Nguyen, Phuong Thi Minh; Ho, Phuc T ong; Pham, Hung Xuan
A icle
Impac s o seasonal clima e a ia ion on ice yield:
E idence om he Cen al Coas o Vie nam
Cogen Economics & Finance
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Impac s o seasonal clima e a ia ion on ice
yield: E idence om he Cen al Coas o Vie nam
Phuong Thi Minh Nguyen, Phuc T ong Ho & Hung Xuan Pham
To ci e his a icle: Phuong Thi Minh Nguyen, Phuc T ong Ho & Hung Xuan Pham (2024)
Impac s o seasonal clima e a ia ion on ice yield: E idence om he Cen al Coas o
Vie nam, Cogen Economics & Finance, 12:1, 2421894, DOI: 10.1080/23322039.2024.2421894
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DEVELOPMENT ECONOMICS | RESEARCH ARTICLE
Impac s o seasonal clima e a ia ion on ice yield: E idence om he
Cen al Coas o Vie nam
Phuong Thi Minh Nguyen , Phuc T ong Ho and Hung Xuan Pham
Facul y o Economics and De elopmen S udies, Uni e si y o Economics, Hue Uni e si y, Hue Ci y, Vie nam
ABSTRACT
This s udy in es iga es he impac o seasonal clima e change on ice p oduc i i y in
Vie nam’s Cen al Coas , using 26yea s o da a om 1996 o 2021. To achie e his
objec i e, he s udy applies a Feasible Gene alized Leas Squa es (FGLS) model o
ob ain obus es ima es o he panel analysis. The indings e eal he consequences
o clima e a ia ion on ice p oduc i i y h oughou di e en seasons. No ably,
inc eases in maximum empe a u e du ing he win e –sp ing season and minimum
empe a u e du ing he summe –au umn season boos ice yields, while highe max-
imum empe a u es in summe –au umn and minimum empe a u es in win e –sp ing
educe yields. Speci ically, a 1% inc ease in maximum empe a u e imp o es win e –
sp ing yields by 1.66% bu educes summe –au umn yields by 1.01%, while a 1% ise
in minimum empe a u e dec eases win e –sp ing yields by 0.30% bu enhances sum-
me –au umn yields by 3.32%. In addi ion, inc eases in bo h maximum and minimum
ela i e humidi y posi i ely impac yields. The s udy also inds ha a 1% inc ease in
maximum p ecipi a ion sligh ly educes summe –au umn yields. These indings p o-
ide impo an insigh s o de eloping s a egies o imp o e he esilience o ice p o-
duc ion o clima e change.
IMPACT STATEMENT
This s udy aims o p o ide eliable scien i ic e idence on he impac s o seasonal cli-
ma e change on ice p oduc i i y o e an ex ended pe iod o ime in he Cen al
Coas o Vie nam. I s indings can assis policymake s in p oposing clima e-adap i e
a ming measu es o mi iga e ad e se e ec s on ice p oduc ion and may also se e
as a e e ence o egions wi h simila clima es in o he ice-p oducing coun ies.
ARTICLE HISTORY
Recei ed 18 May 2024
Re ised 1 Sep embe 2024
Accep ed 22 Oc obe 2024
KEYWORDS
Clima e change; Cen al
Vie nam; FGLS model;
paddy p oduc i i y;
seasonal e ec s
SUBJECTS
Sus ainable De elopmen ;
Economics and
De elopmen ;
En i onmen al Economics;
Ru al De elopmen
1. In oduc ion
Clima e change has exe ed a signi ican impac on ag icul u e wo ldwide and poses a se ious h ea o
global ood secu i y (Malhi e al., 2021; Tilahun, 2021; Eme u, 2022; Mubenga-Tshi aka e al., 2023). The
Uni ed Na ions F amewo k Con en ion on Clima e Change (UNFCCC) de ines clima e change as a phe-
nomenon esul ing om human ac i i ies ha al e he composi ion o he global a mosphe e, leading
o shi s beyond he na u al clima e a iabili y obse ed o e compa able pe iods (WHO, 2016). These
changes a e moni o ed h ough key indica o s iden i ied by he Global Clima e Obse ing Sys em
(GCOS), including su ace empe a u e, ocean hea con en , a mosphe ic CO2 le els, and sea le els,
A c ic & An a c ic sea ice ex en , ocean acidi ica ion, and glacie (WMO, 2023). In ligh o he di icul ies
in collec ing comp ehensi e clima e da a, su ace empe a u e is o en used o assess he impac o cli-
ma e change on ag icul u e. Howe e , clima e change is now econside ed o e e o any al e a ions in
clima ic indica o s o e ime, such as empe a u e o p ecipi a ion (Li, 2023). Acco ding o NASA (2023),
he en mos ecen yea s ank as he wa mes on eco d, and Ea h’s a e age empe a u e in 2023 was
app oxima ely 1.36 C highe han he la e 19 h cen u y (1850–1900) p eindus ial a e age. This indica es
ha clima e change will con inue o be a wo ying conce n o global ag icul u e.
CONTACT Phuong Thi Minh Nguyen [email p o ec ed] Uni e si y o Economics, Hue Uni e si y, Hue Ci y, Vie nam
ß2024 The Au ho (s). Published by In o ma UK Limi ed, ading as Taylo & F ancis G oup
This 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. The 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 .
COGENT ECONOMICS & FINANCE
2024, VOL. 12, NO. 1, 2421894
h ps://doi.o g/10.1080/23322039.2024.2421894
Besides, among a ious c ops, ice is he hi d-la ges cul i a ed ce eal c op wo ldwide (Baza gan
e al., 2014), and i se es as he undamen al ood s aple o mo e han hal o he wo ld’s popula ion,
wi h an annual p oduc ion o app oxima ely 480 million me ic ons o milled ice (Mu hayya e al.,
2014; I shad e al., 2018). Du ing he ea ly 1980s, Vie nam ansi ioned om i s s a us as a ood-
impo ing na ion o eme ging as one o he o emos ice expo e s globally wi hin a pe iod o less han
wo decades (Vu & Nguyen, 2021). By 2023, Vie nam su passed Thailand o become he second-la ges
ice expo e globally, wi h a o al olume o 5.9 million ons (USDA, 2023). Rice cul i a ion in Vie nam
plays a pi o al ole in enhancing global ood secu i y (Sh es ha e al., 2016).
Howe e , Vie nam is among he coun ies ha a e mos ulne able o he impac s o clima e change
(Dasgup a e al., 2009; Wo ld Bank G oup and Asian De elopmen Bank, 2020; Nguyen and Sc imgeou ,
2022; Anh e al., 2023). Nguyen e al. (2008) and Anh e al. (2023) indica ed ha Vie nam’s ex ensi e
coas line, lanked by high moun ains and la loodplains, exposed o e 70% o i s popula ion o a ious
na u al haza ds. Among hese egions, he Mekong Ri e Del a, he no h-cen al egion, and he cen al
coas al egion ace heigh ened ulne abili y o he impac s o global wa ming. Gi en he challenges
posed by clima e change, unde s anding i s e ec s on ice p oduc ion is i al o de ising solu ions o
enhance cu en ood secu i y and bols e he esilience o ag icul u al sys ems. Mo eo e , because
Vie nam has complex seasonal c opping sys ems, wi h c op calenda s and pa e ns a ying ac oss ag oe-
cological zones, i is c ucial o assess hese impac s wi hin he con ex o speci ic seasonal a ia ions o
e ec i ely in es iga e he impac s o clima e change on ice p oduc ion (T inh, 2018). Ne e heless,
esea ch on he impac o clima e a iabili y on ice p oduc ion in Vie nam, pa icula ly in he cen al
egions, emains sca ce (Chung e al., 2015).
Recen s udies ha e explo ed he impac o clima e change on ag icul u al p oduc ion in Vie nam,
pa icula ly emphasizing he cul i a ion o ice, gi en i s signi icance as one o he wo ld’s majo ice-
p oducing egions. Despi e many s udies on he economic impac o clima e change in many ields,
mos o hem used c oss-sec ional da a and ime se ies da a (Chung e al., 2015; Huynh e al., 2020), and
a ew used panel da a (T inh, 2018; Nguyen & Sc imgeou , 2022). In addi ion, mos o hese s udies ha e
u ilized O dina y Leas Squa e, ixed and andom e ec me hods. To he bes o ou knowledge, he e
a e a limi ed numbe o s udies in he exis ing li e a u e ha u ilize easible gene alized leas squa es
(FGLS) models o examine he impac s o clima e change. FGLS models a e ad an ageous in dealing
wi h issues such as g oup-wise he e oscedas ici y and au oco ela ion wi hin panels o he s ochas ic dis-
u bance e m. These issues could de ia e om he s ic assump ions o adi ional panel models,
po en ially leading o biased es ima ions (Wu e al., 2021). The e o e, applying he model wi h he
s eng hs ou lined abo e o e s a p omising app oach o esol ing exis ing challenges o issues le un e-
sol ed in p e ious s udies in Vie nam. This applica ion is expec ed o yield eliable esul s in assessing
he impac o clima e on ice p oduc ion in he s udy a ea, he eby enhancing he comp ehensi e
unde s anding and e ec i e managemen o clima e- ela ed isks in he egion’s ag icul u al sec o .
The objec i e o his s udy is o employ a easible gene alized leas squa es model o examine how
clima e change, wi h speci ic seasonal cha ac e is ics, a ec s ice p oduc i i y in he Cen al Coas o
Vie nam om 1996 o 2021. This s udy makes wo main con ibu ions o he exis ing li e a u e: (i) his
s udy is he i s o apply he FGLS model o mo e in-dep h seasonal esea ch o clima e change
impac s o e an ex emely ex ended pe iod o ime in he Cen al Vie nam, while add essing bo h
g oup-wise he e oscedas ici y and au oco ela ion o ob ain obus es ima es o he panel analysis; and
(ii) he indings o his s udy p o ide scien i ic e idence o Vie namese policymake s o p opose app o-
p ia e a ming measu es o seasonal clima e ac o s, aimed a adap ing o and educing he ad e se
impac o clima e change on he ice p oduc ion sec o . This scien i ic e idence can also be e e enced
o egions wi h simila clima e cha ac e is ics in o he ice-cul i a ing coun ies in he wo ld.
The emainde o his a icle is o ganized as ollows. Sec ion 2 e iews he li e a u e. Sec ion 3
desc ibes he da a sou ces and he me hodology used. Sec ion 4 ep esen s and discusses he esul s.
Finally, Sec ion 5 concludes he s udy wi h a summa y o he indings and policy implica ions.
2 P.T.M. NGUYEN, P.T. HO, AND H.X. PHAM
2. Li e a u e e iew
Resea che s a e inc easingly di ec ing hei a en ion owa ds he economic consequences o clima e change.
P e ious esea ch has yielded a ying esul s ega ding he an icipa ed esponse o ice o o hcoming cli-
ma e change. Rayamajhee e al. (2021) highligh ed he ulne abili y o u al ag icul u al households in Nepal
o clima e change. Employing he s ochas ic on ie unc ion app oach, hei indings unde sco ed he sig-
ni ican nega i e e ec s on ice p oduc ion esul ing om al e a ions in bo h a e age and ex eme p ecipi a-
ion and empe a u es. Emphasis was placed on he h ea s posed by i egula ex eme ain all pa e ns and
a sus ained ise in a e age empe a u es. Massagony e al. (2023) applied he easible gene alised leas
squa es (FGLS) model o examine he e ec s o clima e change on ice p oduc ion in Indonesia using his o -
ical da a om 1986 o 2016. Thei esul s e ealed ha inc eases in empe a u e and p ecipi a ion ha e
ad e se e ec s on ice p oduc ion, whe eas highe ela i e humidi y has a posi i e impac . Recognizing he
ad e se impac o empe a u e changes on ice p oduc ion, Mukhopadhyay and Das (2023) p oposed a p o-
ac i e solu ion and ound ha he adop ion o new ice seed a ie ies wi h heigh ened empe a u e ole -
ance is mo e e ec i e in mi iga ing he challenges posed by clima e change.
Nume ous s udies ha e examined he impac s o clima e change on ice p oduc ion and yield, span-
ning di e se scopes (Kon gis e al., 2019), including na ional (Felkne e al., 2009; Naha e al., 2018,
Fi daus e al., 2020), egional (Ma hews e al., 1997; Li e al., 2017; an Oo & Zwa , 2018) and global
le els (Olszyk e al., 1999; Lobell & Gou dji, 2012). The p ima y ools o assessing he e ec s o clima e
change on c op yield a e c op simula ion and s a is ical models (Shi e al., 2013). These models in eg a e
scien i ic p inciples om ag onomy, ag ome eo ology, physiology, and soil science o simula e he com-
plex in e ac ions be ween ice c ops and hei en i onmen s (Cha as e al., 2009; Shabbi e al., 2020;
Solaymani, 2023). On he o he hand, s a is ical models o es ima ing he impac o clima e change on
ice yields which in ol e using quan i a i e me hods o analyze his o ical da a (T inh, 2018; Rayamajhee
e al., 2021; Tan e al., 2021). These models employ echniques such as eg ession analysis o o he
machine lea ning algo i hms o iden i y pa e ns and co ela ions be ween clima e a iables and c op
p oduc ion (Elbasi e al., 2023). Unlike mechanis ic c op simula ion models, s a is ical models do no
simula e he unde lying physiological p ocesses bu ocus on empi ical ela ionships wi hin he da a.
They p o ide aluable insigh s in o how clima ic ac o s in luence c op yields, aiding in he assessmen
o po en ial impac s unde changing clima ic condi ions (Lobell & Bu ke, 2010).
Acco ding o Wu e al. (2021), p e ious s udies indica ed ha s a is ical models exhibi s ong
explana o y capabili ies and su pass simula ion models in accu a ely assessing da a a speci ic spa ial
scales. Addi ionally, he esul s gene a ed by c op simula ion models a e sensi i e o ac o s such as soil
condi ions, wea he , and managemen indices (Shi e al., 2013), whe eas s udies lack eliable da a on soil
and managemen , o e ing ‘bes -guess’es ima es wi h limi ed in o ma ion on unce ain ies om model
choices (Schlenke & Lobell, 2010). In addi ion, clima e change has g adually un olded, and i s impac is
no immedia ely appa en . A model mus be applied o e an ex ended pe iod o ime o cap u e and
unde s and hese e ec s. Hence, panel da a analysis enhances he e iciency and consis ency in es ima -
ing pa ame e s, pa icula ly when dealing wi h smalle sample sizes, leading o mo e obus and eliable
esul s (Bal agi, 2008; Hsiao, 2014).
Ne e heless, con en ional panel models, including pooled O dina y Leas Squa es (OLS), ixed e ec s,
and andom e ec s models a e in luenced by s ic assump ions, such as he absence o g oup-wise he -
e oscedas ici y, and au oco ela ion wi hin panels o he s ochas ic dis u bance e m (Wu e al., 2021).
Viola ing hese assump ions can esul in biased and ine icien pa ame e es ima es (Woold idge, 2010).
Thus, i is impo an o apply a model ha o e comes hese limi a ions desc ibed abo e (Wu e al.,
2021). In e na ionally, se e al s udies ha e used he FGLS model o in es iga e he impac s o clima e
change (Ali e al., 2017; Kassaye e al., 2021; Wu e al., 2021; Massagony e al., 2023).
3. Ma e ials and me hods
3.1. S udy a ea
To analyze he impac o clima e change on ice yield in Cen al Coas o Vie nam, we u ilized a panel
da ase o he 9 dis ic -le el uni s in Thua Thien Hue P o ince h oughou 1996 o 2021. We chose o
COGENT ECONOMICS & FINANCE 3
s udy in Thua Thien Hue P o ince because i is one o Vie nam’s mos clima e- ulne able a eas and is
o en a ec ed by na u al disas e s such as d ough , yphoons, and looding (Phuong e al., 2018; UNDP,
2018).
As shown in Figu e 1, Thua Thien Hue P o ince is loca ed in he Cen al Coas egion, co e ing abou
5025 km2:I bo de s Quang T i P o ince o he no h and Da Nang Ci y o he sou h, sha es a 81 km
bounda y wi h Laos o he wes , and has a 120 km coas line o he eas . The p o ince’s e i o y ex ends
in a no hwes -sou heas di ec ion, wi h he longes sec ion eaching 120 km along he coas and he
sho es , 44 km, in he wes . Ho izon ally, i spans a no heas -sou hwes di ec ion, wi h he wides pa
measu ing 65 km and he na owes , in he sou he nmos a ea, app oxima ely 2 o 3 km (TTHPPC, 2024).
3.2. Da a collec ion
This s udy used he en i e seconda y da ase collec ed om a ious issues o he Thua Thien Hue
S a is ical Yea book, which is published annually om 1996 o 2021 by Thua Thien Hue S a is ical O ice
o e lec he local social, economic, and clima ic si ua ions. The da a on ice yield we e collec ed o
bo h he win e –sp ing and summe –au umn seasons om he nine dis ic -le el uni s o he p o ince
ep esen ed h ee ypes o a eas: coas al plains, midlands, and highlands. The mon hly clima ic da a
we e p o ided by he h ee-land based me eo ological s a ions om Cen e o Hyd ome eo ological
Fo ecas ing, loca ed in h ee a eas in Thua Thien Hue P o ince. The mon hly da a on me eo ological a -
iables, such as empe a u e and ela i e humidi y, we e he a e ages o daily empe a u es and daily
ela i e humidi y wi hin each mon h. The mon hly da a on p ecipi a ion we e he o al p ecipi a ion o
all days in he mon h. The daily da a, collec ed using measu emen me hods, echniques, and p ecise
equipmen , a e agg ega ed in o ep esen a i e a e age mon hly da a by me eo ological expe s and o i-
cially published by he Thua Thien Hue S a is ics O ice, ensu ing a ce ain le el o eliabili y in he s udy.
Fu he mo e, he long- e m esea ch pe iod is designed o o e a comp ehensi e iew o clima e
change impac s, so mino e o s, i p esen , a e conside ed accep able.
Besides, minimum and maximum clima ic a iables used in his s udy a e c ucial o unde s anding
he impac o clima e change on ice p oduc ion. Ex emes in wea he condi ions o en con ibu e o
a ia ions in ice p oduc ion and may exe a g ea e in luence on yields (Saud e al., 2022). Addi ionally,
di e en c ops ha e dis inc op imal minimum and maximum clima ic condi ions, such as empe a u e
and ain all (Kuma e al., 2021). The e o e, unde s anding hese impac s is necessa y o es ablishing an
ea ly wa ning and o ecas ing sys em o ex eme wea he e en s (Saud e al., 2022).
Figu e 1. Map o he s udy a ea. Sou ce: Wol e al. (2021).
4 P.T.M. NGUYEN, P.T. HO, AND H.X. PHAM
Table 1. Desc ip i e s a is ics o a iables used in he win e –sp ing season model and summe –au umn season
model.
Va iable De ini ion Mean S d.De Minimum Maximum
Win e –sp ing
RY Rice yield ( on/ha) 5.22 0.92 2.87 7.79
Tmin Minimum empe a u e (C) 19.27 1.46 15.1 21.8
Tmax Maximum empe a u e (C) 27.67 1.44 23.1 29.6
P min Minimum p ecipi a ion (mm) 30.62 29.11 1.6 161.1
P max Maximum p ecipi a ion (mm) 428.56 248.67 24.4 1218.8
Hmin Minimum ela i e humidi y (%) 82.37 3.20 75 89
Hmax Maximum ela i e humidi y (%) 93.51 1.99 87 98
Summe –au umn
RY Rice yield ( on/ha) 4.81 0.98 2.03 6.49
Tmin Minimum empe a u e (C) 26.90 1.58 20.2 29.4
Tmax Maximum empe a u e (C) 28.84 1.28 24.7 31.1
P min Minimum p ecipi a ion (mm) 54.18 48.50 1.7 217.8
P max Maximum p ecipi a ion (mm) 257.37 136.14 52.6 650
Hmin Minimum ela i e humidi y (%) 77.05 3.04 71 84
Hmax Maximum ela i e humidi y (%) 83.93 2.81 76 91
Sou ce: Au ho s’es ima ion esul s.
Figu e 2. The a ia ions in clima ic ac o s a ec ing ice yield om 1996 o 2021 in Thua Thien Hue P o ince. Sou ce:
Au ho s’calcula ion esul s.
COGENT ECONOMICS & FINANCE 5
The da ase was collec ed annually in acco dance wi h he summe –au umn and win e –sp ing sea-
sons and was compiled o e he comple e a 26-yea pe iod. The da a o he win e –sp ing model we e
ob ained o e a 6-mon h pe iod, spanning om Decembe o May o he subsequen yea . The da a o
he summe –au umn model we e acqui ed o e a 4-mon h pe iod, anging om May o Augus . The
o al da ase was comple e, and no missing da a we e impu ed in his s udy. Finally, a panel da ase o
234 obse a ions o each season model was used in his s udy, as shown in Table 1.
As demons a ed in Table 1, he minimum and maximum ela i e humidi y indices in he win e –
sp ing season a e 82.37 and 93.51, espec i ely, su passing hose in he summe –au umn season, which
s and a 77.05 and 83.93, espec i ely. While he minimum p ecipi a ion in he summe –au umn season
(54.18 mm) exceeded ha in he win e –sp ing season (30.62 mm), he maximum p ecipi a ion in he
win e –sp ing season (428.56 mm) su passed he minimum p ecipi a ion in he summe –au umn season
(257.37 mm). Fu he mo e, he minimum and maximum empe a u e indices in he win e –sp ing season
a e 19.27 C and 27.67 C, espec i ely, bo h lowe han hose in he summe –au umn season, 26.9 C
and 28.84 C, espec i ely.
Figu e 2 illus a es he ends o clima ic ac o s om 1996 o 2021 in he Cen al Coas o Vie nam,
wi h he do ed lines indica ing he ends o hese ac o s. As shown in Figu e 1, he e we e upwa d
ends in he maximum empe a u e in bo h seasons. O e he 26-yea pe iod, he maximum empe a-
u e inc eased by 1.8 C in he summe –au umn season and 1.9 C in he win e –sp ing season. Du ing
he summe –au umn season, declines we e obse ed in bo h maximum p ecipi a ion and maximum ela-
i e humidi y, whe eas in he win e –sp ing season, bo h clima e indica o s showed inc easing ends.
The minimum empe a u e, p ecipi a ion, and ela i e humidi y exhibi ed luc ua ions ac oss mos sea-
sons, excep o he upwa d end o minimum empe a u e in he summe –au umn season by 3.4 C.
Rega ding p ecipi a ion, he maximum p ecipi a ion index wi h he g ea es luc ua ions was obse ed in
2005–2010 in he summe –au umn season, and in 2015–2020 in he win e –sp ing season. In ac , du ing
he a o emen ioned pe iods, he p o ince expe ienced ex eme wea he e en s, especially in 2009/2010
and 2016/2017, wi h unusually high ain all o up o 1176.3 mm (2017), leading o his o ic loods ha
g ea ly a ec ed ice p oduc ion and he li elihoods o a ming households.
3.3. Model speci ica ion
Based on he li e a u e e iew, he a iables we e used in he models including empe a u e, p ecipi a-
ion, and humidi y (Ali e al., 2017; Kuma e al., 2021; Massagony e al., 2023; Tan e al., 2021; Li, 2023).
The gene al o m e lec ing he ela ionship be ween ice yield and clima ic ac o s in his s udy is
w i en as:
RYi ¼ Tmini ,Tmaxi ,P mini ,P maxi ,Hmini ,Hmaxi
ðÞ
(1)
whe e RY deno es he dependen a iable, ice yield, in Dis ic ia ime ;Tmin deno es minimum em-
pe a u e; Tmax deno es maximum empe a u e; P min deno es minimum p ecipi a ion; P max deno es
maximum p ecipi a ion, Hmin deno es minimum ela i e humidi y; Hmax deno es maximum ela i e
humidi y. i ep esen s he dis ic and ep esen s ime. Eq. (1) is ans o med in o an econome ic
model in he loga i hmic o m deno ed by Eq. (2), as:
lnRYi ¼b0þb1lnTmini þb2lnTmaxi þb3lnP mini þb4lnP maxi
þb5lnHmini þb6lnHmaxi þli (2)
whe e b0 ep esen s he cons an e m; while b1,b2,b3,b4,b5, and b6s and o he coe i-
cien s associa ed wi h explana o y a iables; and li signi ies he e o e m.
3.4. Econome ic me hods
Fo empi ical analysis using panel da a, wo commonly used models a e he ixed e ec s model (FE) and an-
dom e ec s model (RE). Howe e , i is necessa y o conduc a obus ness check o hese models o alida e
he esul s de i ed om he app op ia e model (Wu e al., 2021). The indings show ha he esul s de i ed
om hese wo adi ional models canno add ess he issues o g oup-wise he e oscedas ici y o
6 P.T.M. NGUYEN, P.T. HO, AND H.X. PHAM
au oco ela ion wi hin panels. Acco ding o Woold idge (2010), e ec i ely add essing ex ensi e da ase s and
challenges ela ed o he e oscedas ici y and au oco ela ion can be achie ed by employing he Feasible
Gene alized Leas Squa es (FGLS) me hod. Conside able ocus has been di ec ed owa ds FGLS in ecen
yea s, wi h se e al s udies employing his me hod o examine he impac o clima e change on ag icul u al
ou pu (Reed & Ye, 2011;Alie al.,2017; Kuma e al., 2021; Massagony e al., 2023).
Speci ically, FGLS is pa icula ly ad an ageous, p oducing e icien and consis en es ima es o s and-
a d e o s p o ided ha he panel ime dimension (T) is g ea e han he c oss-sec ional dimension (N)
(Beck & Ka z, 1995; Na een e al., 2021). This condi ion is sa is ied when N<T, whe e N ep esen s he
numbe o c oss-sec ional uni s (dis ic s in ou case) and Tis he ime pe iod. In ou s udy wi h nine dis-
ic s, he c oss-sec ional dimension (N) was less han he ime pe iod (T¼26), con i ming he easibili y
o he FGLS me hod.
The e o e, we u ilized he easible gene alized leas squa e (FGLS) me hod in his s udy. Following
Kuma e al. (2021), he gene al model sugges ed by Pa ks (1967) can be exp essed as ollows:
^
bFGLS ¼X0^
X
−1X
−1X0^
X
−1y(3)
Va
^
bFGLS
¼X0^
X
−1X
−1(4)
whe e
^
Xis he assump ion o au oco ela ion and he e oscedas ici y,
^
bFGLS deno es he FGLS es ima o
o b,ydeno es he dependen a iables, Xdeno es he ec o o independen a iables, and X0deno es
he anspose o X.
3.5. Diagnos ic es s
Conduc ing diagnos ic es s is c ucial o ensu e he obus ness o he model. The p ima y es s con-
duc ed in his s udy we e as ollows:
To de e mine whe he o employ ixed o andom e ec s models, esea che s equen ly u ilize he
Hausman es p oposed by Hausman in 1978. None heless, i is impo an o ecognize ha he
Hausman es may no always o e a conclusi e decision, as i s alidi y is con ingen upon s ingen
condi ions (Buckley e al., 2013). The e o e, we employed a Hausman-like al e na i e es , known as x o-
e id, which p esen s he Sa gan–Hansen es o o e iden i ying es ic ions o a panel da a es ima ion
s a is ic (Scha e & S illman, 2016). The null hypo hesis s a es ha he e is no sys ema ic di e ence in
he coe icien s.
To ensu e he obus ness o he applied panel eg ession, he Woold idge’s(2010) es was employed
o de ec au oco ela ion. This is necessa y because au oco ela ion can esul in biased s anda d e o s
and a educ ion in he e iciency o he pa ame e es ima es (Hamil on, 1994). The null hypo hesis o he
Woold idge es is ha he e is no i s -o de au oco ela ion.
The Modi ied Wald es , as p oposed by Baum (2000), is a s a is ical es used o assess he p esence
o panel g oupwise he e oscedas ici y in he ixed-e ec eg ession model. The null hypo hesis indica es
panel g oup-wise homoscedas ici y and he al e na i e hypo hesis signi ies he exis ence o g oup-wise
he e oscedas ici y wi hin he model.
In ligh o he a o emen ioned alida ion, he easible gene alized leas squa es (FGLS) model was
employed o add ess he au oco ela ion and g oupwise he e oscedas ici y issues o he models.
4. Resul s and discussions
4.1. Co ela ion analysis
Tables 2 and 3show he pai wise co ela ion ma ices be ween a iables used in he models o he
summe –au umn and win e –sp ing seasons, espec i ely. The p ima y me hod o de ec ing mul icolli-
nea i y is h ough a pai wise co ela ion analysis using a co ela ion ma ix. The esul s highligh he
nonexis ence o a high co ela ion be ween he a iables, e ec i ely mi iga ing conce ns ega ding mul-
icollinea i y wi hin he da ase . Commonly accep ed h esholds, such as 0.8 and 0.9, a e employed o
iden i y signi ican bi a ia e co ela ions, as hey indica e s ong linea associa ions o a high deg ee o
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