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

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

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

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 b is negative and statistically significant, the neoclassical hypothesis of convergence is verified: a process only driven by the rate of technological progress

Technological diffusion Neoclassical perspective 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 by Anselin (1988) a growing literature has shown the relevance of the problem and the errors and misspecifications that can occur if the issue is ignored in cross-sectional data analysis involving geographic units. 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

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

1. Barro-style methodology Endogenous spatial lag variable 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 Ro is the spatial autoregressive parameter that indicates the extent of interaction between observations according to a spatial pattern exogenously introduced by means of the standardized weight matrix W.

1. Spatial autocorrelation 2. Spatial heterogeneity 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

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

1. Barro-style methodology c. Spatial autocorrelation 2. SLMs 3. SEMs 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

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

5. 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

5. 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

Time and spatial lag are included 5. 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

5. Panel data models 1. 2. 3. 4. 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. Panel data models 1. 2. 3. 4. 5. 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. Concerning the spatial arrangement W is based on euclidian distances among regions row-normalised and with W that remains constant over time. 4. 5.

5. Panel data models GMM estimator 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 propertis and can be estimated easly

Barro-style methodology 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

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

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

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

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

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

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

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 Space has a role in convergence but it is strongly dependent on the specification of the W matrix 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

Conclusions Convergence clubs Panel data environment? 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 Space has a role in convergence but it is strongly dependent on the specification of the W matrix 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

Conclusions Regions with equilibrium values below the average 1. Introduction Regions with equilibrium values below the average 2. Outline Spatial autocorrelation and heterogeneity 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 Space has a role in convergence but it is strongly dependent on the specification of the W matrix 4. Results 4.1 Cross-sectional models 4.2 Panel models 5. Conclusions