Structural change, productivity growth and Structural Funds in European regions

A key factor for a stable and solid development is the gain in labor productivity. The regions that manage to pull out of poverty and improve the living standards of their inhabitants, in fact, need to be able to diversify away from agriculture and other traditional sectors. In doing this, the speed with which labour and other resources move to modern economic activities is the key factor that differentiates successful regions from unsuccessful ones. In the paper productivity growth is decomposed in two parts: the 'within' and the 'structural' variation. The first denotes the variation of productivity due exclusively to a change inside individual sectors (employment is kept unaltered) while the second term captures the productivity effect of labour reallocations across different sectors. It is essentially the inner product of productivity levels (at the end of the time period) with the change in employment shares across sectors. When changes in employment shares are positively correlated with productivity levels, this term will be positive, and structural change will increase economy-wide productivity growth. The spatial distribution of 'within' and 'structural' variation gives an interpretation of the spatial patterns that characterize European productivity growth and its sustainability in the long run. In the second part of the paper, to assess what are the impacts of the variables that play a role on 'structural' and 'within' variation, a cross-sectional growth regression model is used. At this regard, it is conceivable that the growth of productivity, and of its two components, depends on own region as well as neighbouring region characteristics, on the spatial connectivity structure of the regions, and on the strength of spatial dependence. In order to take account all these aspects in the analysis, a spatial Durbin model is adopted. This spatial econometric model allows also to solve both the problem of spatial dependence in the residuals and of omitted explanatory variables. Furthermore it includes both spatially lagged dependent and independent variables and allows to accounts for spatial spillover effects: a change in a single explanatory variable in a certain region has a 'direct impact' on that region as well an 'indirect impact' on other regions due to the spatial connectivity relationships. Finally regarding the conditioning variables included in the regression model, emphasis is put on the Structural Funds and on an institutional regional quality indicator. Assessing their roles on structural change is essential to develop politics able to use the full potential of the Structural Funds.