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

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

1. Introduction 2. Outline 1. Barro-style methodology 2. Cross- sectional regressions 3. Panel data regressions Spatial effects 80 EU regions NUTS ( / )

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

1. Introduction 2. Outline Technological diffusion 3. Cross- sectional models Neoclassical perspective Economic geography 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 knowledge Empiric literature

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

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

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

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

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

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 Serial dependence of the dependent variable Intensity of the contemporaneous spatial effect Space-time autoregressive and space-time dependence 5. Spatial panel data models

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 1. Dependence results from the neighborhood locations in the previous time period 5. Spatial panel data models

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 Dependence results from location and its neighborhood in the previous time period 5. Spatial panel data models

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 Time and spatial lag are included 5. Spatial panel data models

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 Spatial panel data models

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 Spatial panel data models

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

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 Results 4. 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 Results 4. Results

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 Results: GWR 4. Results

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 Results: GWR and local parameters 4. Results

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 ( )

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 Results: dynamic spatial panel model ( ) 4. Results

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 ( )

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

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 Conclusions 4. Results 5. Conclusions 4.1 Cross-sectional models 4.2 Panel models Convergence clubs Different explanations by theoretical literature Neoclassical perspective Endogenous growth th. Saving rate out of wage larger than saving rate out of capital Different initial values of human capital and knowledge Panel data environment?

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 Conclusions 4. Results 5. Conclusions 4.1 Cross-sectional models 4.2 Panel models Spatial autocorrelation and heterogeneity Policy interventions Regions with equilibrium values below the average NUTS2 Administrative units Different agricultural and socio economic regions