fdi and economic growth
TRANSCRIPT
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FDI and Economic Growth
DONG NI, CLAIRE, XU
DEC.03 2012
Abstract
The purpose of this paper is to examine the influence of
foreign direct investment on per capita GDP growth. Employing a
data set from 1965 to 2005 for 88 countries, I build a model I
use the ordinary least squares technique to estimate the linear
regression equation that shows how GDP per capita is affected by
saving, population growth, education and FDI. Overall, the
results supports that the countries with a high level of FDI
could increase economic development.
Introduction
A question that has long been discussed and addressed by
economists is what makes countries rich or poor? The answer lies
in how countries differ in terms of: population growth, state of
technology, institutions in place, level of global involvement,
climate, colonization history, slavery, natural resources and
culture. In this paper, I will focus on globalization, which is
crucial for the process of economic development. Romer (1990),
Grossman & Helpman (1991), and Barro & Sala-i-martin (1995)
indicate that a more liberal view regarding trade with other
countries, the greater the ability to boost technological
advances. Taking East Asia as an example, most of the countries
have experienced spectacular GDP growth in the last few decades
since their participation in the international economy (World
Bank, 1993).
International trade provides innovative technological
knowledge to foreign countries, which enhances the rate of growth.
In other words, cross-border movements play an important factor
in economic development. Globalization is known for the feature
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of economic integration. My investigation into the effects of
foreign direct integration (FDI), which is one of the components
of the integration, is the focus on this paper. FDI is a
significant contributor to the development in burgeoning
countries. FDI occurs when companies invest in a host nation to
take advantage of economic opportunities, which can range from
resource and labor abundance to favorable economic policies. FDI
may seem as exploitation exacted by foreign investors, but there
are mutual benefits to be gained. In order for foreign firms to
reap the benefits to produce with cheaper labor, the existing
technology and know-how needs to be implemented in the host
nations.
The hypothesis of this paper is to demonstrate that foreign
direct investment has a positive impact on the economic growth
over the long run. Section 1 of this research paper is a review
of literature discussing the relationship between FDI and the
growth rate of GDP per capita. Section 2 introduces the data
used to test the hypothesis. A long-run growth equation has been
built, which will be tested by ordinary least squares (OLS), and
the results are analyzed in section 3. The last section concludes
the research results and findings.
I. Literature Reviews
My hypothesis is to illustrate that FDI has a discernible
positive influence on economic growth, which is supported by
Alfaro (2003), Naveed & Shabbir (2006), Batten & Vo (2009), and
Cieslik & Tarsalewska (2011). The methodology employed to answer
this encompassing question involves the implementation of
econometric techniques (e.g. fixed effect, control variable and
leader-follower), and the usage of static and dynamic panel data
and cross-country data. The following is a review of the selected
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literature that discusses and investigates the issues of FDI from
different perspectives.
Alfaro (2003) examines the relationship between the FDI and
economic growth. It is determined that the branches of government
responsible for economic growth considered easing the
restrictions on FDI as the best strategy to attract more foreign
investments. Alfaro (2003) investigates whether the FDI inflows
in different economic sectors (i.e. primary, manufacturing and
services) have different effects on economic growth. Alfaro
(2003) uses cross section regressions to test the direct impact
of various types of FDI on GDP growth. The variables he adopts
are control variables (initial income, human capital and human
capital), growth variables (inflation) and independent variables
(openness). Using the data of 47 countries from 1981 to 1999 to
test the hypothesis, Alfaro (2003) finds that more than 60% of
private capital flows are FDI. FDI in the primary sector has a
negative effect on growth; FDI in the manufacturing sector has a
positive effect; FDI in the service sector has an ambiguous
effect. Therefore, Alfaro’s investigation implies that not all
forms of FDI will stimulate economic growth.
The goal of Naveed & Shabbir’s (2006) paper is to study the
effects of trade openness and FDI on GDP growth. It argues that
FDI is important for developing countries in an economic growth
process through technological diffusion of new ideas and
technologies. Naveed & Shabbir (2006) input two variables into
Barro’s equation in their research. They use a control set
variables to analyze the data of 23 developed countries from 1971
to 2000: the dependent variable is the growth rate of GDP, and
the independent variables that are used in fixed effect
regression are log of GDP, openness ratio and FDI ratio. Naveed &
Shabbir (2006) use a statistical hypothesis test, the Granger
causality test, to examine the relationship among the variables.
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The results indicate that only trade openness causes GDP to
change, while reverse causality fails. The empirical study
concludes that openness to trade is positively related to GDP
growth, but the impact of FDI is still insignificant regarding
economic growth.
Batten & Vo (2009) adopt the panel data technique to
investigate how the economic growths of 79 countries are affected
by FDI during the time period from 1980 to 2003. The four
variables examined in the data are: FDI inflows, gross FDI flows,
stock of FDI inflows, and gross stock of FDI. In addition, they
also consider the strength of the relationship between FDI and
GDP when countries have different levels in terms of economic,
educational, and institutional conditions. Batten & Vo (2009)
come to the conclusion that the influence of FDI on economic
growth is a strong positive correlation for the countries with a
lower growth rate of population, inflation and risk, a higher
level of educational achievement and stock development. They
conclude that before making the decision to reform the cross-
border investment policy, countries need to take into account the
purpose of foreign governments and how it will accelerate GDP
growth as well.
Cieslik & Tarsalewska (2011) conduct an investigation of the
link between openness and economic growth in developing countries.
More specifically, they divide the external openness into two
parts: openness to FDI, and openness to international trade to
study how these two affect GDP growth respectively. Cieslik &
Tarsalewska (2011) employ the leader-follower model to test 97
developing countries from 1974 to 2006, and generate results by
using static and dynamic panel data. The empirical results tell
that both FDI and international trade have positive relationships
with economic growth, and are statistically significant at 5% and
10% respectively. Moreover, they point out that the role of
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openness to FDI is more important than openness to trade, because
1% increases in international trade causes 0.02% increases in GDP
per capita, while 1% increases in FDI causes 0.10% increases in
GDP per capita.
II. Data Source
Using the data from a sample of 89 developed and developing
countries the impact of FDI on economic growth was investigated.
The selected countries are chosen based on the availability of
data. The data used to test my hypothesis are adopted from the
Penn World Table 7.1 are GDP per capita, population and
consumption share of output, which are used to calculate the of
growth rate of GDP, growth rate of population and average saving
rate between 1965 and 2005. Additionally, the other two variables
are education in 2005 (the percentage of population over the age
of 25 with a secondary education), which is from the UNESCO
Institute for Statistics (UIS) yearbook, and foreign direct
investment (FDI) net inflows from 1982 to 2005, which is
collected from World Bank.
FDI inflows are the value made by foreigners coming into the
host country, while the outflows are the value flowing outside
the country. FDI net inflows are the difference between inflows
and outflows. The FDI net inflow is expressed as the percentage
of FDI inflows to GDP. In the paper, the weakness of the source
is that the data of FDI net inflows before 1982 is limited, and
only a few countries’ FDI are reported. Therefore, the small
sample size may reduce the precision and affect the accuracy of
the results. In order to mitigate the imprecision, I calculate
the average FDI net inflow by adding up all FDI net inflows from
1982 to 2005 and taking the average of the sums. The positive FDI
net inflows suggest that the value of recouping the investment by
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foreign company is less than the value of capital newly invested
in the host country.
By observing the data, the countries with a very low FDI in
1982 also have a low score of GDP per capita in 1965. Using China
as an example, China has a score of 5.19 of FDI and a score of
4335 of GDP per capita in 2005, but the FDI in 1982 is 0.21 and
the GDP is only 63 in 1965. This indicates that no matter how
poor a county was, it would become prosperous when more foreign
investments are established in that country. FDI brings in more
benefits for poor countries, such as new technology, and
increases productivity of output and labour and booster the
growth of the economy.
Variables Mean Variance
Growth Rate of
Population
0.33
0.03
Average Saving Rate 0.28
0.01
Secondary Education 0.05
0.00
Average FDI Net
Inflows
1.90 3.00
III. The Empirical Results of the Regression Models
My estimating equation is derived from the Solow growth
model, and I estimate the regression by taking natural log of GDP
per capita in 2005 as the dependent variable; the log of average
saving, log of population growth, log of education and log of
average FDI are independent variables.
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The model can be written as follows:
Ln(Yᵢ/Lᵢ)=β₀+β₁ln(sᵢ)+β₂ln(nᵢ+g+d)+β₃ln(eduᵢ)+β₄ln(FDI)+eᵢ
Where, Y/L is output per worker, s represents average saving, n
is the population growth rate, g is the technology growth rate, d
is the deprecation rate, edu is the education, FDI is the foreign
direct investment and e is the error term. It is assumed that all
the countries have same technology and depreciation rate, so (g+d)
is equal to 0.05. The regression equation is estimated by using
ordinary least squares (OLS), which shows the relationship
between the independent variable and dependent variables.
The Solow model predicts that it is important to consider
the savings rate and population growth to estimate economic
growth. It takes these two as independent variables that
determine the steady state level of income per capita. The model
tells us how these variables affect the GDP per capita. The
higher the rate of saving, the richer the country it is. The
higher the rate of population growth, the poorer the country it
is. In the process of growth development, human capital also
plays a significant role. According to John W. Kendrick (1997),
over half of the total gross investment in the United States in
1969 is education and training. Human capital is a prerequisite
for long- term economic growth, because better education can lead
to higher individual income. In the paper, FDI is the main
variable of interest that used for investigating the relationship
with economic growth.
Regression Result Table
Dep. Variable 1 2 3
Intercept 8.558***
(0.384)
10.883***
(0.797)
10.636***
(0.799)
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Log(s) 1.155***
(0.200)
1.171***
(0.190)
1.092***
(0.192 )
Log(n+0.05) -1.485***
(0.175)
-1.323***
(0.173)
-1.303***
(0.171)
Log(edu) 0.670***
(0.204)
0.631***
(0.203)
Log(FDI) 0.155*
(0.086)
R2 Adj.
No. Obs
0.625
(89)
0.663
(89)
0.672
(89)
Notes: Standard Errors in parentheses
*** Indicates significance at the 1% level; **Indicates significance at the 5%
level; *Indicates significance at the 10% level
I estimate the linear regressions three times: the first
regression includes saving and population growth variables; the
education variable is added in the second regression; and the FDI
appears in the last regression. I focus mainly on the third
equation, and the regression results in equation form:
Ln (Yᵢ/Lᵢ) =10.64+1.09ln (sᵢ)-1.30ln (nᵢ+0.05) +0.63ln (eduᵢ)
+0.16ln (FDIᵢ)
The sign of the coefficient of saving and education are
positive and statistically significant at the 1% level. The
negative sign of the population growth rate demonstrates the
higher the population growth, the lower the economic growth per
capita and at the 1% level. The coefficient of FDI is
statistically significant at the 10% level and has a positive
relationship with the GDP per capita, which supports my
expectation that the FDI is positively related to GDP per capita.
It indicates that one percent increase in FDI will also increase
the GDP per capital by 0.16 percent. The value of adjusted R
square is 0.67, meaning that 67 % of the total variation in GDP
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per capital is explained by saving, population growth, education
and FDI. The adjusted R-square in the third regression increases
from 62% to 67% after adding the FDI, which signifies that the
variable helps to improve the accuracy of the results.
The residual graph suggests that the variance of the error
term (the vertical distances between the actual GDP per capital
and the predicted GDP per capital) is constant. This indicates
the homoscedasticity, and the linear regression estimator is
unbiased. The correlation table shows that the correlations
between FDI and other explanatory variables are 0.26, -0.17 and
0.14. All the independent variables in a multiple regression
model are not highly correlated, indicating the absence of
multicollinearity. It emphasizes the accuracy of the results
again.
The estimated results confirm my hypothesis that FDI has a
positive correlation with GDP per capita. The reason why the FDI
promotes economic growth is that the FDI brings the new
innovative technology and knowledge into the countries. The
technology transfer is a major aspect in the process of economic
development, because it lowers the costs of conducting
experiments to develop new machines, skills and systems for host
countries. Such technology innovation could increase productivity
of output. Additionally, foreign investments also provide the
employment opportunities for the local residents, and it also
lowers the unemployment rate. FDI increases the productivity,
which is the driving factor to enhance GDP and leading to a
higher standard of living.
Figure 1
FDI Residual and FDI
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Correlation Table (Multicollinearity Test)
log (s) log
(n+0.05)
log
(education)
log(FDI)
log (s) 1
log (n+0.05) -0.29874 1
log
(education)
0.063602 -0.29099 1
log(FDI) 0.262699 -0.16932 0.142646 1
IV. Conclusion
In this paper, the empirical the link between FDI and
economic growth was examined. The hypothesis is that foreign
direct investment leads to GDP per capita growth. I take a major
insight into this issue by adopting a comprehensive data set of
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
-4 -3 -2 -1 0 1 2 3Re
sid
ual
s
FDI
FDI Residual Plot
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89 countries from 1965 to 2005, and the selection of countries
and time period are based on the availability of the data.
Using the growth model to analyze long-run economic growth
by looking at the four variables (i.e. saving, population growth,
education and FDI), I find that FDI inflow contributes to the
development effort of a country and is significantly causing GDP
per capital to grow. Additionally, the regression equation of my
study also emphasizes the influences of FDI on other independent
variables are noticeable and cannot be ignored. The correlation
between FDI and economic growth is strong and positive for the
countries with a lower growth rate of population, a higher rate
of saving and higher level of educational achievement. It is
found that 1% increases in FDI causes 0.16% increases in GDP per
capita. In the recent academic literature, Batten & Vo (2009)
examine the effect of FDI on GDP growth when countries with
different level economic, educational, and institutional
conditions. Cieslik & Tarsalewska (2011) indicate that 1%
increases in FDI causes 0.10% increases in GDP per capita. Their
final results confirm the exogenous component of FDI does exert a
robust, positive influence on economic growth.
Consequently, my findings highlight the importance for
countries extending productions to another county, because the
considerations of the lower costs and higher efficiency may
generate more profits and make them wealthier. In order to create
national incentive to attract the technology and skills into
their countries, some policies should be implemented by
governments, such as the removal of restrictive trade regulations,
lower tariffs and taxes, free trade agreements, and finial
support for foreign investment enterprise.
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APPENDIX
Cou
ntr
y
GDP
200
5
Pop
Gro
wth
Sav
ing
s
Edu
cat
ion
AVE
RAG
E
FDI
S/1
00
E/1
00
POP
/10
0
log
(s)
log
(n+
0.0
5)
log
(ed
uca
tio
n)
log
(FD
I)
log
(Y/
L)
Alg
eri
a
598
8.7
8
43.
485
089
64
54.
92
4.3
373
0.4
959
455
94
0.5
492
0.0
433
73
0.4
348
508
96
-
0.5
992
926
05
-
0.7
239
138
65
-
3.1
379
181
51
-
0.7
012
890
48
8.6
976
429
98
Arg
ent
ina
967
0.7
1
24.
510
278
88
31.
715
6.0
258
5
1.8
276
517
45
0.3
171
5
0.0
602
585
0.2
451
027
89
-
1.1
483
804
31
-
1.2
204
315
47
-
2.8
091
116
38
0.6
030
319
43
9.1
768
570
09
Aus
tri
a
361
50.
99
5.1
414
660
57
34.
14
5.4
690
5
2.1
428
991
46
0.3
414
0.0
546
905
0.0
514
146
61
-
1.0
747
004
69
-
2.2
885
376
17
-
2.9
060
652
59
0.7
621
596
53
10.
495
459
61
Ban
gla
des
h
108
5.5
3
37.
857
678
71
10.
98
2.2
338
4
0.2
027
661
4
0.1
098
0.0
223
384
0.3
785
767
87
-
2.2
090
947
5
-
0.8
472
853
57
-
3.8
014
481
08
-
1.5
957
019
86
6.9
898
236
26
Bar
bad
os
285
75.
05
7.6
189
169
51
19.
89
6.6
701
6
0.9
194
537
07
0.1
989
0.0
667
016
0.0
761
891
7
-
1.6
149
530
-
2.0
699
731
-
2.7
075
263
-
0.0
839
755
10.
260
289
24
XU 13
93 53 38 82
Ben
in
113
6.4
1
52.
710
630
22
23.
855
4.5
140
7
1.4
844
967
43
0.2
385
5
0.0
451
407
0.5
271
063
02
-
1.4
331
763
47
-
0.5
497
287
97
-
3.0
979
71
0.3
950
758
21
7.0
356
294
5
Bot
swa
na
944
1.3
8
53.
398
315
36
33.
745
7.8
253
5
2.6
540
416
82
0.3
374
5
0.0
782
535
0.5
339
831
54
-
1.0
863
379
28
-
0.5
378
831
43
-
2.5
478
017
22
0.9
760
836
41
9.1
528
574
35
Bra
zil
723
4.0
5
35.
688
193
41
33.
605
5.7
172
7
1.5
970
125
6
0.3
360
5
0.0
571
727
0.3
568
819
34
-
1.0
904
953
21
-
0.8
992
322
24
-
2.8
616
787
67
0.4
681
347
34
8.8
865
543
24
Bur
kin
a
Fas
o
848
.87
44.
143
711
99
16.
995
4.0
102
6
0.2
936
905
52
0.1
699
5
0.0
401
026
0.4
414
371
2
-
1.7
722
510
03
-
0.7
104
212
83
-
3.2
163
141
09
-
1.2
252
286
09
6.7
439
060
53
Cam
ero
on
174
2.6
9
45.
147
819
44
13.
685
3.4
815
2
0.9
980
948
02
0.1
368
5
0.0
348
152
0.4
514
781
94
-
1.9
888
698
43
-
0.6
901
951
53
-
3.3
577
012
06
-
0.0
019
070
16
7.4
631
851
75
Can
ada
366
53.
9
20.
779
123
26
31.
435
4.7
715
1
1.8
215
778
24
0.3
143
5
0.0
477
151
0.2
077
912
33
-
1.1
572
482
64
-
1.3
556
051
98
-
3.0
425
073
69
0.5
997
030
62
10.
509
275
11
XU 14
Cen
tra
l
Afr
ica
n
Rep
ubl
ic
532
.32
42.
825
752
36
19.
99
1.1
883
9
0.5
728
311
84
0.1
999
0.0
118
839
0.4
282
575
24
-
1.6
099
380
37
-
0.7
376
059
39
-
4.4
325
707
36
-
0.5
571
642
23
6.2
772
448
12
Chi
le
110
68.
39
27.
405
832
21
38.
055
4.5
339
4.2
283
781
99
0.3
805
5
0.0
453
39
0.2
740
583
22
-
0.9
661
377
04
-
1.1
268
317
73
-
3.0
935
876
9
1.4
418
185
15
9.3
118
485
77
Chi
na
Ver
sio
n 1
433
5.1
2
25.
855
832
69
54.
85
1.9
067
2
2.7
104
142
62
0.5
485
0.0
190
672
0.2
585
583
27
-
0.6
005
679
99
-
1.1
758
443
87
-
3.9
597
856
98
0.9
971
014
88
8.3
745
045
7
Col
omb
ia
649
0.9
9
34.
733
055
7
21.
425
4.7
966
8
2.1
556
308
34
0.2
142
5
0.0
479
668
0.3
473
305
57
-
1.5
406
117
22
-
0.9
229
867
07
-
3.0
372
461
74
0.7
680
834
11
8.7
781
703
4
Com
oro
s
939
.82
51.
267
943
02
23.
06
7.6
081
3
0.5
232
557
51
0.2
306
0.0
760
813
0.5
126
794
3
-
1.4
670
706
71
-
0.5
750
452
09
-
2.5
759
527
74
-
0.6
476
849
27
6.8
456
883
68
Cos
ta
993
9.3
45.
165
29.
07
6.3
123
2.6
277
0.2
907
0.0
631
0.4
516
-
1.2
-
0.6
-
2.7
0.9
661
9.2
042
XU 15
Ric
a
2 784
89
5 979
83
235 578
49
354
634
71
898
369
67
626
621
54
462
27
538
87
Cot
e
d`I
voi
re
129
8.5
63.
780
785
04
24.
475
4.6
029
9
1.2
238
359
61
0.2
447
5
0.0
460
299
0.6
378
078
5
-
1.4
075
179
98
-
0.3
742
457
67
-
3.0
784
640
94
0.2
019
901
56
7.1
689
650
31
Cyp
rus
182
40.
1
22.
645
925
08
39.
295
7.9
408
4
4.0
786
265
31
0.3
929
5
0.0
794
084
0.2
264
592
51
-
0.9
340
729
02
-
1.2
856
918
44
-
2.5
331
511
23
1.4
057
602
97
9.8
113
777
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XU 17
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Moz 628 38. 6.6 5.0 2.3 0.0 0.0 0.3 - - - 0.8 6.4
XU 21
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53
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XU 22
48 694 4 161
26
5 004 896
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032
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24
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45
145
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51
0.2
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295
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343
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57
-
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260
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31
-
0.7
250
109
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tug
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588
XU 23
84 17 67 297
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XU 24
78 32 23 83
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XU 25
Tri
nid
ad
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0.8
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key
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0.5
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04
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1.7
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524
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07
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1.6
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1
-
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547
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-
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ted
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266
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1
-
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147
9
-
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ted
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tes
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2
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900
59
-
1.2
387
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-
1.4
516
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-
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49
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843
3
10.
656
844
92
XU 26
Uru
gua
y
870
9.7
8
8.3
613
896
35
24.
775
2.8
802
6
0.9
580
492
76
0.2
477
5
0.0
288
026
0.0
836
138
96
-
1.3
953
351
06
-
2.0
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010
09
-
3.5
472
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11
Ven
ezu
ela
892
0.9
44.
509
209
45.
7
3.6
885
9
1.7
834
182
08
0.4
57
0.0
368
859
0.4
450
920
9
-
0.7
830
718
88
-
0.7
030
114
93
-
3.2
999
259
15
0.5
785
318
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17
Zam
bia
106
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4
50.
107
335
53
32.
99
1.3
453
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3.9
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15
0.3
299
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134
536
0.5
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733
55
-
1.1
089
657
01
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0.5
958
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-
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bab
we
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519
858
21
21.
47
2.4
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2
0.6
304
478
67
0.2
147
0.0
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702
0.3
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985
82
-
1.5
385
135
74
-
0.8
092
348
45
-
3.6
940
849
79
-
0.4
613
248
12
5.7
784
569
53
XU 27
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XU 28
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