Heterogeneity and Spatial Dependence of Regional Growth in the EU: A Recursive Partitioning Approach
Martin Wagner and
German Economic Review, 2019, vol. 20, issue 1, 67-82
We use model‐based recursive partitioning to assess heterogeneity of growth and convergence processes based on economic growth regressions for 255 European Union NUTS2 regions from 1995 to 2005. Spatial dependencies are taken into account by augmenting the model‐based regression tree with a spatial lag. The starting point of the analysis is a human‐capital‐augmented Solow‐type growth equation similar in spirit to Mankiw et al. (1992, The Quarterly Journal of Economics, 107, 407–437). Initial GDP and the share of highly educated in the working age population are found to be important for explaining economic growth, whereas the investment share in physical capital is only significant for coastal regions in the PIIGS countries. For all considered spatial weight matrices recursive partitioning leads to a regression tree with four terminal nodes with partitioning according to (i) capital regions, (ii) non‐capital regions in or outside the so‐called PIIGS countries and (iii) inside the respective PIIGS regions furthermore between coastal and non‐coastal regions. The choice of the spatial weight matrix clearly influences the spatial lag parameter while the estimated slope parameters are very robust to it. This indicates that accounting for heterogeneity is an important aspect of modeling regional economic growth and convergence.
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