Spatial and non spatial approaches to agricultural convergence in Europe

1. Spatial and non spatial approaches to agricultural convergence in Europe Luciano Gutierrez*, Maria Sassi** *University of Sassari **University of Pavia

2. Political and financial perspective Empirical perspective 1. Introduction - Real convergence: a key objective of the EU - Little attention - Rather small number of studies that deal with theoretical and empirical advancement - Interest to agriculture Accelleration of growth and income CAP and RD for territorial disparities reduction The role of spatial effects

3. 2. Cross-sectional regressions Spatial effects 1. Barro-style methodology 1. Introduction 2. Outline 3. Panel data regressions 80 EU regions NUTS2 1980-2007 (1980-93/1994-2007)

4. 1. Barro-style methodology Per capita income at the initial year 1. Introduction 2. Outline 3. Cross-sectional models Annual average growth rate of per capita income parameter of convergence If bis negative and statistically significant, the neoclassical hypothesis of convergence is verified: a process only driven by the rate of technological progress

5. Technological diffusion 1. Introduction Neoclassical perspective Economic geography 2. Outline 3. Cross-sectional models knowledge entirely disembodied and understood as a pure public good a regional public good with limited spatial range differentials of income and growth rate across regions cannot be explained in terms of different stocks of knowledge regions might show different path of growth even in opposite direction Empiric literature

6. Spatial effects 1. Introduction 2. Outline 1. Spatial autocorrelation 2. Spatial heterogeneity 3. Cross-sectional models Assumption of independent residuals Coincidence of attribute similarity and location similarity Unobservable variables and steady state The value of variables sampled at nearby location are not independent from each others Geographic spill-over effects

7. 1. Barro-style methodology 1. Introduction 2. Outline 3. Cross-sectional models 2. Spatial lag model Endogenous spatial lag variable 3.1 Global spatial cross-sectional models 3. Spatial error model Omitted variables

8. Spatial effects 1. Introduction 2. Outline 1. Spatial autocorrelation 2. Spatial heterogeneity 3. Cross-sectional models Structural instability or group-wise heteroskedasticity Convergence clubs Possibility of multiple, locally stable steady state equilibria

9. 1. Barro-style methodology 1. Introduction 2. Outline 3. Cross-sectional models 4. GWR models 3.1 Global spatial cross-sectional models 3.2 Local spatial cross-sectional models each data point is a regression point that is weighted by the distance from the regression point itself

10. 2. SLMs 3. SEMs 1. Barro-style methodology 1. Introduction 2. Outline 3. Cross-sectional models 5. Panel data models 3.1 Global spatial cross-sectional models 3.2 Local spatial cross-sectional models 4. Panel data models a. Time dependence c. Spatial autocorrelation b. Space dependence

11. 5. Spatial panel data models 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models Space-time autoregressive and space-time dependence Serial dependence of the dependent variable 3.2 Local spatial cross-sectional models 4. Panel data models Intensity of the contemporaneous spatial effect

12. 5. Spatial panel data models 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 1. 3.2 Local spatial cross-sectional models Dependence results from the neighborhood locations in the previous time period 4. Panel data models

13. 5. Spatial panel data models 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 1. 3.2 Local spatial cross-sectional models 2. 4. Panel data models Dependence results from location and its neighborhood in the previous time period

14. 5. Spatial panel data models 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 1. 3.2 Local spatial cross-sectional models 2. 4. Panel data models 3. Time and spatial lag are included

15. 5. Spatial panel data models 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 1. 3.2 Local spatial cross-sectional models 2. 4. Panel data models 3. 4.

16. 5. Spatial panel data models 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 1. 3.2 Local spatial cross-sectional models 2. 4. Panel data models 3. 4. 5.

17. 5. Spatial panel data models 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models All the special cases of the general specification can be estimated with only few modifications to moment restrictions 3.2 Local spatial cross-sectional models GMM estimator 4. Panel data models With spatial lags it shows good properties and can be easly estimated

18. Results Barro-style methodology 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 3.2 Local spatial cross-sectional models 4. Panel data models 4. Results

19. Results 1994-2007 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 3.2 Local spatial cross-sectional models 4. Panel data models 4. Results

20. Results: GWR 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 3.2 Local spatial cross-sectional models 4. Panel data models 4. Results

21. Results: GWR and local parameters 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 3.2 Local spatial cross-sectional models 4. Panel data models 4. Results

22. GWR and local parameters of convergence (1994-2007) 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 3.2 Local spatial cross-sectional models 4. Panel data models 4. Results

23. Results: dynamic spatial panel model (1980-2007) 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 3.2 Local spatial cross-sectional models 4. Panel data models 4. Results

24. Results: dynamic spatial panel model (1994-2007) 1. Introduction 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models 3.2 Local spatial cross-sectional models 4. Panel data models 4. Results

25. Conclusions Little formal guidance available for cross-country and panel data spatial models (Florax & de Graaff) Specification of the weight matrix 1. Introduction 2. Outline 3. Cross-sectional models Global spatial cross-sectional models Spatial panel data models 3.1 Global spatial cross-sectional models Exogenous constructed W matrix 3.2 Local spatial cross-sectional models Binary scheme designed according to the Queesn’s contiguity Euclidian distances – row normalised 4. Panel data models 4. Results Fixed vs. adaptive bandwidth 4.1 Cross-sectional models Enogenous constructed W GWR 4.2 Panel models n. regions into the kernel 5. Conclusions Type of spatial weight

26. Conclusions 1. Introduction Convergence clubs Panel data environment? 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models Different explanations by theoretical literature 3.2 Local spatial cross-sectional models Neoclassical perspective Endogenous growth th. 4. Panel data models 4. Results 4.1 Cross-sectional models Saving rate out of wage larger than saving rate out of capital Different initial values of human capital and knowledge 4.2 Panel models 5. Conclusions

27. Conclusions 1. Introduction Regions with equilibrium values below the average Spatial autocorrelation and heterogeneity 2. Outline 3. Cross-sectional models 3.1 Global spatial cross-sectional models Policy interventions 3.2 Local spatial cross-sectional models NUTS2 Administrative units Different agricultural and socio economic regions 4. Panel data models 4. Results 4.1 Cross-sectional models 4.2 Panel models 5. Conclusions