Vo, Thuy Dung; Yang, Laike; T an, Manh Dung
A icle
De e minan s in luencing Vie nam co ee expo s
Cogen Business & Managemen
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Sugges ed Ci a ion: Vo, Thuy Dung; Yang, Laike; T an, Manh Dung (2024) : De e minan s in luencing
Vie nam co ee expo s, Cogen Business & Managemen , ISSN 2331-1975, Taylo & F ancis,
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De e minan s influencing Vie nam coffee expo s
Thuy Dung Vo, Laike Yang & Manh Dung T an
To ci e his a icle: Thuy Dung Vo, Laike Yang & Manh Dung T an (2024) De e minan s
influencing Vie nam coffee expo s, Cogen Business & Managemen , 11:1, 2337961, DOI:
10.1080/23311975.2024.2337961
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ManageMen | ReseaRch aR icle
Cogen Business & ManageMen
2024, VoL. 11, no. 1, 2337961
De e minan s in luencing Vie nam co ee expo s
huy Dung Voa,b, laike Yanga and Manh Dung anc
aeas China no mal uni e si y, shanghai, China; bFacul y o Comme ce, Van Lang uni e si y, Ho Chi Minh Ci y, Vie nam;
cna ional economics uni e si y, Hanoi, Vie nam
ABSTRACT
his s udy in es iga es he impac le els o de e minan s on Vie nam’s co ee expo s
by applying he spa ial g a i y Model and panel da a o conside he spa ial dependence
in he g a i y model. he spa ial g a i y Model was employed o spa ially examine
in luencing ac o s among ading pa ne s by means o spa ial econome ic app oaches,
based on Vie namese co ee expo da a o 20 coun ies om 2005 o 2019. he esul s
e eal ha he expo o co ee p oduc s is in luenced by many de e minan s such as
he g oss domes ic p oduc (gDP) o he impo ing coun y, popula ion, exchange
luc ua ions, ee ade ag eemen (F a), non- a i ba ie s o ade, and ax a e. among
hese de e minan s, gDP g ow h and popula ion g ow h ha e a posi i e impac on
co ee expo s. On he o he hand, a i s, non- a i ba ie s and exchange a e a iables
a e ac o s ha nega i ely a ec co ee expo s. in addi ion, he dis ance be ween
coun ies has a nega i e impac on expo s o e all.
IMPACT STATEMENT
his s udy in es iga es he impac le els o de e minan s on Vie nam’s co ee expo s
by applying he spa ial g a i y Model and panel da a o conside he spa ial dependence
in he g a i y model. he spa ial g a i y Model was employed o spa ially examine
in luencing ac o s among ading pa ne s by means o spa ial econome ic app oaches,
based on Vie namese co ee expo da a o 20 coun ies om 2005 o 2019. he esul s
e eal ha he expo o co ee p oduc s is in luenced by many de e minan s such as
he g oss domes ic p oduc (gDP) o he impo ing coun y, popula ion, exchange
luc ua ions, ee ade ag eemen (F a), non- a i ba ie s o ade, and ax a e.
1. In oduc ion
co ee is an impo an expo c op and accoun s o a la ge p opo ion o Vie nam’s ag icul u al expo
u no e . acco ding o p elimina y da a epo ed by Vie nam cus oms in 2019, co ee expo s eached o
1,653,265 ons, wi h a o al expo alue o 2.85 billion UsD, accoun ing o 27% o o al expo alue.
in ecen yea s, he inc easing end and demand o co ee in he wo ld has d awn a en ion o he
impo ance o co ee p oduc s, p o iding an oppo uni y and a new g ow h engine o co ee expo s in
Vie nam. he e o e, he Vie namese go e nmen has pa icipa ed in a ious co ee-expo suppo p oj-
ec s including joining he eu opean-Vie nam F ee ade ag eemen (eVF a). his ag eemen has s ongly
suppo ed Vie nam’s co ee expo s o eu ope when he ax was elimina ed o all aw o p ocessed
co ee p oduc s speci ically aw co ee (dec eased om 7–11% o 0%), p ocessed co ees (down om
9–12% o 0%) which o e ed compe i i e ad an ages o Vie namese co ee.
Mo eo e , he accessibili y o he Wo ld ade O ganiza ion (W O), and o he in e na ional, and
egional o ganiza ions has c ea ed a la ge ma ke o he Vie namese co ee indus y. he policy o
ee ade and ma ke expansion in ecen yea s has b ough in o play he s eng h o all economic
sec o s in co ee p oduc ion and ading. he connec ion be ween domes ic and o eign ma ke s
© 2024 he au ho (s). Published by in o ma uK Limi ed, ading as aylo & F ancis g oup
CONTACT huy Dung Vo [email protected] eas China no mal uni e si y, shanghai, China
h ps://doi.o g/10.1080/23311975.2024.2337961
his is an open access a icle dis ibu ed unde he e ms o he C ea i e Commons a ibu ion License (h p://c ea i ecommons.o g/licenses/by/4.0/), which
pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any medium, p o ided he o iginal wo k is p ope ly ci ed. he e ms on which his a icle has been
published allow he pos ing o he accep ed Manusc ip in a eposi o y by he au ho (s) o wi h hei consen .
ARTICLE HISTORY
Recei ed 18 Feb ua y
2023
Re ised 19 Feb ua y 2024
accep ed 25 Ma ch 2024
KEYWORDS
spa ial g a i y model;
co ee; expo ; Vie nam
REVIEWING EDITOR
hui en (helen) cai,
Middlesex Uni e si y
Business school, Uni ed
Kingdom
SUBJECTS
econome ics;
in e na ional economics;
De elopmen economics
JEL CLASSIFICATION
CODES
c21; F1
2 .D. VO, l. Yang, anD M. DUng Ran
c ea ed a o able condi ions o he co ee indus y o expand i s consump ion channels domes ically
and in e na ionally. in ecen yea s, he la ges co ee expo ma ke s o Vie nam a e ge many, he
Uni ed s a es, i aly, spain and Japan. hese coun ies accoun o an a e age o 75.65% o he o al
co ee expo u no e o Vie nam. Which, ge many is Vie nam’s la ges co ee expo ma ke , accoun -
ing o 24.3% o o al co ee expo alue. i aly, secondly, accoun ed o 15.5%. spain, hi dly,
accoun ed o 13.67%. Finally, Japan accoun ed o 9.18%. he olume o co ee expo ed om
Vie nam om 2005 o 2019 has luc ua ed. Du ing 2005, 775.5 housand onnes o co ee we e
expo ed (speci ically, i eached 775.5 housand onnes in 2005), which esul ed in an expo ed alue
o abou 827 million dolla s.
F om 2006 o 2011, he olume o Vie namese co ee expo s ended o inc ease g adually compa ed
o 2005. F om 2012 o 2014, he olume o co ee expo s began o show a dec ease. Vie nam’s co ee
expo s ha e been cons an ly luc ua ing om 2015 o 2019 wi h a dec easing expo olume e e since.
al hough he Vie namese go e nmen has p omo ed and implemen ed many p ojec s o suppo co ee
expo s, in 2019 co ee expo s o aled 1,303.2 housand ons, a dec ease o 12.09% in olume and a
21.28% dec ease in p ice compa ed o expo s in 2018. co ee expo s in 2018 o aled 1,361.8 housand
ons, down 11.92% in olume and 19.28% in p ice compa ed o 2017. co ee expo olume in 2017
eached 1,476.5 housand ons, wo h 3,137.2 million UsD, down 19% in olume and 2.7% in p ice com-
pa ed o 2016 (See Figu e 1). he e o e, he analysis o ac o s a ec ing Vie nam’s co ee expo s will be
impo an in iden i ying and p edic ing he luc ua ions o expo s. Vie nam is he second la ges co ee
p oduce and expo e in he wo ld. howe e , 90% o Vie namese co ee expo s a e in aw o m: co ee
beans, which ha e low- alue expo s.
he main ac o s a ec ing Vie nam’s co ee expo s, ha ha e caused a sha p decline in co ee expo s
in ecen yea s, a e economic condi ions, changes in ading pa ne s, exchange a e luc ua ions, F ee
ade ag eemen s (F as), ax a es, non- a i ba ie s, and o he s. howe e , s udies on he co ee ade ha
comp ehensi ely conside ac o s a ec ing Vie nam’s co ee expo s ha e no been esea ched ho oughly.
Figu e 1. Co ee expo s o Vie nam o he pe iod 2005–2019.
Sou ce: gene al Depa men o Vie nam Cus oms in 2019.
cOgen BUsiness & ManageMen 3
2. Li e a u e e iew
nume ous wo ks go u he in o esea ch and analysis on he elemen s in luencing Vie nam’s co ee
expo s, bu only a small numbe o hem use g a i y models o make co ee expo s. Pham (2018) uses
a g a i y model o assess he ma ke size, economy size, popula ion, and gDP de e minan s in luencing
Vie nam’s co ee expo s o he chinese ma ke . howe e , hese s udies do no accoun o he spa ial
dependence o spillo e e ec o ade lows be ween coun ies when using g a i y models. he s udy
by Do (2019), which also used he g a i y model, examined and iden i ied he cu ency a e as one o
he ac o s in luencing Vie nam’s co ee expo s o na ions in he eu opean Union om 2010 o 2014. as
a esul , i is di icul o de e mine how he exchange a e would a ec Vie nam’s expo s o co ee. By
using he eal e ec i e exchange a e, which is he weigh ed a e age o he p opo ion o co ee expo s,
compu ed h ough he p ice index o co ee, consump ion, and gDP, his s udy will hus add ess he
laws o he p io s udy.
he s udy by nguyen (2021) ha in es iga es he elemen s a ec ing Vie nam’s co ee expo s uses he
g a i y model; he esea ch indings show ha ade expenses a e one o hose aspec s. hese s udies,
howe e , did no pay pa icula a en ion o elemen s like a i s and non- a i ba ie s, which ha e
ecen ly eme ged as a sou ce o pa icula wo y o na ions. Bui (2020) es ima ed ha he expo ol-
ume o Vie namese co ee and cocoa, which a e he main expo i ems o china, will inc ease by an
a e age o 2.1% when he eVF a, and asean – china we e signed. Resea ch h ough he g a i y model
in he es ima ion o ac o s a ec ing co ee and cocoa expo s showed ha ac o s such as ansi ime,
eigh a es, exchange a es, W O membe ship and F a ag eemen s ha e a s ong in luence on Vie nam’s
co ee and cocoa expo s. he esea ch does, howe e , no include ime-in a ian a iables, such as he
dis ance be ween coun ies, because hey pose a comple e mul icollinea i y issue in he ixed-e ec s
analysis. ins ead, he dis ance-by-coun y analysis is ca ied ou a a la e le el in he andom e ec s
model. he oil p ice gap a iable, which is compu ed by looking a oil p ices o he dis ances be ween
po s ha ha e been used, will be employed in his s udy o be mo e p ecise. Because he di e ence in
oil p ices among na ions changes, i is possible o examine how dis ance be ween na ions a ec s expo s
e en in a ixed-e ec s model.
in ecen yea s, s udies in es iga ing he ac o s a ec ing Vie nam’s co ee expo s based solely on he
g a i y model ha e no ocused on spa ial dis ance. Resea ch applying spa ial g a i y models o analyze
ac o s a ec ing Vie nam’s co ee expo s is cu en ly oo limi ed. he e a e e y ew s udies ocusing on
mac o ac o s such as gDP, geog aphical dis ance, exchange a es, e c. he e o e, his s udy analyzes he
ac o s a ec ing Vie nam’s co ee expo s using a spa ial g a i y model, using panel da a o conside
spa ial dependence in he g a i y model. F om he e, i helps manage s make implica ions and manage-
men policies ela ed o imp o ing he ou pu and quali y o Vie nam’s co ee expo s.
Rendleman and Vasin a hana (2013) analyzed he impac o non- a i ba ie s such as haza d analysis
and c i ical con ol Poin s (haccP) and he Bio e o ism ac on Us ag icul u al impo s employing a i-
ables dis ance, gDP, e c. he dependen a iable used in he analysis is Us ag icul u al impo s and
independen a iables a e based on haza d analysis and c i ical con ol Poin s (haccP) which ope a ed
in he Us in 1998 when bio e o ism laws wen in o e ec and ading pa ne s’ expo olumes. he
es ima ed esul s showed ha haza d analysis and c i ical con ol Poin s (haccP) and he Bio e o ism
ac ha e a nega i e impac on Us ag icul u al impo s, bu i is no s a is ically signi ican . howe e , he
s udy using he g a i y model did no ake in o accoun spa ial dependence such as dis ance be ween
coun ies.
a a ie y o analy ical me hods ha e been applied o analyze he in luencing ac o s in co ee expo ,
including he g a i y model which was in oduced as a heo y o in e na ional ade. his model shows
ha he size o ade a ies wi h gDP and he dis ance be ween he wo coun ies. in addi ion, i is
widely used o analyze he impac o ee ade ag eemen s, ax a es and non- a i ba ie s on ade
be ween coun ies.
e ene and Kelle (2002) e alua ed ha he g a i y model is e ec i e in explaining he bila e al ela ionship
be ween he wo coun ies. ne e heless, p e ious s udies ha e no explici ly examined he spa ial dependence
be ween ading pa ne s. Fo example, ansac ions be ween ce ain ading pa ne s can a ec ansac ions
be ween o he expo ing coun ies, and ice e sa. in o he wo ds, expo de e minan s such as exchange a e
4 .D. VO, l. Yang, anD M. DUng Ran
luc ua ions, F a ag eemen s, ax a es and non- a i ba ie s no only a ec ading pa ne s, bu also o he
coun ies’ expo s ha e a la ge sha e o ade. he e o e, bo h analysis o impac s be ween ading pa ne s and
spillo e e ec s be ween ading pa ne s should be included in he model and analysis.
nguyen (2020) analyzed he ac o s ha posi i ely impac ade acili a ion wi h he applica ion o he spa-
ial g a i y model and showed ha ade acili a ion a iables such as logis ics and anspo a ion in as uc u e
ha e a signi ican in luence on bila e al ade. in addi ion, he esul s e ealed ha p og ess in he a e age
expo alue o he neighbo s o a ading pa ne has dec eased he alue o ha ading pa ne ’s expo s
which means ha a iables in he neighbo ing coun ies o a ading pa ne ha e a nega i e e ec on he
expo s o bo h coun ies. howe e , by e alua ing one-way ade pa e ns in e ms o he g a i y model, he
s udy is es ic ed o he in es iga ion o mul ina ional ade pa e ns. as a esul , his esea ch in eg a es
mul i-coun y comme ce, which is de ined as a ype o bila e al ade, o add ess he sho comings o ea lie
esea ch. nguyen (2012) analyzed he a i educ ion o sou h Ko ea based on he ade di e sion e ec om
impo s o neighbo ing coun ies o impo s om he Us h ough he aboli ion o a i s since he signing o
he ag eemen Ko ea-Us ee ade ag eemen (F a) es ima ed esul s based on space g a i y model ha he
olume o ade be ween Ko ea and he Us inc eased due o a i educ ions, whils o e all ade olume wi h
neighbo ing coun ies dec eased due o ipple e ec .
gau schi (1981) ound ha when he model is es ima ed wi hou conside ing he in luence o spa ial
ac o s be ween coun ies, he es ima ed coe icien s o ela ed a iables will end o be o e es ima ed,
making i impossible o gua an ee he eliabili y o he o e all es ima e. in his ega d, anselin (1988)
sugges ed ha when es ima ing a model using spa ial da a such as coun y o egion, he dependen
a iable o e o o an indi idual coun y is co ela ed wi h he dependen a iable o e o obse ed in
neighbo ing coun ies, his is called spa ial dependence which is also known as spa ial au oco ela ion.
Figu e 2 illus a es ha he dependen a iables o e o s a e spa ially co ela ed. i spa ial depen-
dence is no conside ed, his iola es he basic assump ions o econome ics conce ning explana o y
a iables and e o s. When es ima es a e made by using gene al eg ession, spa ial au oco ela ion will
appea biased o ine icien . in e ms o he spa ial au oco ela ion in he g a i y model, lesage and
Pace (2008), basic a iables in he g a i y model o he coun ies include gDP, popula ion, p oduc ion o
analyzed commodi ies, e c. his is because he da a is buil in o an a i icial spa ial uni called a bo de
e en hough hey end o be in e connec ed. Fo example, he gDP and popula ion o eU and naF a
coun ies a e in luenced by he cha ac e is ics and p oximi y o neighbo ing coun ies; he socio-economic
si ua ion o neighbo ing coun ies and he mobili y o he labo o ce be ween coun ies. Fo his eason,
i is necessa y o ac i ely conside spa ial in luences when using g a i y models, i.e. spa ial dependence.
he e a e, howe e , e y ew domes ic s udies on g a i y models ha ake in o accoun spa ial depen-
dency (au oco ela ion) o he spillo e e ec o ade lows be ween na ions. he e a en’ many s udies
Figu e 2. wo majo ypes o au oco ela ion spa ial dependencies.
Sou ce: Lesage (1999) spa ial econome ics.
cOgen BUsiness & ManageMen 5
ha quan i a i ely examine he a iables in luencing Vie nam’s expo s o co ee. o o e come he sho -
comings o p e ious s udies, his s udy will employ a spa ial g a i y model ha akes spa ial dependence
in o accoun . he objec i e is o ga he , analyze, and unc ion-analyze da a. By u ilizing a spa ial g a i y
model o analyze he a iables impac ing Vie nam’s co ee expo s, ake no e o he managemen policies
connec ed o he enhancemen o expo ou pu .
3.Theo e ical amewo k and esea ch me hodology
3.1. Theo e ical amewo k
he i s g a i y model was applied by leibens ein and inbe gen (1962). i is named he ‘g a i y model’ like
isaac new on’s law o g a i y. he heo y o he g a i y model is de i ed om new on’s law o g a i a ion o
explain he low o ade be ween wo coun ies. inbe gen’s applica ion o g a i y model easoning o in e -
na ional ade analysis is shown in model (1) below, which means ha he amoun o ade be ween coun-
ies is di ec ly p opo ional o he economic size o he coun ies and in e sely p opo ional o he dis ance.
YA
XX
D
d
od
od
od
=
×
≠,o (1)
in model (1),
Yod
is he numbe o ansac ions be ween he o igin (o) and he des ina ion (d); a is
a ac i e coe icien ; X
o
is economic scale o o igin;
Xd
is economic scale o des ina ion;
Dod
is he phys-
ical dis ance be ween wo ading pa ne s. o apply o empi ical analysis, aking he loga i hm o bo h
sides o model (1) will be as ollows.
ln ln ln ln ,Y X X D od
od o d od
=++ + ≠
αβ β γ
123
(2)
in model (2),
α
,
β
,
γ
is he coe icien o each explana o y a iable; his g a i y model is use ul o
es ing he elas ici y o ade olume o key a iables by adding ade-in luenced a iables in o he
model (2), i can be analyzed as an ex ended g a i y model.
3.2. Spa ial g a i y model
he dis ance a iables a e hough o be signi ican in he gene al g a i y model, i is because he lows
o ade be ween he wo coun ies a e independen . he e o e, spa ial dependence o spillo e e ec s
o ade lows be ween coun ies a e no conside ed. g i i h (2007), i he olume o ade be ween
coun ies i and j changes, hen he olume o ade be ween coun ies i and k o be ween coun ies j
and k can also change. i spa ial dependence o spa ial au oco ela ion be ween coun ies is no consid-
e ed, bias o ine iciency o he es ima o may occu .
lesage and Pace (2009) in oduced spa ial econome ic model o sol e he p oblem o spa ial dependence
be ween obse a ions. he basic model used o es ima ion wi h spa ial panel da a is he spa ial lag model,
spa ial e o model and gene al spa ial model o conside he spa ial dependence be ween dependen a iables.
i hese spa ial measu es a e applied in he g a i y model, he ade lows be ween expo e s a e as ollows.
spa ial lag g a i y model3
Y WY WY W Y X X D
NI
o o d d w w oo dd
N
= + + + + ++
()
ρ ρ ρ ββ ε
εσ
γ
~ ,0 2
2
(3)
spa ial e o g a i y model
YX X D u
oo dd
= + ++
ββ
γ
(4)
u Wu Wu W u
oo dd ww
=++ +
λλλ ε
6 .D. VO, l. Yang, anD M. DUng Ran
ε
∼
()
NI
N
02
2
,
σ
gene al spa ial g a i y model
Y WY WY W Y X X D u
o o d d w w oo dd
= + + + + ++
ρ ρ ρ ββ
γ
(5)
u Wu Wu W u
oo dd ww
=++ +
λλλ
ε
ε
∼
()
NI
N
02
2
,
σ
W
is spa ial weigh ma ix, used o de e mine he s uc u e o he spa ial dependence be ween indi-
idual obse a ions, based on in e se dis ance ma ix o con igui y ma ix.
Y
is he dependen a iable.
X
is he explana o y a iable.
WY
is he space lag dependen a iable;
Wu
is he spa ial lag e o e m;
α
,
β
,
γ
a e he coe icien s o he es ima es o X WY Wu
,,.
he eliabili y o he es ima e is exp essed h ough he unbiasedness o he es ima ed esul . an es ima o is
said o be e ec i e i i s compu a ion uses o exploi s all he in o ma ion ela ed o he da a as well as he
model’s assump ions. he combina ion o spa ial e o a iables (
Wu
) plays a ole in conside ing he spa ial
dependence be ween e o s in he spa ial g a i y model and i s meaning is like he spa ial di e ence dependen
a iable. he a iables
WY
and
Wu
ha e a ole in conside ing spa ial dependence in he spa ial g a i y model.
lesage and Pace (2008) applied a spa ial weigh ing ma ix o de e mine he spa ial dependence
be ween n indi idual obse a ions, means wo-way ade lows be ween coun ies by applying he ol-
lowing h ee ypes o spa ial weigh ing ma ices acco ding o o igin and des ina ion.
O igin-based spa ial weigh ma ix
WI W
W
W
W
oN
=⊗=
…
…
0
0
00
0
00
0
⋮
⋮
⋱
Des ina ion-based spa ial weigh ma ix
W WI
W
W
W
W
dN
n
n
nn
=⊗=
…
…
0
0
00
⋮
⋮
⋱
O igin and Des ina ion-based spa ial weigh ma ix
W WW I W W I W W
w od N N
= =⊗
()
⊗
()
=⊗
.
his spa ial g a i y model depends on he cause o he spa ial dependence, he coe icien s o he
spa ial lag dependen a iable, and he spa ial e o a iable.
ρτ
τ
, ,,=
odw
and
λτ
τ
, ,,=
odw
depending
on s a is ical signi icance, di e en o ms can exis . he cause o spa ial dependence will be conside ed
h ough he hypo hesis and empi ical analysis model o his s udy.
4. Analysis using spa ial g a i y model
4.1. Resea ch model and hypo heses
acco ding o Beens ock and Felsens ein (2013), in luen ial ac o s include he eal e ec i e exchange
a e, gDP, popula ion, dis ance be ween coun ies, ax a es, and he quan i y o non- a i ba ie s. he
esea ch’s indings also indica e ha while dis ance be ween na ions, ax a es, he amoun o non- a i
cOgen BUsiness & ManageMen 7
ba ie s, and he eal e ec i e exchange a e a e all ac o s ha nega i ely a ec expo s, gDP and pop-
ula ion a e wo elemen s ha posi i ely a ec hem. in which gDP is also known as g oss Domes ic
P oduc . gDP is he alue o inal physical p oduc s and se ices p oduced by he economy in a ce ain
pe iod o ime. Popula ion is he o al popula ion o a coun y. Dis ance be ween coun ies means he
geog aphical dis ance be ween wo coun ies. ax a e is he ax a e ha mus be paid pe uni ha
de e mines he alue o he ax a e payable o a axable objec . ax a es a e exp essed in pe cen age,
depending on he condi ions o he ype o subjec o ela ed condi ions o assessmen . he numbe o
non- a i ba ie s a e ways o p e en and hinde impo ed goods bu do no impose impo axes. he
eal e ec i e exchange a e is he a e ha ep esen s he in e na ional compe i i eness o ha cu ency.
he g a i y model de eloped by Beens ock and Felsens ein (2013) is employed in his esea ch o
examine he a iables in luencing Vie nam’s co ee expo s. gDP, popula ion, dis ance be ween na ions,
ax a e, quan i y o non- a i ba ie s, and eal e ec i e exchange a e a e some examples o hese
de e minan s.
he de ini ions and da a sou ces employed in he empi ical analysis a e p esen ed in able 1, below:
Dis ance be ween coun y o o igin and coun y o des ina ion is aken om aXsMa ine loca ion,
po - o-po dis ance (nau ical miles) is ea ed as a dis ance a iable. in he po se up, he main po s
ep esen ing each coun y we e selec ed as he a ge including ge many (hambu g), Usa (hous on),
i aly (naples), spain (Valencia), Japan (Fukuoka), Philippines (Manila), Russia (no o ossiysk), Belgium
(an we p), alge ia (O an), hailand (laem chabang), is ael (hai a), indonesia (sema ang), china (Qingdao
Po ), sou h a ica (cape own), cambodia (sihanouk ille), chile (Valpa aiso), singapo e (Keppel), Myanma
(Kyaukpya), hunga y (hambu g), Poland (gdansk). his is he sho es dis ance be ween po s excluding
ansi poin s. Mo eo e , by se ing he speed o ship o 25 kno s (k s) o make he a e age speed o
ood anspo ships and con aine s, he numbe o ope a ing days be ween po s has been calcula ed.
since he dis ance a iable is ixed yea - o-yea , he annual uni o oil p ice should be used o o e come
he sho coming o he ixed-e ec model. he oil p ice s anda d o Wes exas in e media e (W i) is he
ep esen a i e c ude oil o he Uni ed s a es as he s anda d o luc ua ions in wo ld oil p ices aken
om he U.s. ene gy in o ma ion adminis a ion (eia). addi ional analysis was pe o med by gene a ing
dis ance a iables acco ding o he luc ua ions.
he a i a iable is calcula ed based on he ax a e (%) o co ee p oduc s o he Wo ld ade
O ganiza ion (W O) using he ha monized sys em codes (hs code). he da a is ob ained using he
ha monized sys em codes (hs code) by applying he a e age ax a e o each i em due o he di e en
ax a es. co ee expo p oduc ion da a om o igin o des ina ion was ob ained om he Food and
ag icul u e O ganiza ion o he Uni ed na ions (FaO).
he da a on non- a i ba ie s om he s a is ics o he Wo ld ade O ganiza ion (W O) is aken in
his s udy as he sPs and B da a o use o he non- a i ba ie a iables. non- a i ba ie s o each
i em a e applied di e en ly, hus, he da a on non- a i ba ie s is used h ough hs codes.
he exchange a e a iable is he eal e ec i e exchange a e o he co ee p oduc om 2005 o
2019. in he case o nominal exchange a es, annual da a o he s anda d exchange a e o he a e age
Table 1. Va iables and sou ce da a.
Va iables Con en s sou ces
Y
Bila e al ade expo s be ween he wo
coun ies ($)
un Com ade
GDP
g oss domes ic p oduc o coun y ($) Wo ld Da abank, WDi
POP
Popula ion o coun y (pe son)
D
he sho es dis ance be ween coun ies (km),
(25 kno ), (oil p ice).
aXsMa ine, eia
PROD
o al ou pu o co ee expo s ( ) Fao
TA
ax a es o co ee, using Hs code (%) W o
NTB
numbe o non- a i ba ie s (sPs, B ) W o
REER
Real e ec i e exchange a e (ReeR) iMF, unC aDs a
FTA
F ee ade ag eemen in o ce W o
g oss domes ic p oduc (gDP) and popula ion da a a e independen a iables. hese da a a e aken om Wo ld Da abank
and Wo ld De elopmen indica o , which include he g oss domes ic p oduc (gDP) and he popula ion o he coun y o
o igin and he coun y o des ina ion.
14 .D. VO, l. Yang, anD M. DUng Ran
Fi s , an inc ease in gDP and popula ion can posi i ely a ec expo s. his is in consis en wi h
exis ing g a i y model heo y and is conside ed o e lec eali y well. his esul is also consis en
wi h hypo heses h1 and h2, meaning ha when he gDP o bo h coun ies inc eases, Vie nam’s co -
ee expo s inc ease. and as he popula ions o bo h coun ies inc ease, Vie nam’s co ee expo s
inc ease.
Second, he dis ance be ween wo coun ies is calcula ed h ough an elas ici y o he oil p ice gap o
-0.845 indica ing an inc eased gap will cause expo s o all. his is consis en wi h hypo hesis h3, mean-
ing ha as he dis ance be ween he wo coun ies becomes bigge , Vie nam’s co ee expo s will
dec ease.
Thi d, he highe he ax a es, non- a i ba ie s, and he eal e ec i e exchange a e, he lowe
expo s a e, which a e ac o s ha nega i ely a ec expo s. his esul is consis en wi h hypo heses h4,
h5 and h6, meaning ha he highe he ax a e be ween he wo coun ies, he lowe Vie nam’s co ee
expo s. When he numbe o non- a i ba ie s is g ea e , Vie nam’s co ee expo s dec ease, and when
he eal e ec i e exchange a e is highe , Vie nam’s co ee expo s dec ease.
hus, i can be obse ed ha he esea ch indings suppo he heo y ha has been pu o wa d,
and his inding is ema kably compa able o hose o Beens ock and Felsens ein (2013).
he ecen changing end o he ade en i onmen is ha he ax a e is dec easing due o he
inc ease in he numbe o ade ag eemen s such as F as be ween ading pa ne s, which is expec ed
o ha e a posi i e impac on co ee expo s. On he o he hand, due o he exchange a e policy, he
economic si ua ion o each coun y is di e en , and he policies o imp o e compe i i eness cause he
exchange a e o ise, nega i ely a ec ing expo s. in addi ion, non- a i ba ie s, unlike a i s, which a e
di icul o unde s and and no quan i ied, ope a e indi idually in each coun y, making i di icul o ind
solu ions o imp o e. he e o e, i i is possible o de elop coun e measu es o he ac o s ha nega i ely
a ec expo s and imp o e he ac o s ha posi i ely a ec expo s o help Vie nam es ablish a mo e
de eloped co ee expo ma ke .
Fou h, in he es ima ion esul s o he spa ial g a i y model, he signs o indi ec e ec s and di ec
e ec s a e di e en , he esul s o he analysis ha e shown ha expo lows be ween speci ic ading
pa ne s can c ea e a ipple e ec ha has a nega i e impac on expo s be ween neighbo ing coun ies.
in addi ion, Vie nam can apply high echnology o co ee p oduc ion o help inc ease expo alue, make
he mos o na u al esou ces, labo o ce, p oduc ion equipmen , o imp o e e iciency and quali y o
expo ed co ee p oduc s.
howe e , his s udy is limi ed in ha i only examines he a iables in luencing he expo o co ee
p oduc s om Vie nam o he 20 la ges expo ma ke s as o 2019. in addi ion, because he dependen
a iable was he o al expo alue o co ee p oduc s, i was no possible o pe o m a b eakdown by
co ee i em. Due o he lack o andomness in he use o he ma ix o iden i y he spa ial dependence,
he s udy can also be a bi a ily s udied. i is possible o examine and es he model using a ious
ma ices while accoun ing o geog aphical dependence in la e esea ch. Mo eo e , a mo e comp ehen-
si e examina ion o ac o s ha in luence Vie nam’s co ee expo s and i s policy implica ions can be
conduc ed.
No es
1. spa ial lag e e s o he case whe e he dependen a iables o coun y i a e a ec ed simul aneously by he
explana o y a iables o coun y i and coun y j. he e o e, he dependen a iables and he e o a e also
co ela ed wi h each o he .
2. spa ial e o is a case whe e spa ial uni s a e linked be ween e o s wi h di e en spa ial uni s. hus, in his
case, he basic s a is ical assump ion is iola ed and becomes less e icien when he es ima e does no con-
side spa ial e ec s.
3. Model (3),
WY
o s ands o he ypical in isible low be ween he neighbo ing na ions o he o igin (o) and he
des ina ion (d). o pu i ano he way, imagine ha na ions a, b, and c a e close o he beginning loca ion and
ha coun ies k, l, and m a e close o he des ina ion. he a e age ade low be ween o igin (o) and des ina-
ion (d) in na ions a, b, and c is
WY
o.
WY
d deno es he a e age amoun o ade lowing om he o igin o he
nea by coun ies (k, l, and m). he a e age o ade lows om coun ies a, b, and c nea he o igin o coun ies
k, l, and m nea he des ina ion is ep esen ed by he e m
Ww
. he unbiasedness o he es ima ed esul se es
cOgen BUsiness & ManageMen 15
as a measu e o he es ima e’s accu acy. i an es ima o uses o akes ad an age o all he da a- ela ed in o -
ma ion and model assump ions in i s compu a ion, i is said o be e icien . When e alua ing he spa ial de-
pendency be ween mis akes in he spa ial g a i y model, he combina ion o spa ial e o a iables (Wu) is
impo an , and i s signi icance is compa able o ha o he di e ence dependen a iable o ime.
4. 20 coun ies including ge many, Usa, i aly, spain, Japan, Philippines, Russia, Belgium, alge ia, hailand, is ael,
indonesia, china, sou h a ica, cambodia, chile, singapo e, Myanma , hunga y, Poland, laos.
Disclosu e s a emen
no po en ial con lic o in e es was epo ed by he au ho (s).
Abou he au ho s
Thuy Dung Vo is a lec u e a Van lang Uni e si y, Vie nam. she is cu en ly wo king as a doc o al
s uden a eas china no mal Uni e si y, shanghai, china. he in e es s a e in e na ional economics
and business.
Laike Yang is a p o esso , and Di ec o o he in e na ional ade Depa men o he eas china no mal Uni e si y,
shanghai, china. he go PhD a Xiamen Uni e si y in china. his in e es s a e in e na ional ade and he en i onmen ,
Upg ading o he global alue chain and ade s uc u e, Regional economic in eg a ion, p oduc ion segmen a ion
and echnological p og ess.
Manh Dung T an is an associa e P o esso , and senio lec u e a he na ional economics Uni e si y, Vie nam. he is
now a Depu y edi o -in-chie o he Jou nal o economics and De elopmen . he go PhD a Macqua ie Uni e si y in
aus alia. his in e es s a e in e na ional economics and business, inance, and managemen .
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