In the context of modeling regional freight the four‐stage model is a popular choice. The first stage of the model, freight generation and attraction, however, suffers from three shortcomings: first ...of all, it does not take spatial dependencies among regions into account, thus potentially yielding biased estimates. Second, there is no clear consensus in the literature as to the choice of explanatory variables. Second, sectoral employment and gross value added are used to explain freight generation, whereas some recent publications emphasize the importance of variables which measure the amount of logistical activity in a region. Third, there is a lack of consensus regarding the functional form of the explanatory variables. Multiple recent studies emphasize nonlinear influences of selected variables. This article addresses these shortcomings by using a spatial variant of the classic freight generation and attraction models combined with a penalized spline framework to model the explanatory variables in a semiparametric fashion. Moreover, a Bayesian estimation approach is used, coupled with a penalized Normal inverse‐Gamma prior structure, to introduce uncertainty regarding the choice and functional form of explanatory variables. The performance of the model is assessed on a real‐world example of freight generation and attraction of 258 European NUTS‐2 level regions, covering 25 European countries.
•A model is developed to deal with non-linearities and spatial dependencies in freight generation.•The capabilities of the model are demonstrated in a series of Monte Carlo studies.•A case study of ...European NUTS-2 level freight generation (aggregate and sectoral) is presented.•Presence of non-linearities and spatial dependence is demonstrated.
This paper proposes for the purposes of freight generation a spatial autoregressive model framework, combined with non-linear semi-parametric techniques. We demonstrate the capabilities of the model in a series of Monte Carlo studies. Moreover, evidence is provided for non-linearities in freight generation, through an applied analysis of European NUTS-2 regions. We provide evidence for significant spatial dependence and for significant non-linearities related to employment rates in manufacturing and infrastructure capabilities in regions. The non-linear impacts are the most significant in the agricultural freight generation sector.
Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of ...deforestation processes is required. Spillover effects and factors that differ across locations and over time play important roles in these processes. They are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we model connectivity between regions and consider heterogeneous effects to gain more accurate quantitative insights into the inherent complexity of deforestation. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006-2017 considering spatial spillovers and varying impacts over time and space. Spillovers between municipalities that emanate from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in recent years. This suggests a moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that the neglect of the spatial dimension and the assumption of homogeneous impacts lead to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design.
In this paper, we propose a Bayesian estimation approach for a spatial autoregressive logit specification. Our approach relies on recent advances in Bayesian computing, making use of Pólya–Gamma ...sampling for Bayesian Markov-chain Monte Carlo algorithms. The proposed specification assumes that the involved log-odds of the model follow a spatial autoregressive process. Pólya–Gamma sampling involves a computationally efficient treatment of the spatial autoregressive logit model, allowing for extensions to the existing baseline specification in an elegant and straightforward way. In a Monte Carlo study we demonstrate that our proposed approach markedly outperforms alternative specifications in terms of parameter precision. The paper moreover illustrates the performance of the proposed spatial autoregressive logit specification using pan-European regional data on foreign direct investments. Our empirical results highlight the importance of accounting for spatial dependence when modelling European regional FDI flows.
This paper studies the joint dynamics of foreign direct investments (FDI) and output growth in European regions by using spatially augmented systems of equations modeling framework that incorporates ...third‐region and spillover effects. The joint framework is used to study the dynamic impacts of regional human capital endowments, which demonstrates the importance of explicitly accounting for an endogenous relationship. The relationship is highlighted in a stylized projection exercise, where the long‐run impacts are pronounced in Eastern Europe and capital cities. Overall, ignoring the relationship of regional economic performance and FDI distorts the implied transmission mechanism, which is of utmost importance for policy makers.
Resumen
Este artículo estudia la dinámica conjunta de la inversión extranjera directa (IED) y el crecimiento de la producción en las regiones europeas utilizando un marco de modelización de sistemas de ecuaciones aumentados espacialmente que incorpora los efectos de tercera región y de spillover. El marco conjunto se utiliza para estudiar los efectos dinámicos de las dotaciones regionales de capital humano, lo que demuestra la importancia de tener en cuenta explícitamente una relación endógena. La relación se pone de relieve en un ejercicio de proyección estilizada, en el que los efectos a largo plazo son pronunciados en Europa del Este y en las capitales. En general, ignorar la relación entre los resultados económicos regionales y la IED distorsiona el mecanismo de transmisión implícito, que es de suma importancia para los formuladores de políticas.
抄録
本稿では、第三地域とスピルオーバー効果を組み込む空間的に拡張された方程式モデリングのフレームワークを用いて、欧州地域における外国直接投資(FDI)と生産増加の共同ダイナミクスを検討する。この共同フレームワークは、地域の人的資本の動的な影響を研究するために使用されるが、これは内生的な関係を明示的に説明することの重要性を明らかにするものである。この関係は様式化された投影演習において明示されており、また長期的な影響は東欧や首都において顕著であることも指摘される。全体として、地域の経済パフォーマンスとFDIの関係を無視することは、暗示的な伝達のメカニズムを歪めることになるが、これは政策立案者にとって極めて重大なことである。
This paper presents an empirical study of spatial origin and destination effects of European regional FDI dyads. Recent regional studies primarily focus on locational determinants, but ignore ...bilateral origin- and intervening factors, as well as associated spatial dependence. This paper fills this gap by using observations on interregional FDI flows within a spatially augmented Poisson interaction model. We explicitly distinguish FDI activities between three different stages of the value chain. Our results provide important insights on drivers of regional FDI activities, both from origin and destination perspectives. We moreover show that spatial dependence plays a key role in both dimensions.
In this paper we use spatial econometric specifications to model daily infection rates of COVID-19 across countries. Using recent advances in Bayesian spatial econometric techniques, we particularly ...focus on the time-dependent importance of alternative spatial linkage structures such as the number of flight connections, relationships in international trade, and common borders. The flexible model setup allows to study the intensity and type of spatial spillover structures over time. Our results show notable spatial spillover mechanisms in the early stages of the virus with international flight linkages as the main transmission channel. In later stages, our model shows a sharp drop in the intensity spatial spillovers due to national travel bans, indicating that travel restrictions led to a reduction of cross-country spillovers.
We analyze the spatial relationships of economic output dynamics in European regions from 2000 to 2019 using dynamic spatial autoregressive growth models. In contrast to previous studies that rely on ...exogenous spatial weight matrices based solely on geographical proximity, we use a novel Bayesian approach to fully estimate the spatial weight matrix. Our results show that economic and sectoral characteristics (e.g. sectoral production structure, education, etc.) significantly influence the degree of regional interdependence. The approach thus allows to study the complex dynamics of regional economic development beyond mere distance.
We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. Datasets in regional economic literature are typically characterized by a limited number ...of time periods
relative to spatial units
. When the spatial weight matrix is subject to estimation severe problems of over-parametrization are likely. To make estimation feasible, our approach focusses on spatial weight matrices which are binary prior to row-standardization. We discuss the use of hierarchical priors which impose sparsity in the spatial weight matrix. Monte Carlo simulations show that these priors perform very well where the number of unknown parameters is large relative to the observations. The virtues of our approach are demonstrated using global data from the early phase of the COVID-19 pandemic.
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