spatial and non spatial approaches to agricultural convergence in europe

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

The role of spatial effects

1. Introduction

2. Outline1. Barro-style methodology

2. Cross-sectional

regressions

3. Panel data regressions

Spatial effects

80 EU regions

NUTS2 1980-2007(1980-93/1994-2007)

1. Introduction

2. Outline

1. Barro-style methodology

3. Cross-sectional models i0

i,0

i,Ti,T yln

y

yln

T

1g

2,0 N

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

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


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

Assumption of independent residuals

Unobservable variables and steady state

Unobservable variables and steady state

Geographic spill-over effects

Geographic spill-over effects

1. Introduction

2. Outline



i0i,0

i,Ti,T yln

y

yln

T

1g

2,0 N

3.1 Global spatial cross-sectional models

2, 0, , , 0, T i i T i ig y Wg N I

20 0,Tg S y W N I

2. Spatial lag model

Endogenous spatial lag variable

3. Spatial error model

Omitted variables

1. Introduction

2. Outline

Spatial effects


1. Spatial autocorrelation

2. Spatial heterogeneity

Structural instability or group-wise

heteroskedasticity

Possibility of multiple, locally stable steady

state equilibria

Convergence clubs

Convergence clubs

1. Introduction

2. Outline



i0i,0

i,Ti,T yln

y

yln

T

1g

2,0 N


4. GWR models

ij ijiijiiiT yvuvug 0,ln,,

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




5. Panel data models


4. Panel data models c. Spatial

autocorrelationa. Time

dependence

b. Space dependence

2. SLMs 3. SEMs

1. Introduction

2. Outline





1,1,1

ylnwylnwylnylnij ji

t.ii1t,iijt,iij1t,it,i

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





1,1,1

lnlnlnln .1,,1,,

ij ji

tiitiijtiijtiti ywywyy

"recursivespacepure"0 model1.

Dependence results from the neighborhood locations in the

previous time period


1. Introduction

2. Outline





1,1,1

lnlnlnln .1,,1,,

ij ji


"recursivespacepure"0 model

"recursivespacetime"0 model

1.

2.

Dependence results from location and its neighborhood in the previous time period


1. Introduction

2. Outline





1,1,1

lnlnlnln .1,,1,,

ij ji




"eoustansimulspacetime"0 model

1.

2.

3.

Time and spatial lag are included


1. Introduction

2. Outline





1,1,1

lnlnlnln .1,,1,,

ij ji





"spatial"0 data panelon model

1.

2.

3.

4.


1. Introduction

2. Outline





1,1,1

lnlnlnln .1,,1,,

ij ji





"spatial"0 data panelon model

"dymanicsimple"0 model panel

1.

2.

3.

4.

5.


1. Introduction

2. Outline






1,1,1

lnlnlnln .1,,1,,

ij ji


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





Results

4. Results

Barro-style methodology

1. Introduction

2. Outline





Results

4. Results

1994-2007

1. Introduction

2. Outline





Results: GWR

4. Results

1. Introduction

2. Outline





Results: GWR and local parameters

4. Results

1. Introduction

2. Outline





4. Results

GWR and local parameters of convergence (1994-2007)

1. Introduction

2. Outline





Results: dynamic spatial panel model (1980-2007)

4. Results

ij ji

t.ii1t,iijt,iij1t,it,i ylnwylnwylnyln

1. Introduction

2. Outline





4. Results

Results: dynamic spatial panel model (1994-2007)

1. Introduction

2. Outline





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

GWREnogenous

constructed W

Fixed vs. adaptive bandwidth

n. regions into the kernel

Type of spatial weight

1. Introduction

2. Outline





Conclusions

4. Results

5. Conclusions


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





Conclusions

4. Results

5. Conclusions


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

spatial and non spatial approaches to agricultural convergence in europe

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