The paper presents a new model for trade flows in Europe. This is integrated with a logistics model for transport chain choice through Logsum variables. This linkage of accessibility to the trade model makes it possible to evaluate how changes in policies on transport costs. This article discusses how such a complex model can be estimated and considers the choice of mathematical formulation. Trade models can use to forecast future trade patterns conditional on scenarios about economic development. Most existing large-scale trade models use a simple distance variable as the measure of resistance between zones on trade. The trade model presented in this paper is developed as part of the European Transtools3 model. This article focuses on the trade model, a specific sub-model of the freight and logistics model, as depicted in the top-right box. Read this article about the trade model within Transtools3.
The data used
The trade model within Transtools3: The application of the trade model takes place at this rather detailed zoning level. Obtained from international organizations, and harmonized by ETISplus. There are good reasons to believe that zero observations really indicate the absence of trade. The basis of our data is the production-consumption matrix from ETISplus at the NUTS3 level. We estimated models explaining this PC matrix, as well as models explaining the matrix of flows aggregated to the country to country level. Our foremost explanatory variables are GDP, GDP per capita and a measure of resistance to trade. As a data source for the GDP, we use the World Bank database “World development indicators (WDI)”, GDP at current prices in USD. We defined a number of dummy explanatory variables, see a list of variables following Eq. in “The gravity model for trade” section. The downside of this is that we cannot link the model to economic statistics of trade.
The trade model within Transtools3: At this stage, we estimate the impact of GDP effects but the impact of EFTA and EURO dummies on the overall trade pattern. We used the parameters for the random effects model at the country level and then fixed these in a subsequent estimation at the zone level. We found that the GDP elasticities of trade flows in tonnes were rather similar for models estimated on zonal. We started the estimation of the trade models on the country data by estimating a gravity model without fixed or random effects. Replacing GDP by gross value added did not improve the fit of the models. The dummies standing for trade facilities/easements usually have the expected positive influence on trade. In the random effects model, we do not have to exclude destination GDPs, in contrast to the fixed effects model where this is necessary.
The trade model within Transtools3: Conclusions
Accessibility is measured in this model across an entire multi-modal logistical chain, on the basis of a logistics model. Most existing large-scale trade models use a simple distance variable as the measure of resistance between zones. Trade models that include country-specific fixed or random effects are more in line with modern economic theory. As the trade data originates at the country-to-country level it is natural to estimate GDP.