In the past few decades spatial econometric models have become a standard tool in empirical research. Nevertheless applications in binary-choice models remain scarce. This paper makes use of Bayesian Spatial Probit Models to model and estimate spatial interactions in location decisions. For this purpose, we focus on the Austrian retail gasoline market, which is going through a process of remarkable structural changes. A short analysis shows that, during the last decade 10.9% of the stations had left the market and a percentage of 29.6% had either left the market or had changed the brand. This paper aims at investigating this process. A special characteristic of this market is the local competition structure which is characterized by spatial dependencies along local competitors. To capture these spatial dependencies and since the dependent variable is binary in nature (an exit had taken place or not), we apply a Bayesian spatial probit model using MCMC estimation on station level data for the whole Austrian retail gasoline market. Our results suggest, that the decision to leave the market, does not only depend on own characteristics, but also on competitors. In particular, we find the exit decisions to exhibit a negative spatial correlation. Moreover, our model allows to quantify spatial spillover effects of this market. (authors' abstract) / Series: Department of Economics Working Paper Series

This study investigates whether there is a link between the successful implementation of European cohesion policy and the voters' attitudes towards the EU. Using the French presidential elections in 2017 as a case study, we do not solely consider regional funds expenditures but also its induced effects in a region as further potential determinant of pro-European or eurosceptic voting behavior. In order to measure the effectiveness of EU structural funds and Cohesion Fund assignment, firm-level employment effects in French NUTS-2 regions stemming from project allocation during the multi-annual financial framework 2007-2013 are estimated. The obtained average treatment effects are, in a next step, used together with other regional characteristics to capture the citizens' perceived exposure to the EU in an empirical voting model for the French presidential election in 2017. The estimation results reveal a significant negative relationship between the effectiveness of EU funds allocation and the vote share of the eurosceptic candidate Marine Le Pen. / Series: Department of Economics Working Paper Series

This paper focuses on Austrian outbound foreign direct investment (FDI, measured by sales of Austrian affiliates abroad) in Europe over the period 2009-2013, using a spatial Durbin panel data model specification with fixed effects, and a spatial weight matrix based on the first-order contiguity relationship of the countries and normalised by its largest eigenvalue. Third-country effects essentially enter the empirical analysis in two major ways: first, by the endogenous spatial lag on FDI (measured by FDI into markets nearby the host country), and, second, by including an exogenous market potential variable that measures the size of markets nearby the FDI host country in terms of gross domestic product. The question whether the empirical result is compatible with horizontal, vertical, export-platform or complex vertical FDI then depends on the sign and significance levels of both the coefficient of the spatial lag on FDI and the direct impact estimate of the market potential variable. The paper yields robust results that provide significant empirical evidence for horizontal FDI as the main driver of Austrian outbound FDI in Europe. This result is strengthened by the indirect impact estimate of the market potential variable indicating that spatial spillovers do not matter. (authors' abstract) / Series: Working Papers in Regional Science

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.

In this paper we put forward a Bayesian Model Averaging method aimed at performing inference under model uncertainty in the presence of potential spatial autocorrelation. The method uses spatial filtering in order to account for uncertainty in spatial linkages. Our procedure is applied to a dataset of income per capita growth and 50 potential determinants for 255 NUTS-2 European regions. We show that ignoring uncertainty in the type of spatial weight matrix can have an important effect on the estimates of the parameters attached to the model covariates. After integrating out the uncertainty implied by the choice of regressors and spatial links, human capital investments and transitional dynamics related to income convergence appear as the most robust determinants of growth at the regional level in Europe. Our results imply that a quantitatively important part of the income convergence process in Europe is influenced by spatially correlated growth spillovers.

This paper focuses on Austrian outbound foreign direct investment (FDI, measured by sales of Austrian affiliates abroad) in Europe over the period 2009-2013, using a spatial Durbin panel data model specification with fixed effects, and a spatial weight matrix based on the first-order contiguity relationship of the countries and normalised by its largest eigenvalue. Third-country effects essentially enter the empirical analysis in two major ways: first, by the endogenous spatial lag on FDI (measured by FDI into markets nearby the host country), and, second, by including an exogenous market potential variable that measures the size of markets nearby the FDI host country in terms of gross domestic product. The question whether the empirical result is compatible with horizontal, vertical, export-platform or complex vertical FDI then depends on the sign and significance levels of both the coefficient of the spatial lag on FDI and the direct impact estimate of the market potential variable. The paper yields robust results that provide significant empirical evidence for horizontal FDI as the main driver of Austrian outbound FDI in Europe. This result is strengthened by the indirect impact estimate of the mark et potential variable indicating that spatial spillovers do not matter. (authors' abstract)

Club-convergence analysis provides a more realistic and detailed picture about regional income growth than traditional convergence analysis. This paper presents a spatial econometric framework for club-convergence testing that relates the concept of club-convergence to the notion of spatial heterogeneity. The study provides evidence for the club-convergence hypothesis in cross-regional growth dynamics from a pan-European perspective. The conclusions are threefold. First, we reject the standard Barro-style regression model which underlies most empirical work on regional income convergence in favour of a two regime [club] alternative in which different regional economies obey different linear regressions when grouped by means of Getis and Ord's local clustering technique. Second, the results point to a heterogeneous pattern in the pan-European convergence process. Heterogeneity appears in both the convergence rate and the steady-state level. But, third, the study also reveals that spatial error dependence introduces an important bias in our perception of the club-convergence and shows that neglect of this bias would give rise to misleading conclusions.

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.

This note proposes the continuous treatment approach as a valuable alternative to propensity score matching for evaluating economic effects of merger and acquisitions (M&As). This framework allows considering the variation in treatment intensities explicitly, and it does not call for an arbitrary definition of cutoff values in traded ownership shares to construct a binary treatment indicator. We demonstrate the usefulness of this approach using data from European M&As and by relying on the example of post-M&A employment effects. The empirical exercise reveals some heterogeneities over the whole distribution of acquired ownership shares and across different types of M&As and country groups.

This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries. In addition, a correct interpretation of the spatial regression parameter estimates that takes into account the simultaneous feed- back nature of the spatial autoregressive model is provided. Our findings indicate that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates. (authors' abstract)