Transcript Krugman test

Trade similarity across
the Mediterranean Basin
Luca De Benedictis
and
Università di Macerata
Lucia Tajoli
Politecnico di Milano
Bridging the gap: the role of trade and FDI in the Mediterranean
Naples, 9 June 2006
Luca De
Research
questions
Benedictis:
Trade and
other
General Issues
openness
indicators Is economic integration affecting trade structures making
often
countries more similar or more diversified in terms of
positively production and trade patters?
associated to
growth, but
criticisms on
Which are the implications of a given specialization?
the
Is the trade structure relevant?
robustnesstheory
of
vs. empirics
the evidence,
static vs. dynamic
on the
indicators
used, and on
Which is the role of export composition in determining income
the lack of convergence
a
within a group of countries (catching-up)?
clear
underlying
mechanism
linking the
two variables.
Research questions
Does it make a difference to change the export
pattern?
Does it matter to become more or less similar to a
given country or group of countries?
Does it matter in which way (in terms of forms of
integration and in terms of sectoral composition) a
country is open (and not only how much it is open)?
Relevance of these issues for the Mediterranean
countries
Relatively high GDP growth rates for the MED
countries, but little or no catching-up in terms of GDP
per capita
Many political and institutional problems
hampering growth and integration
Difficulties in running acceptable growth
regressions for these countries
Are trade and export composition related to these
problems ? Can an export-led growth model be
achieved?
Research questions
•
Aim of this work:
-verify if export structures in the process of economic
integration with the EU has become more similar to the
EU export structure
- verify if the change in the export structure is associated
with other forms (non-traditional trade) of economic
integration
- verify if export structures capture characteristics of the
development process
The EU - Med partnership
A group of countries with very strong ties with the EU
Initial agreements very early, in the late 1978
EU is the main trade partner for the MED group, but
not for all
Growth of Med economic integration with the EU
- and growth of their trade in general - somehow
disappointing
Barcellona Agreement as a compensation for the
trade diversion?
EU trade with the CEECs and with
the MEDA group
EU total imports from CEECs and MEDA
140000000
120000000
100000000
80000000
Tot MEDA
Tot CEECS
60000000
40000000
20000000
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
0
Export Share to EU - 1990
Egypt
Algeria
Israel
Jordan
Lebanon
Morocco
Turkey
Tunisia
Palestinean Terr. Syria
Export Share to EU - 2003
Israel
Egypt
Jordan
Lebanon
Algeria
Morocco
Tunisia
Palestinean Terr.
Syria
Turkey
Data and sources for this empirical analysis
Countries: Algeria, Egypt, Israel, Jordan, Lebanon, Morocco,
Palestinian territories, Syria, Tunisia, Turkey
Benchmark: EU15
Trade data: exports toward the EU market in 97 sectors from
Comext, Eurostat database
Time period: 1990-2003
Characteristics of the export composition of the MED
Three groups of countries in this sample:
Mono-export (fuel) countries: Algeria and Syria
Diversified but not changing
Diversified and changing
60
40
20
0
export share
80
Algeria 1990
Gini = 0.983
40
20
0
export share
60
80
Algeria 2003
Gini = 0.931
0
10
20
30
40
export share
50
60
Egypt 1990
Gini = 0.858
20
10
0
export share
30
Egypt 2003
Gini = 0.775
8
6
4
2
0
export share
10
12
Israel 1990
Gini = 0.801
10
5
0
export share
15
Israel 2003
Gini = 0.897
10
5
0
export share
15
20
Jordan 1990
Gini = 0.887
15
10
5
0
export share
20
25
Jordan 2003
Gini = 0.802
6
4
2
0
export share
8
10
Lebanon 1990
Gini = 0.786
8
6
4
2
0
export share
10
12
Lebanon 2003
Gini = 0.855
15
10
5
0
export share
20
25
Morocco 1990
Gini = 0.860
15
10
5
0
export share
20
25
Morocco 2003
Gini = 0.860
20
10
0
export share
30
Tunisia 1990
Gini = 0.898
20
15
10
5
0
export share
25
30
Tunisia 2003
Gini = 0.882
60
40
20
0
export share
80
100
Palestinian Territories 1990
Gini = 0.989
20
15
10
5
0
export share
25
30
Palestinian Territories 2003
Gini = 0.955
40
20
0
export share
60
80
Syria 1990
Gini = 0.980
40
20
0
export share
60
80
Syria 2003
Gini = 0.972
10
5
0
export share
15
Turkey 1990
Gini = 0.814
10
5
0
export share
15
Turkey 2003
Gini = 0.815
0
2
4
6
8
export share
10
12
EU 1990
Gini = 0.675
0
2
4
6
8
export share
10
12
14
EU 2003
Gini = 0.718
Measuring export structure and similarity
Export structure: the vector of shares of each sector on
total exports, x1j, ……xnj.
Self-similarity: taking a base year, we observe how a
country export structure changed in time. The change is
measured by the variation of the correlation or distance
indices.
EU-Similarity: we compare a country’s export structure
with the one of the EU, using different indices.
We compare country’s export structure to the EU
benchmark over time to observe whether differences
narrow or widen.
Why similarity in trade structure should matter? Some
possible channels:
Productivity
Selection (Melitz, 2003)
Knowledge spillovers (Keller, 2002)
Factor composition (Slaughter, 1997; Ventura, 1997)
=> proxy used: high-tech intensity
Investments
FDI
Outward Processing Trade
=> proxy used: FDI + OPT
Adaptation to international demand
The Linder hypothesis (Linder, 1961; Markusen, 1986)
=> proxy used: growth in demand
Stability
International risk sharing (Acemoglu and Zilibotti, 1997)
Optimal currency area arguments
=> proxy used: efficiency of financial system and institutions
Methodological points
Similarity in Trade Structures
Measuring similarity in trade structures through
a synthetic metric based on distance (De
Benedictis-Tajoli, 2004)
Similarity
=> (1 – Distance)
Distance: Bray-Curtis index
j = country
k = benchmark
x = sectoral export share
i = sector
countries
sectors
Export sectoral shares
Strong similarity  1
Weak similarity  0
Measuring similarity in trade structures both with
respect to itself at the beginning of the period
(SELF-SIMILARITY), and with respect to the EU15
(EU-SIMILARITY)
Methodological points
Advantages of such a similarity index with respect to
other alternatives:
- no need of a normal distribution of observations, it
is is appropriate in presence of skewed distributions
(unlike correlation)
- change of weight of sectors is taken into account
(not based on pure ranking) =>it capture changes
due to specific sectors
- this particular index is immune from the doublezero paradox, it has the advantage of not increasing
in the number of sectors considered, n; of being
invariant to proportional sub-classifications of the n
sectors considered; of considering both large and
small differences
1.0
0.8
0.6
0.4
0.2
0.0
MEDA Self-similarity (Bray-Curtis)
Algeria
Egypt
Israel
Jordania
Lebanon
Morocco
Tunisia
Palestinian territories
Syria
Turkey
0.0
0.2
0.4
0.6
MEDA EU-Similarity (Bray-Curtis)
0.8
1.0
A comparison with another group:
The EU- and SELF- similarity Plot for the CEECs
How can economic integration influence the
observed changes?
On the supply side: through FDI and other forms of
delocalization of production, production sharing
agreements between the EU and the MEDA can affect the
share of exports in important sectors
On the demand side: opening of the EU market can
influence the export structure of the MEDA to accomodate
the European demand
Previous result for the CEECs confirm the relevance of these
effects: changes in the export structure of all CEECs is
driven by changes in a few sectors highly involved in
processing trade, and growth in EU demand also plays a
role. But for the CEECs international fragmentation of
production can foster both convergence and divergence of
trade structures
Are these effects at work for the Mediterranean
countries? Has integration gone far enough to
produce them?
Algeria - Opt in 2003
0.15
Autovehicles
0.10
0.05
0.00
sectoral share of opt
Wood
0
20
40
60
sectors
80
100
Egypt - Opt in 2003
0.04
0.01
0.02
0.03
Apparel
0.00
sectoral share of opt
0.05
0.06
Autovehicles
0
20
40
60
sectors
80
100
Israel - Opt in 2003
0.15
0.10
0.05
Aircraft
0.00
sectoral share of opt
0.20
Clocks
0
20
40
60
sectors
80
100
Jordan - Opt in 2003
0.10
0.05
0.00
sectoral share of opt
0.15
Cutlery and tools
0
20
40
60
sectors
80
100
Lebanon - Opt in 2003
0.20
0.15
0.10
0.05
0.00
sectoral share of opt
0.25
Photog. Products
0
20
40
60
sectors
80
100
1.0
Morocco - Opt in 2003
0.6
Hats
Cereal preparations
0.2
0.4
Other animal prods.
0.0
sectoral share of opt
0.8
Clocks
0
20
40
60
sectors
80
100
0.8
Tunisia - Opt in 2003
0.4
Sugar
0.2
Cement
Jewellery
0.0
sectoral share of opt
0.6
Photog. Products
0
20
40
60
sectors
80
100
0.6
Syria - Opt in 2003
0.4
0.3
0.1
0.2
Art pieces
0.0
sectoral share of opt
0.5
Jewellery
0
20
40
60
sectors
80
100
Turkey - Opt in 2003
0.20
0.15
0.10
0.05
Pharmaceut.
Jewellery
0.00
sectoral share of opt
0.25
0.30
Art pieces
0
20
40
60
sectors
80
100
Exports toward the EU market: total and OPT trade
Correlation for Tunisia: 0.95
Tunisia composition of OPT and total exports
60
50
OPT
Total exports
30
20
10
CN sectors
97
93
89
85
81
77
73
69
65
61
57
53
49
45
41
37
33
29
25
21
17
13
9
5
0
1
%
40
Exports toward the EU market: total and OPT trade
Correlation for Israel: 0.39
Israel: composition of total and OPT trade
40
35
30
OPT
Total export
20
15
10
5
CN sectors
97
93
89
85
81
77
73
69
65
61
57
53
49
45
41
37
33
29
25
21
17
13
9
5
0
1
%
25
Exports toward the EU market: total and OPT trade
Correlation for Turkey: 0.40
Turkey composition of OPT and total trade
35
30
25
20
%
OPT
Total exports
15
10
5
CN sectors
97
93
89
85
81
77
73
69
65
61
57
53
49
45
41
37
33
29
25
21
17
13
9
5
1
0
Some regression results
Export structure correlated to export volumes
Changes in export composition correlated with
increase in EU similarity
Changes in export composition correlated with
inward FDI
Changes in export composition correlated with OPT
Similarity in export composition and trade
Dependent Variable: TOTEXPEU?
Method: Pooled Least Squares
Date: 05/22/06 Time: 15:42
Sample: 1990 2003
Included observations: 14
Number of cross-sections used: 8
Total panel (balanced) observations: 112
Cross sections without valid observations dropped
Variable
CoefficientStd. Error t-Statistic Prob.
EUSIM?
SELFSIM?
Fixed Effects
ALG--C
EGY--C
ISR--C
LEB--C
MOR--C
SYR--C
JOR--C
TUR--C
32261372
-6626375
5333587 6.048719
1890934 -3.50429
0
0.0007
13089913
-401063
-6014709
-7187733
-2184132
5554367
-7857860
4284490
R-squared
0.795012
Adjusted R-squared
0.776925
S.E. of regression
2350038
Log likelihood -1796.72
Mean dependent var 4711510
S.D. dependent var 4.98E+06
Sum squared resid 5.63E+14
F-statistic
395.5902
Similarity in export composition and trade
Dependent Variable: TOTEXPEU?
Method: Pooled Least Squares
Date: 06/08/06 Time: 19:38
Sample: 1990 2003
Included observations: 14
Number of cross-sections used: 9
Total panel (unbalanced) observations: 115
Variable
CoefficientStd. Error t-Statistic Prob.
GDP?
EUSIM?
Fixed Effects
EGY--C
ISR--C
LEB--C
MOR--C
TUN--C
TUR--C
ALG--C
SYR--C
JOR--C
107.9112 11.94513 9.033907
13369352 5118070 2.612186
0
0.0103
-8310466
-9957794
-6248052
-2268398
-1709597
-1E+07
3113890
-471313
-5363180
R-squared
0.867569
Adjusted R-squared
0.854836
S.E. of regression 1813994
Log likelihood
-1814.67
Mean dependent var5013036
S.D. dependent var 4.76E+06
Sum squared resid 3.42E+14
F-statistic
681.3174