The Impact of the Eastern European Enlargement on the
Prosperity of the Old Member States’ Agricultural Export
Sectors
A comparative assessment of the French, German and Italian
agricultural export developments towards the new eastern
member states and of their effects on the respective revealed
Szervezeti advantage
egység
comparative
positions
Master thesis presentation
at AFEPA summer school 2013
Weinbrenner Timo
Supervisor: Prof. Dr. Fertő Imre
Presentation Outline
1. Motivation of the study and research question
2. Research objectives and indicators
3. Analytical method
Szervezeti egység
4. Descriptive and analytical results
5. Summary and Conclusion
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2013.08.08.
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Research
question
EU-enlargement
consequences
Motivation
(1)
Motivation and research question
Literature shows that the Central and Eastern European Countries (CEECs) benefitted from
their EU-accessions in the form of welfare gains, while the EU-enlargement implied a
moderate welfare decrease for the old member states (OMS).
The thesis singles the OMS’ agricultural sectors out and assesses the impact of the EUenlargement on them specifically.
Consequences for the business environment of the agricultural producers in the OMS:
Szervezeti egység
What overall economic effect did the three changes in the business
environment have on the OMS’ agricultural sectors?
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2013.08.08.
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(2)
Research Objectives and Indicators
Data
of basis for all indicators
No. specification
Objective
Indicator
Data and
data source
Changes
in quantitative Exports in value; OECD iLibrary data base
1
Disaggregated exports in value from OMS towards NMS
trade
dynamics
Representatives for OMS
France, Germany and Italy
(Reporters)
NMS’s significance as
2
markets
NMS export
in eastern
Europe
Share of exports towards the NMS in the OMS’ total exports.
Estonia, Latvia, Lithuania, Poland, Czech Republic, Slovakia,
Slovenia,in Hungary,
Romania,
and Bulgaria
• Exports
value by product
category
(degree of processing)
• Lawrence
Index
(trade pattern analysis tool)
Assessment period
1999 to 2011
3 Change in trade pattern • Krugman Index
(trade pattern analysis tool)
Szervezeti
egység
Analyzed trade items
543 agricultural
products
• Annual
mean deviation
of export data
(all agricultural
entries inanalysis)
SITC-5)
(distribution
divergence
• Normalized
Revealed Comparative Advantage Index.
Product
categories
1 – raw commodities
Impact
on revealed
(NRCA
Index) intermediate goods
(differentiated
by degree
of
2 – processed
4 comparative
advantage
• Annual
mean deviation
of NRCA Index Chen categories
processing
and
input-intensity)
3
–
consumer-ready
food
(RCA) position
(distributional
divergence
4 – horticultural
products analysis
Greatest beneficiary
5
Comparative
analysis
the individual reporter’s results.
Data panel with
7059ofobservations
among the OMS
(13 temporal observations for each of the 543 products)
}
=
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(2)
Index
Explanation of indicators; Lawrence and Krugman Indices
Assessment Objective
Lawrence Structural transformation in the trade pattern of the reporter within a predefined period.
Krugman
Formulas:
Trade pattern similarity of the reporter’s trade towards a predefined trading partner
compared to the reporter’s world export pattern at one point in time.
Lawrence
Krugman
Szervezeti egység
Where
and
r represent the reporter’s share of product j in it’s exports
towards a predefined trading partner i and towards the world at time t respectively
and β is assumed to equal 0.5
Interpretation
Lawrence Index
Range
Behavior
Krugman Index
0 –1
0 –1
Increasing with structural transformation
Increasing with structural dissimilarity
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(2)
Where
Explanation of indicators; NRCA Index
Eij
denotes the export value of country i for commodity j
Ei
denotes total export value (of all ‘j’) of country i
EREFj
denotes the export value of commodity j of the group of reference countries
EREF
denotes the total export value (of all ‘j’) of the group of reference countries
Interpretation:
Szervezeti egység
NRCA value
Interpretation
=0
Country i exports product j at the comparative-neutral level
>0
Country i exhibits a comparative (export) advantage in product j
<0
Country i exhibits a comparative (export) disadvantage in product j
Range
-2500 to 2500 (after multiplication of all index values by 10,000 for a more convenient
presentation)
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(3)
1
Analytical Method to analyze the indicators
Descriptive analysis
Time series regression model to test for structural breaks:
2
Change in mean
value after 2004
Change in trade
growth rate after 2004
Szervezeti egység
Wald test for joint probability: c(3) = 0; c(4) = 0
3
Panel Unit Root Test for Distributional Divergence:
Panel Unit Root Tests are performed on the mean deviations of the
export in value data from the OMS to the NMS.
A confirmed Unit Root would indicate distributional divergence
over the assessment period
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(4) Results
1. Trade Quantities
Total agricultural exports from the OMS towards the CEEC country aggregate:
Evolvement from 1999 to 2011 (bn USD)
12
10
France
8
Germany
Italy
6
4
2
-
1999 2000 2001
2002 2003 egység
2004 2005 2006 2007 2008 2009 2010 2011
Szervezeti
Regression results:
France
c(1)
Regression
results
c(2)
c(3)
c(4)
c(1)
607,539
0.51
10,348
0.31
1,971,922
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
Wald test
c(3)/c(1)
Germany
c(2)
c(3)
0.69 58,550
(0.00)
Significant
1.7%
Italy
(0.00)
c(4)
c(1)
0.29 872,661
(0.00)
(0.00)
c(2)
0.51 10,787
(0.00)
Significant
3.0%
c(3)
(0.00)
c(4)
0.37
(0.00)
Significant
1.2%
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(4) Results
2. NMS as export markets
Proportion of the reporters’ world exports directed towards the CEEC region (export shares):
Evolvement from 1999 to 2011
14%
France
12%
Germany
Italy
10%
8%
6%
4%
2%
0%
Szervezeti egység
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Regression results:
France
c(1)
Regression
results
c(2)
1.3E-05
0.54
(0.00)
(0.00)
Wald test
c(3)/c(1)
Germany
c(3)
9.9E-08
c(4)
0.17
(0.00) (0.00)
c(1)
4.0E-05
c(2)
0.73
c(3)
8.8E-07
(0.00) (0.00)
Significant
0.8%
Italy
c(4)
c(1)
c(2)
0.10
3.7E-05
0.54
(0.00) (0.00)
(0.00)
(0.00)
c(3)
c(4)
3.3E-07
0.18
Significant
2.2%
(0.00) (0.00)
Significant
0.9%
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(4) Results
3. Trade Pattern
Trade pattern = the composition of the export flow of a reporting country
Lawrence
Krugman average annual change
Distributional Divergence
1999- 199920042008 2003
2008
France
0.37
0.31
0.25
Germany
0.42
0.23
0.27
Italy
0.29
0.22
0.22
200020052004
2009
France
-0.005
-0.010
Germany -0.018
-0.008
Italy
-0.006
-0.011
Annual Mean Deviations:
Above-average transformation rate;
No EU-enlargement effect detectable
Continuous adaptation;
Difference apparent, but
inconsistent
Unit Root is accepted for
France, Germany and Italy
at 1% level
Divergence in export
flows confirmed
Chen categories as division key: Szervezeti egység
8,000
Agri-exports towards the NMS, in million USD
6,000
1999
4,000
2004
2,000
-
2011
Initial ranking of Chen categories
systematically reinforced (in
absolute and relative terms):
1. Consumer-ready food
2. Processed intermediate goods
3. Raw commodities
4. Horticultural products
Chen Chen Chen Chen Chen Chen Chen Chen Chen Chen Chen Chen
1
2
3
4
1
2
3
4
1
2
3
4
France
Germany
Italy
But: for Italy Horticultural products
is the second most important
category.
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(4) Results
4. RCA position
Reference country group is the OECD family
40
NRCA evolvements from 1999 to 2011
20
0
-20
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
-40
-60
France
Germany
Szervezeti
egység Italy
Regression results, exemplarily for Germany:
Germany
c(1)
c(2)
c(3)
c(4)
NRCA Index
-0.024892 0.703081
0.000443
-0.03463
for entire Agri(0.0000)
(0.0000)
(0.0001)
(0.0000)
sector
Wald test
c(3)/c(1)
(improving) significant
For France, Germany and Italy the c(3)
and c(4) coefficients are significant and
improving in nature (except FRA c(4)).
But: the NRCA Index developments are
not congruent with the export flow
developments.
Conclusion: EU-enlargement had a
supportive effect on the NRCA Index
-1.8%
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2
Absolute
trade
increase
1
5. Greatest Beneficiary
Rank Country
1 Germany
2 Italy
3 France
c(3)
58,550
10,787
10,348
Relative
trade
increase
(4) Results
Rank Country
1 Germany
2 France
3 Italy
c(3)/c(1)
3.0%
1.7%
1.2%
heterogeneous
Relatively equal
3
Effect on
NRCA
position
Szervezeti egység
In concordance with the level of involvement in the
CEECs’ sales markets:
Rank Country Export share towards NMS 2011
1 Germany
13%
2 Italy
7%
3 France
3%
Decisive is the general involvements of the OMS in the sales markets of the NMS
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(5)
Summary and Conclusion (i)
EU-Enlargement effect on French, German and Italian agricultural sectors
Positive effect proven for:
1. Trade quantities towards NMS
2. Export shares towards
NMS egység
Szervezeti
Trade creation
3. Support of RCA position
Business Boost for OMS
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(5)
Summary and Conclusion (ii)
Other Revealments
•The agricultural sectors of the OMS are internationally competitive.
•OMS supply the NMS at different intensity levels (shares of total export). Germany has
highest involvement, followed by Italy. France marginally active in NMS.
•Export patterns continuously transformed and converged to world export pattern.
•Transformation occurred with structural determination, in favor of highly processed
products (while Italy as well is strong in horticulture).
•Convergence indicates aSzervezeti
process of egység
maturing trade linkages.
•Germany exhibits least Krugman-differences
•NMS are heterogeneous sales markets for the OMS. Main trading partners:
•Absolute quantities: Poland, Czech Republic and Hungary, new comer Romania.
•Quantity per capita: Baltic states, Czech Republic, Slovenia and Slovakia.
•Within the agribusiness, the food processing industry might be a major winner, since the
greatest share of trade increases was achieved with consumer-ready food products.
• NRCA Index improvements rely heavily on a small number of competitive products
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2013.08.08.
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Thank You for Your Attention!!
Szervezeti egység
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2013.08.08.
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