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
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
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
1. Barro-style methodology
3. Cross-sectional models
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
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
Convergence clubs
1. Introduction
2. Outline
1. Barro-style methodology
3. Cross-sectional models
i0i,0
i,Ti,T yln
y
yln
T
1g
2,0 N
3.1 Global spatial cross-sectional models
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
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
autocorrelationa. 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
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
3. Cross-sectional models
3.1 Global spatial cross-sectional models
3.2 Local spatial cross-sectional models
4. Panel data models
1,1,1
lnlnlnln .1,,1,,
ij ji
tiitiijtiijtiti ywywyy
"recursivespacepure"0 model1.
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
1,1,1
lnlnlnln .1,,1,,
ij ji
tiitiijtiijtiti ywywyy
"recursivespacepure"0 model
"recursivespacetime"0 model
1.
2.
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
1,1,1
lnlnlnln .1,,1,,
ij ji
tiitiijtiijtiti ywywyy
"recursivespacepure"0 model
"recursivespacetime"0 model
"eoustansimulspacetime"0 model
1.
2.
3.
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
1,1,1
lnlnlnln .1,,1,,
ij ji
tiitiijtiijtiti ywywyy
"recursivespacepure"0 model
"recursivespacetime"0 model
"eoustansimulspacetime"0 model
"spatial"0 data panelon model
1.
2.
3.
4.
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
1,1,1
lnlnlnln .1,,1,,
ij ji
tiitiijtiijtiti ywywyy
"recursivespacepure"0 model
"recursivespacetime"0 model
"eoustansimulspacetime"0 model
"spatial"0 data panelon model
"dymanicsimple"0 model panel
1.
2.
3.
4.
5.
5. 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
1,1,1
lnlnlnln .1,,1,,
ij ji
tiitiijtiijtiti ywywyy
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
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
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 (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
Results: dynamic spatial panel model (1980-2007)
4. Results
ij ji
t.ii1t,iijt,iij1t,it,i ylnwylnwylnyln
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
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
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