to assess the uncertainty regarding model specification and parameter inference and is widely applied in fields where no strong theoretical guidelines are present. Its major advantage over single-equation models is the combination of evidence from a large number of specifications. The three papers included in this thesis all investigate model structures in the BMA model space. The first contribution evaluates how priors can be chosen to enforce model structures in the presence of interactions terms and multicollinearity. This is linked to a discussion in the Journal of Applied Econometrics regarding the question whether being a Sub-Saharan African country makes a difference for growth modelling. The second essay is concerned with clusters of different models in the model space. We apply Latent Class Analysis to the set of sampled models from BMA and identify different subsets (kinds of) models for two well-known growth data sets. The last paper focuses on the application of "jointness", which tries to find bivariate relationships between regressors in BMA. Accordingly this approach attempts to identify substitutes and complements by linking the econometric discussion on this subject to the field of Machine Learning.(author's abstract)

No description available.

Les douleurs neuropathiques se caractérisent par l’apparition de symptômes positifs tels qu’une allodynie et de symptômes négatifs tel qu’une hypoalgésie. Les douleurs neuropathiques ont un retentissement important sur la qualité de vie et il n’existe à ce jour aucune thérapie efficace pour leur prise en charge préventive. Récemment, un système rénine-angiotensine tissulaire a été mis en évidence au sein du système nerveux périphérique sensitif et il a été démontré que sa modulation pharmacologique modifie la perception douloureuse chez l’animal. Dans ce travail, nous nous sommes intéressés à la physiopathologie et à la prévention thérapeutique des neuropathies sensitives par des modulateurs du SRA. Pour cela, deux modèles murins de neuropathie sensitive à l’origine de douleurs neuropathiques ont été utilisés ; un modèle de neuropathie induite par la résinifératoxine (RTX), toxine naturelle spécifique des petites fibres nociceptives et un modèle de neuropathie induite par la vincristine (VCR), un agent anticancéreux particulièrement neurotoxique. Une étude pharmacologique menée sur la neuropathie induite par la RTX nous a permis de mettre en évidence que seul le candésartan prévenait le développement de la neuropathie et que son effet était AT2R-dépendant. L’efficacité du candésartan a été confirmée dans le modèle de neuropathie chimio-induite, développé et caractérisé au cours de ce travail. Ce modèle de neuropathie induite par la VCR a permis de révéler, pour la première fois, le potentiel neuroprotecteur du C21 (agoniste direct du récepteur AT2R, Vicore Pharma) dans un contexte de neuropathie périphérique. L’ensemble de ces résultats confirme l’intérêt de la stimulation du récepteur AT2R dans le traitement des douleurs neuropathiques associées à une chimiothérapie, et plus largement d’origine toxique. / Neuropathic pain was characterized by positive symptoms as allodynia and negative symptoms as hypoalgesia. Neuropathic pain has a major impact on patient’s quality of life and there is, currently, no specific treatment for its preventive management. Recently, a specific renin angiotensin system in sensory peripheral nervous system has been showed and it has been demonstrated that its pharmacological modulation could modify pain perception in animals. In this work, we studied the neuroprotective potential of RAS modulators in two animal models of sensory neuropathy leading to neuropathic pain; a model of neuropathy induced by resiniferatoxin (RTX), a specific natural toxin of nociceptive nerve fibers, and a model of neuropathy induced by vincristine (VCR), a neurotoxic anticancer agent. Pharmacological study on mice with RTX-induced neuropathy allowed to conclude that only candesartan was neuroprotective and that its effect was AT2R-dependent.The effective neuroprotective effect of candesartan was confirmed on the model of VCR-induced neuropathy which was previously developed and characterized. This VCR-induced neuropathy mouse model allowed to demonstrate, for the first time, that C21 (a direct AT2R receptor agonist, Vicore Pharma) was neuroprotective against a peripheral neuropathy. All these results confirm the interest of stimulation of the AT2R receptor in the treatment of neuropathic pain associated with chemotherapy and more generally of toxic origin.

The existing theoretical and empirical literature to investigate the existence of local market power is typically based on spatial competition models in the tradition of Hotelling's (1929) linear city and Salop's (1979) circular city. In models of this kind, strong assumptions are made that lead to a spatial homogeneity (symmetry) of firms in a highly stylized one-dimensional market space. However, some of these assumptions are hardly satisfied in many (retail) markets. The present thesis builds on a recent model by Chen and Riordan (2007), in which the market is characterized by a star-shaped graph with a central intersection. In an extension of Chen and Riordan, I distinguish between firms close to the center and firms in the periphery of a spatial market. This spatial heterogeneity leads to an asymmetric competition between firms. A central firm directly competes with a larger number of firms than remote firms do. The implications of the theoretical model are tested in two empirical applications to the retail gasoline market of Vienna and Austria. Using station level data on diesel prices, I estimate price reaction functions for gasoline stations in two different approaches. In the first approach the Austrian retail gasoline market is divided into numerous highly localized and delimited markets. The second approach analyzes the metropolitan area of Vienna and treats the whole market as one big network of gasoline stations, which are connected through the road network. In both approaches I apply econometric spatial autoregressive (SAR) models. The estimated parameters of the slopes of the reaction functions are used to evaluate the impact of individual gasoline stations on equilibrium market prices depending on their location within the market (network). All results obtained provide evidence for (more) central suppliers serving as a stronger reference in pricing than (rather) remote suppliers. Thus, the assumption of a symmetry in spatial competition which is usually implied by spatial competition models in theoretical and applied research, is rejected. (author's abstract)

We empirically investigate the importance of centrality (holding a central position in a spatial network) for strategic interaction in pricing for the Austrian retail gasoline market. Results from spatial autoregressive models suggest that the gasoline station located most closely to the market center - defined as the 1-median location - exerts the strongest effect on pricing decisions of other stations. We conclude that centrality influences firms' pricing behavior and further find that the importance of centrality increases with market size. (authors' abstract) / Series: Department of Economics Working Paper Series

This paper provides an empirical assessment of whether the world economy has become smaller in terms of economic distance over the last decades. We adopt a cross-sectional spatial econometric approach, relating domestic output volatility to (distance-weighted averages of) other countries' output volatility, using a sample of 135 countries and rolling 10-year time windows over the period 1955 to 2006. Using descriptive measures, test statistics, and spatial econometric estimates, we find that cross-country interdependence was virtually insignificant in the early post-war period but has increased strongly from the mid-1960s to the mid-1980s and remained at a high level since then. Results for the most recent period suggest that common shocks to output volatility have a magnified impact and roughly quadruplicate through international spillover effects, which are transmitted through both trade and financial openness.

We highlight the importance of "centrality" for pricing. Firms characterized by a more central position in a spatial network are more powerful in terms of having a stronger impact on their competitors' prices and on equilibrium prices. These propositions are derived from a simple theoretical model and investigated empirically for the retail gasoline market of Vienna, Austria. We compute a measure of network centrality based on the locations of gasoline stations in the road network. Results from a spatial autoregressive model show that prices of gasoline stations are more strongly correlated with prices of central competitors.