20 2016, XIX, 3
Ekonomie
20 DOI: 10.15240/ ul/001/2016-3-002
In oduc ion
The g a i y model (o equa ion) o in e na ional
ade is an economic analogy o New on’s
Law o g a i y. The economic e sion o his
law assumes ha in e na ional bila e al ade
is di ec ly p opo ional o ‘sizes’ o ading
economies, and indi ec ly p opo ional o
hei dis ance. G a i y was in oduced in o
economic heo y by Tinbe gen (1962), and
la e his pionee wo k was ollowed by many
o he s udies, see e.g. Ande son (1979; 2010),
Be gs and (1985), Dea do (1998), Ande son
and an Wincoop (2003), Helpman e al. (2008),
o Be gs and and Egge (2011). A concise
e iew o g a i y app oach can be ound e.g. in
Ande son (2010) o Sal a ici (2014).
Theo e ical explana ion o he g a i y
equa ion o agg ega ed o disagg ega ed ade
can be ound e.g. in Ande son (1979) o Chaney
(2011). T ade in a eal wo ld is in l uenced no
only by economic size and dis ance, bu also
by ade ‘ ic ions’ ( ade cos s o ade ba ie s)
such as bo de s among coun ies, a di e en
language and cu ency, colonial ies, ee ade
ag eemen s, e c., hese addi ional ac o s we e
inco po a ed in o g a i y models as well, see
e.g. Dea do (1998), Baie and Be gs and
(2009), Ande son (2010), Be gs and and
Egge (2011) o Sal a ici (2014).
G a i y models heo e ically explain he ole
o an economic size in bila e al ade l ows a any
scale (coun ies, egions, e c.); hough he ole o
a dis ance is no well unde s ood ye , see Disdie
and Head (2008). Mo eo e , g a i y applies
o o he socio-economic phenomena such as
mig a ion o di ec o eign in es men s. Gene ally,
acco ding o Ande son (1979) o Chaney (2011),
g a i y models o ade can be conside ed he
mos success ul empi ical ools in economics.
The empi ical e idence o g a i y models
is a he s ong, as a ious s udies epo he
coe i cien o de e mina ion be ween 0.6 and
0.8. A me a-analysis o 1,467 es ima es in 103
pape s p o ided by Disdie and Head (2008)
ound 1

in ela ion (1) (see below).
Rema kably, he coe i cien γ has been s able
(and close o one) o mo e han one cen u y.
A heo e ical explana ion o his esul can be
ound e.g. in Chaney (2011).
G a i y model es ima ions a e usually
ca ied ou o c oss-sec ional o panel da a.
Howe e , coun ies all o e he wo ld o m
a e y he e ogeneous sample. The e a e
coun ies wi h cen u ies o indus ial adi ion
and expo ( he USA, he UK, Ge many, e c.),
coun ies ha expo agg essi ely in he las
decades (Japan, Ko ea, China, e c.), and
also a la ge numbe o de eloping coun ies
whose expo s a e limi ed o a icles such as
bananas o cocoa beans. Mo eo e , a ade
is a p oduc o pa icula human ac ion, and
people li ing unde di e en condi ions and
egimes simply canno ac in a simila way.
The e o e, one should no expec in e na ional
ade o be uni e sally desc ibed o explained
by one equa ion, model o a o mula. To mix
such di e en ade pa ne s while looking o
a gene al pa e n (as in physics) makes li le
sense in economics. Hence, a mo e sensible
app oach migh be he use o mo e homogenous
se s o coun ies, such as de eloped (OECD)
coun ies, La in-Ame ican coun ies, e c.
This a o emen ioned app oach is ollowed
in his pape , whe e he g a i y equa ion is
used o model agg ega e expo ’s sha es o
one coun y (Ge many) o i s ading pa ne s
(impo ing coun ies).
As he g a i y model is usually o mula ed
in a mul iplica i e o m, i is log- ans o med in o
a linea equa ion and coe i cien s o a model a e
es ima ed by an app op ia e eg ession me hod.
A p oblem o a co ec es ima ion o a g a i y
model is a b oadly discussed issue, see e.g.
Heckman (1979), Sil a and Ten ey o (2006),
ON THE GRAVITY EQUATION OF TRADE:
A CASE OF GERMANY
Jiří Mazu ek
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21
Ma ínez-Za zoso e al. (2007), Helpman e al.
(2008), Baie and Be gs and (2009), Egge
(2010), He e a and Baleix (2010) o He e a
(2013). Howe e , he e is no a consensus on
wha he mos app op ia e eg ession es ima ion
is in he case when assump ions o O dina y
Leas Squa es (OLS) me hod a e iola ed, in
pa icula , when a signi i can he e oscedas ici y
is p esen . Unde such ci cums ances he
Pseudo Poisson Maximum Likelihood (PPML)
me hod, he Nonlinea Leas Squa es (NLS)
me hod o he Feasible Gene alized Leas
Squa es (FGLS) me hods we e p oposed by
Sil a and Ten ey o (2006) o Ma ínez-Za zoso
e al. (2007). Ne e heless, i assump ions o
OLS a e sa is i ed, i is he BLUE (Bes Linea
Unbiased Es ima o ) me hod.
The aim o he a icle is o examine how he
agg ega ed g a i y models i he la es expo
da a o Ge many bo h wi hou and wi h ade
ic ions. Ge many was selec ed o his s udy
because i is he 3 d la ges wo ld expo e (behind
he USA and China), and i anks among he mos
de eloped coun ies o he wo ld. I is sui ably
loca ed in he middle o he con inen su ounded
by many ading pa ne s in di e en dis ances;
and las , bu no leas , he da a o Ge many can
be conside ed accu a e and eliable.
The pape is o ganized as ollows: in sec ion
1 Ge many’s expo is b ie l y discussed, in
sec ion 2 he da a is desc ibed, in sec ions 3
and 4 agg ega e g a i y models wi hou and wi h
ic ions a e p esen ed along wi h hei esul s.
Conclusions ollow a he end o he a icle.
1. Ge many’s Expo
Ge many belongs among coun ies wi h
a posi i e balance o in e na ional ade in he
long e m. In 2014, he balance (expo minus
impo ) eached 217 billion Eu os acco ding o
S a is isches Bundesam (2015b). Commodi y
s uc u e o expo is a he s able in he las
decade. The mos impo an expo i ems a e
anspo a ion (ca s, ca pa s, engines and
ae ial echnology), ins umen s o elec ical
enginee ing, elecommunica ions echnology,
o i ce echnology and machine y; see
S a is isches Bundesam (2015a). Figu e 1
p o ides mo e de ailed commodi y s uc u e o
expo in 2014.
The mos impo an ade pa ne s o
Ge many a e F ance, USA, UK, Ne he lands
and China (see also Appendix A). In 2014,
58% o Ge many’s expo wen o EU, 17%
o Asia, 12% o Ame icas, 10% o non-EU
Eu opean coun ies, 2% o A ica and he es
(0.3%) o Aus alia and Oceania acco ding o
S a is isches Bundesam (2015b).
Ge many is a ede al pa liamen a y epublic
consis ing o 16 cons i uen s a es. In 2014,
he s onges expo ing s a es we e Baden-
Wü embe g (16% o o al Ge many’s expo ),
No h Rhine-Wes phalia (15.9%), Ba a ia
(14.9%) and Lowe Saxony (6.9%). A ound 85%
o expo was a comple ed p oduc ion, 5% semi-
i nished p oduc ion, and a ound 1% accoun ed
o aw ma e ial. Mo e de ailed da a including
a s uc u e o expo by indi idual s a es can be
ound in S a is isches Bundesam (2015b).
Fig. 1: The commodi y s uc u e o Ge many expo in 2014 (in billion Eu os)
Sou ce: S a is isches Bundesam (2015a)
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22 2016, XIX, 3
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2. Me hod and Da a
Ge many’s expo is examined ia agg ega e
g a i y equa ions wi h and wi hou ic ions.
These equa ions (models) a e in oduced and
desc ibed in de ail in he ollowing wo sec ions.
As g a i y equa ions ha e a mul iplica i e
o m, hey we e linea ized by he loga i hmic
ans o ma ion. Then, linea eg ession was
pe o med by he mos sui able es ima ion
me hod.
Fo he empi ical in es iga ion he ollowing
da a was used:
 Impo ing pa ne s’ sha es (in %) o
Ge many’s expo o he yea 2013 we e
ob ained om S a is isches Bundesam
(2014). The da a is p o ided in he o m
o a anking o ading pa ne s in he
descending o de . Fo his s udy he lis o
all coun ies was unca ed and includes 66
main pa ne s ( om F ance o Qa a ) wi h
an indi idual sha e o impo om Ge many
a leas equal o 0.10%, as he ela i e
e o o alues lowe han 0.10% would be
inapp op ia ely high (see a discussion a
he end o sec ion 4). These 66 coun ies
comp ise 97.6% o Ge many’s expo .
 Dis ances be ween Ge many and impo ing
coun ies (in kilome e s) we e ob ained
om a dis ance calcula o a Timeandda e
(2013). The dis ance be ween wo coun ies
was de i ned as an ai dis ance be ween
hei capi al ci ies.
 GDP (PPP) in billions USD o impo ing
coun ies we e e ie ed om he
In e na ional Mone a y Fund (2013).
 O he coun ies’ da a include hei cu ency,
a membe ship o Schengen a ea and an
exis ence o join bo de s wi h Ge many.
The dependen a iable in he models
was impo ing pa ne s’ sha es (in %), o he
a iables we e conside ed independen . All da a
is p o ided in Appendix A. I should be no ed
ha expo sha es and GDP (PPP in billion
dolla s) could be a subjec o la e e isions.
3. F ic ionless G a i y Model,
Resul s and Discussion
The s anda d g a i y model (equa ion) o
agg ega e in e na ional ade usually akes he
ollowing o m (Chaney, 2011):
ij
ij
ij
GDP GDP
Tk d



, (1)
whe e Tij is a ade om a coun y i o a coun y
j, GDPi deno es g oss domes ic p oduc , k is
a posi i e coe i cien , and dij is a geog aphic
dis ance o bo h coun ies.
Ande son (2010) assumes ha supply Yi
o a coun y i is a ac ed by a demand Ej o
a coun y j, whe e dij deno es a dis ance o
bo h coun ies, and p oposes he ollowing
ic ionless and agg ega e g a i y model
o ade:
2
iJ
ij
ij
YE
Td

(2)
Also, in some al e na i e g a i y models an
income pe capi a (along wi h a popula ion)
o coun ies is used ins ead o supply and
demand, and an e o e m is added on he igh
hand side o equa ions; see Ande son (1979):
ij i j i j ij ij
MYYNNdU
  

 (3)
In (3) ij
M is he dolla l ow o a gi en
good om a coun y i o a coun y j, i
Y and
j
Y
a e incomes in bo h coun ies, i
N and j
N
a e hei popula ions, and ij
U is a log-no mally
dis ibu ed e o e m wi h (ln ) 0
ij
EU.
In he las decades, mo e sophis ica ed
models o disagg ega ed goods and wi h ade
ic ions we e de eloped, see e.g. Ande son
(2010) o Sal a ici (2014).
In his pape he ollowing agg ega e
ic ionless g a i y model o Ge many’s expo
sha es is conside ed:


i
i
iDIST
GDP
kE 
(4)
In (4) i
E deno es a sha e o Ge many’s
expo (in %) o a coun y i,
i
GDP is a g oss
domes ic p oduc o an impo ing coun y i,
i
DIST is a dis ance be ween coun y i and
Ge many, and α, β, and k a e coe i cien s.
Rela ion (4) simply s a es ha an expo
ises when an impo ing coun y is close and/
o iche . I should be no ed ha i absolu e
alues o expo s (e.g. in billions o USD) we e
conside ed in (4) ins ead o ela i e expo s,
hen only he coe i cien k would change.
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23
Fo he eg ession analysis he ela ion (4)
is e o mula ed in he ollowing way:
jjj
E k GDP DIST

 
(5)
G a i y equa ion is ans o med
loga i hmically (all a iables a e posi i e) which
yields:
ln ln ln ln
jjj
E k GDP DIST

 
(6)
The co ela ion ma ix o a iables in he
model (6) is shown in Table 1. As i can be seen,
co ela ion coe i cien s we e no pa icula ly
high. Mul icollinea i y in he model (6) was
examined ia he Va iance In l a ion Fac o
(VIF), 2
1/(1 )
ii
VIF R
, whe e 2
i
R is he
p opo ion o a iance in he i- h independen
a iable associa ed wi h o he independen
a iables in a model, see O’B ian (2007).
A ule o humb s a es ha o alues o VIF
la ge han 10 mul icollinea i y o a model can
be conside ed a se ious p oblem. In he model
(6) VIF o bo h explana o y a iables was only
a ound 1.3.
Fo he eg ession model (6) he da a
om Appendix A was used. The eg ession
was pe o med ia s a is ical so wa e G e l.
Residuals we e examined o exogenei y,
no mali y and he e oscedas ici y. All assump ions
o OLS we e sa is i ed wi h an excep ion
o he e oscedas ici y associa ed wi h he
loga i hm o GDP, whe e he null hypo hesis
(homoscedas ici y) could be ejec ed by Whi e’s
es a p = 0.04 le el. The e o e, G e l’s buil -
in OLS wi h he co ec ed he e oscedas ici y
me hod (which inco po a es weigh ed leas
squa es me hod) was used o he es ima ion.
Resul s a e epo ed in Table 2.
As i can be seen om Table 2, bo h
eg esso s (logs o GDP and dis ance) we e
ound signi i can a 0.01 le el. As expec ed,
eg ession coe i cien o loga i hm o dis ance
is nega i e, and he coe i cien o loga i hm o
GDP is posi i e, bo h coe i cien s a e close o 1,
which is in acco d wi h o he s udies’ i ndings.
The adjus ed coe i cien o de e mina ion
R2 = 0.742, which is wi hin a ange o 0.6-0.8
was ound in o he simila s udies as well.
Ln(expo ) Ln(Dis ) Ln(GDP)
Ln(expo ) 1 -0.291 0.525
Ln(Dis ) 1 0.506
Ln(GDP) 1
Sou ce: own
Reg esso /me hod OLS wi h c. h.
Cons . 1.030 (0.554)*
Ln(Dis ) -0.903 (0.081)***
Ln(GDP) 0.896 (0.071)***
No. o obse . 66
Sou ce: own
No es: S anda d e o s in b acke s. * signi i can a 10%; ** signi i can a 5%; *** signi i can a 1%
Tab. 1: The co ela ion ma ix o a iables om he model (6)
Tab. 2: The ic ionless g a i y model – es ima ion esul s
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24 2016, XIX, 3
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4. G a i y Model wi h F ic ions,
Resul s and Discussion
F ic ions o any kind (bo de s, a di e en
language o cu ency, colonial ies, legisla i e,
cul u e, eligion, e c.), which in l uence eal ade,
can be inco po a ed in o he g a i y models as
well. In his pape he ollowing ade ic ions as
explana o y a iables we e examined:
Adjacency (A): he exis ence o na ional
bo de s is conside ed one o he mos
impo an ic ions in in e na ional ade.
Acco ding o Ande son (2010), c oss-bo de
ade is ypically educed by a ac o o 1/20
o 1/3 o i s po en ial alue. Howe e , when
a ade is ca ied ou be ween coun ies
wi hou common bo de , i is educed e en
mo e signi i can ly.
Cu ency (C): di e en cu ency migh
diminish ade olumes due o exchange
a es unce ain y; i may equi e addi ional
ansac ion cos s as well.
Loca ion in he Schengen a ea (LISA):
he Schengen a ea, es ablished in 1995,
abolished in e nal bo de con ols and
allowed ee ans e o people wi hin he
a ea. This could a ec ade olumes wi hin
he Schengen a o ably.
All hese ade ic ion a iables a e dummy
(bina y) a iables wi h alues 0 o 1, see
Table 3. Also se e al o he ade ic ions we e
conside ed a he beginning o he s udy, such as
democ acy index o impo ing coun ies, leng h
o common bo de s wi h Ge many, geog aphic
loca ion o impo ing coun ies (no jus hei
dis ance), o he ac whe he anspo a ion
o goods is managed on a land o a sea, bu
p elimina y esul s showed hese a iables
we e no s a is ically signi i can in he examined
models, o we e highly co ela ed wi h o he
independen a iables, so hey we e elimina ed
om he model. Ne e heless, hough only h ee
a iables associa ed wi h ade ic ions we e
le in he model, i s explana o y powe was e y
high (see esul s a he end o his sec ion).
The ic ion g a i y model has he ollowing
o m:
(%)
iii
E k GDP DIST

  
()
ii i
EXP C A LISA
 


(7)
Log- ans o m o (7) yields:
l
n( ) ln ln ln
iii
E k GDP DIST

  
ii i
CALISA

 (8)
The co ela ion ma ix o all eg esso s in
(8) is p o ided in Table 4. Va iance In l a ion
Fac o (VIF) o all explana o y a iables was
ound lowe han 4, wi h a maximum alue o
3.24 o LISA. The e o e, mul icollinea i y o he
model (8) did no cons i u e a p oblem.
Again, assump ions ega ding he use
o OLS we e examined, wi h he esul OLS
is an app op ia e es ima ion me hod wi h an
excep ion o he e oscedas ici y o ln(GDP), so
OLS wi h co ec ed he e oscedas ici y in G e l
was pe o med again. Es ima ion esul s a e
shown in Table 5.
All a iables associa ed wi h ade ic ions
we e ound s a is ically signi i can , hough only
adjacency was ound s a is ically signi i can a
0.01 le el. Mo eo e , eg ession coe i cien s
o all ic ion a iables we e ound nega i e as
expec ed. Acco ding o coe i cien s’ alues,
he mos impo an ade ic ion is adjacency
– coun ies no bo de ing wi h Ge many impo
signi i can ly less (by a ac o o 1.6) han
Ge many’s neighbo s. Adjus ed coe i cien o
de e mina ion o he model is e y high, R2 = 0.92,
which indica es he model is app op ia e and
possess high explana ion powe .
T ade ic ion Ac onym Values
The same cu ency C yes: 0, no: 1
Sha ed bo de s wi h Ge many A yes: 0, no: 1
Loca ion in he Schengen a ea LISA yes: 0, no: 1
Sou ce: own
Tab. 3: Selec ed ade ic ions
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3, XIX, 2016
Economics
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A guably, he mos impo an de e minan s
o expo olumes (o sha es) a e dis ance
be ween ading coun ies and a alue o GDP
o impo ing coun ies (coun ies’ weal h).
Howe e , when expo is low, in l uence o o he
ac o s may be no negligible. Fo example,
one la ge go e nmen o p i a e con ac (such
as opening o Bosch’s subsidia y in Kenya
du ing 2014), may change he expo da a
subs an ially. This can be easily illus a ed by
he S a is ische Bundesam (2013; 2014) da a
on Ge many’s expo s om 2012 and 2013.
The change in expo olumes be ween yea s
2012 and 2013 o he op en impo e s om
Ge many ( om F ance o Belgium) was abou
2% on a e age, bu o coun ies anked om
he 101 h o he 110 h place ( om El Sal ado
o Zambia) his yea - o-yea change amoun ed
o 46% on a e age. A ques ion a ises, whe he
such da a can be conside ed mo e han jus
a noise.
In his s udy only he da a o op 66 coun ies
wi h a leas 0.10% sha e o Ge many’s expo
was examined, bu i s o al sha e o expo is
97.6%. Remaining 173 coun ies sum up o
only 2.4% o Ge many’s expo , bu , i used,
hey would o m a majo i y o he da ase . Tha
is he main eason why hey we e le ou o his
s udy. Howe e , i a e aged da a o e some
longe pe iod ( i e o en yea s) demons a e
mo e s abili y, hen i migh be possible o
include hese coun ies as well.
Conclusions
In his s udy Ge many’s la es agg ega e expo
sha es wi h he use o a g a i y equa ion
wi hou and wi h ade ic ions we e examined.
This is a sligh ly di e en app oach om
a s anda d me hodology whe e ade olumes
a e s udied wi h he use o c oss-sec ional o
panel da a. Also, only da a o coun ies wi h
impo sha es om Ge many exceeding 0.10%
we e employed, as coun ies wi h lowe ade
olumes a e suscep ible o la ge yea - o-
yea l uc ua ions, which a ec he es ima ion
by g a i y equa ion nega i ely. The use o
a e aged da a o e longe pe iods migh be
mo e app op ia e as elimina ion o smoo hing
Ln(DIST) Ln(GDP) A C LISA
Ln(DIST) 1 0.506 0.545 0.493 0.726
Ln(GDP) 1 0.077 0.239 0.364
A 1 0.334 0.562
C 1 0.655
LISA 1
Sou ce: own
Reg esso s OLS wi h c. h.
cons . 0.416 (0.494)
Ln(Dis ) -0.695 (0.088)***
Ln(GDP) 0.877 (0.040)***
A -0.495 (0.135)***
C -0.351 (0.135)**
LISA -0.328 (0.171)*
No. o obse . 66
Sou ce: own
No es: S anda d e o s in b acke s. * signi i can a 10%; ** signi i can a 5%; *** signi i can a 1%
Tab. 4: Co ela ion o explana o y a iables in he model (8)
Tab. 5: Es ima ion esul s o he model (8)
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26 2016, XIX, 3
Ekonomie
26
o da a l uc ua ions may esul in much be e
s a is ical pe o mance o g a i y models in
gene al.
One o main i ndings o his s udy is ha
he ic ionless g a i y model is e y success ul
in i ing he da a wi h adjus ed coe i cien o
de e mina ion R2 equal o 0.74. As expec ed,
expo sha es we e ound ( oughly) di ec ly
p opo ional o a GDP o an impo e and
nega i ely p opo ional o impo e s’ dis ance.
Mo e in e es ing i ndings conce n he
g a i y model wi h ic ions, namely adjacency,
cu ency and loca ion in he Schengen a ea.
This model i ed he da a e en be e han
ic ionless model, wi h he adjus ed coe i cien
o de e mina ion R2 as high as 0.92. All ic ions
we e ound s a is ically signi i can a 0.10 le el,
and hei eg ession coe i cien s we e ound
nega i e, which means hey we e ac o s
con ibu ing o he ade dec ease indeed, wi h
(no )adjacency as he mos impo an ade
ba ie i sel .
The bo de e ec , which diminishes ade
subs an ially e en in cases whe e no bo de s
a e physically p esen (as in he EU), is s ill
conside ed puzzling, see Ande son (2010). One
possible explana ion, somewha o e looked in
he li e a u e, migh es in in o ma ion de i ciency.
To ade, in o ma ion abou demand and supply
o pa icula goods mus be a ailable o bo h
po en ial ade pa ne s. Bu subjec s o ade
(Ge many’s expo e s, o example) a e be e
in o med abou si ua ion a hei home ma ke in
Ge many han abou he si ua ion a neighbo ing
ma ke s ( o example in Belgium), because hey
p ima ily acqui e in o ma ion h ough home
Ge man media (TV, newspape s, In e ne , e c.,
and also h ough pe sonal con ac ). Howe e ,
nea bo de s wi h Belgium Ge man expo e s
can acqui e in o ma ion om Belgian sou ces,
and hus could be, a leas pa ially, in o med
abou i s ma ke . This in o ma ion acquisi ion
is e en mo e educed when he e is no bo de
be ween bo h coun ies (no adjacency), as
in o ma ion can be sha ed only indi ec ly (by
In e ne , sa elli e TV) o by (no so o en) pe sonal
con ac . Tha is why ade wi h a o eign subjec
is less likely.
This pape was suppo ed by he Minis y
o Educa ion, You h and Spo s Czech Republic
wi hin he Ins i u ional Suppo o Long- e m
De elopmen o a Resea ch O ganiza ion in
2015.
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Mg . Jiří Mazu ek, Ph.D.
Silesian Uni e si y in Opa a
School o Business Adminis a ion in Ka iná
Depa men o Ma hema ical Me hods
in Economics
[email p o ec ed]
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28 2016, XIX, 3
Ekonomie
28
Coun y Expo sha e (%) Dis . (km) GDP PPP
(bil. USD) C LISA A
001 F ance 9.146 879 2,534.5 0 0 0
002 Uni ed S a es 8.174 6,727 16,768.1 1 1 1
003 Uni ed Kingdom 6.906 932 2,320.4 1 1 1
004 Ne he lands 6.492 577 780.3 0 0 0
005 China 6.121 7,377 16,149.1 1 1 1
006 Aus ia 5.148 523 376.7 0 0 0
007 I aly 4.871 1,183 2,035.4 0 0 1
008 Swi ze land 4.293 752 432.0 1 0 0
009 Poland 3.885 520 896.8 1 0 0
010 Belgium 3.882 651 455.0 0 0 0
011 Russian Fede a ion 3.275 1,616 3,491.6 1 1 1
012 Spain 2.868 1,870 1,488.8 0 0 1
013 Czech Republic 2.843 280 287.6 1 0 0
014 Tu key 1.955 2,042 1,443.5 1 1 1
015 Sweden 1.894 813 418.2 1 0 1
016 Hunga y 1.601 691 229.6 1 0 1
017 Japan 1.562 8,940 4,667.6 1 1 1
018 Denma k 1.449 356 240.9 1 0 0
019 Ko ea Rep. 1.322 8,150 1,697.0 1 1 1
020 B azil 1.033 9,573 3,012.8 1 1 1
021 Slo akia 0.973 554 144.0 0 0 1
022 Uni ed A ab Emi a es 0.906 4,641 570.6 1 1 1
023 Romania 0.882 1,297 371.2 1 1 1
024 Saudi A abia 0.844 4,175 1,553.1 1 1 1
025 India 0.837 5,793 6,776.0 1 1 1
026 Mexico 0.818 9,741 2,058.9 1 1 1
027 Canada 0.807 6,146 1,518.4 1 1 1
028 Aus alia 0.785 16,062 1,052.6 1 1 1
029 Sou h A ica 0.780 8,789 662.6 1 1 1
030 No way 0.750 840 328.0 1 0 1
031 Finland 0.747 1,109 218.3 0 0 1
032 Po ugal 0.582 2,315 268.8 0 0 1
033 Singapo e 0.577 9,928 425.3 1 1 1
034 Taiwan 0.538 8,971 970.9 1 1 1
035 Hong Kong 0.514 8,767 382.5 1 1 1
036 Luxembou g 0.507 601 48.5 0 0 0
037 I eland 0.500 1,320 213.3 0 1 1
038 Uk aine 0.492 1,210 392.5 1 1 1
Appendix A: The da a o he g a i y model wi h ic ions – Pa 1
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