Wang, Zu; Wu, Zhihao; Wei, Longbao
A icle
E ec s o iscal suppo o ag icul u e on g ain p oduc ion
echnical e iciency: Empi ical e idence om Chinese
a ms
Jou nal o Applied Economics
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Effec s o fiscal suppo o ag icul u e on g ain
p oduc ion echnical efficiency: empi ical e idence om
Chinese a ms
Zu Wang, Zhihao Wu & Longbao Wei
To ci e his a icle: Zu Wang, Zhihao Wu & Longbao Wei (2024) Effec s o fiscal suppo o
ag icul u e on g ain p oduc ion echnical efficiency: empi ical e idence om Chinese a ms,
Jou nal o Applied Economics, 27:1, 2347687, DOI: 10.1080/15140326.2024.2347687
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RESEARCH ARTICLE
E ec s o iscal suppo o ag icul u e on g ain p oduc ion
echnical e iciency: empi ical e idence om Chinese a ms
Zu Wang
a,b
, Zhihao Wu
a,b
and Longbao Wei
a,b
a
China Academy o Ru al De elopmen , Zhejiang Uni e si y, Hangzhou, China;
b
School o Public A ai s,
Zhejiang Uni e si y, Hangzhou, China
ABSTRACT
This s udy u ilizes mic o-le el a m panel da a om 2007 o 2012 o
measu e he echnical e iciency o g ain p oduc ion among a ms
using a s ochas ic on ie analysis model. Addi ionally, i employs a
wo-way ixed e ec s model o empi ically in es iga e he impac o
iscal suppo o ag icul u e on g ain p oduc ion echnical e i-
ciency and i s unde lying mechanisms. The esul s e eal signi ican
oom o imp o emen in he echnical e iciency o g ain p oduc-
ion among Chinese a ms, wi h inc eased iscal suppo o ag i-
cul u e demons a ing a subs an ial enhancemen o hei
e iciency. This p omo ing e ec only exis s in he majo g ain-
p oducing a eas and inc eases wi h he inc ease o a m size. The
augmen a ion o iscal suppo o ag icul u e achie es his goal by
augmen ing mode n ag icul u al inpu ac o s, alle ia ing inancing
cons ain s aced by a ms, and op imizing ag icul u al p oduc ion
in as uc u e. Ou indings p o ide guidance o op imizing iscal
policy o suppo ag icul u e, p omo ing ag icul u al mode niza-
ion, and achie ing ood secu i y.
ARTICLE HISTORY
Recei ed 7 Augus 2023
Accep ed 22 Ap il 2024
KEYWORDS
China; iscal suppo o
ag icul u e; g ain
p oduc ion; echnical
e iciency
1. In oduc ion
Food secu i y is a c i ical issue wi h signi ican implica ions o human su i al. The
Uni ed Na ions (UN) has iden i ied “e adica ing hunge and achie ing ood secu i y” as
one o he se en een Sus ainable De elopmen Goals (SDGs). Ensu ing consis en and
s able g ow h in g ain yield is o signi icance o he economic de elopmen and social
s abili y o de eloping coun ies. China is he la ges de eloping coun y and one o he
majo ag icul u al coun ies, s udying China’s ood secu i y issues holds aluable insigh s
o o he de eloping coun ies wo ldwide. Nowadays China’s ood secu i y is con on ed
wi h se e al challenges including he ising demands o g ain and ood consump ion,
he non-ag icul u al ans e o c ucial inpu ac o s such as labou and land, and he
moun ing cons ain s imposed by limi ed esou ces and en i onmen al conce ns.
The e o e, ensu ing ood secu i y p ima ily hinges upon enhancing he g ain p oduc ion
e iciency, a he han inc easing inpu ac o s o g ain p oduc ion (Zhang e al., 2021).
CONTACT Zhihao Wu [email p o ec ed] China Academy o Ru al De elopmen , Zhejiang Uni e si y, 866
Yuhang ang Road, Hangzhou, Zhejiang 310058, China
JOURNAL OF APPLIED ECONOMICS
2024, VOL. 27, NO. 1, 2347687
h ps://doi.o g/10.1080/15140326.2024.2347687
© 2024 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-NonComme cial License (h p://
c ea i ecommons.o g/licenses/by-nc/4.0/), which pe mi s un es ic ed non-comme cial 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 .
The e exis s an ex ensi e body o li e a u e ha has quan i a i ely assessed g ain p oduc-
ion echnical e iciency a mac o le els, including p o incial and coun y scales (Odeck,
2007; Zheng e al., 2023), and some o he li e a u e used small-scale mic o-le el da a o
empi ical analysis (Gong e al., 2019; Z. Liu & Zhuang, 2000). Howe e , he e is a lack o
esea ch based on na ional da a on he echnical e iciency o g ain p oduc ion on a ms,
which a e he main body o g ain p oduc ion. Cha i e al. (2021) s udied he impac o
land p ope y e o m on a me p oduc i i y by using he na ional ixed poin (NFP)
su ey da a a he a me le el. Al hough hey used da a on a me s a he na ional le el,
hey did no examine he impac o iscal suppo o ag icul u e on echnical e iciency.
Compa ed o hei s udy, he measu emen o echnical e iciency in his s udy is u he
accu a e o he a me -c op le el. On he o he hand, conside ing he public a ibu es
and ex e nali ies associa ed wi h ag icul u e and i s low p o i abili y as a ulne able
indus y, go e nmen iscal suppo plays a pi o al ole in ag icul u al de elopmen .
Ag icul u al p o ec ion and subsidies a e p e alen p ac ices globally (Bye lee & Sain,
1986; Mendelsohn, 2003). In China, he go e nmen places g ea impo ance on iscal
suppo and subsidies o ag icul u e. F om 2016 o 2019, China’s gene al public budge
expendi u e alloca ed a cumula i e expendi u e o 6.07 illion yuan o ag icul u al and
u al pu poses, wi h an a e age annual g ow h a e o 8.8%, su passing he a e age
g ow h a e o he gene al public budge expendi u e.
1
The go e nmen aims o imp o e
g ain p oduc ion echnical e iciency by os e ing he en husiasm o a me s h ough
inc easing iscal suppo o ag icul u e, so as o ensu e a s able g ain supply and enhance
he in e na ional compe i i eness o g ain p oduc s. The e o e, examining g ain p oduc-
ion echnical e iciency a he a m le el and in es iga ing he impac and mechanism o
iscal suppo o ag icul u e on China’s g ain p oduc ion echnical e iciency hold
signi ican heo e ical and policy implica ions o ensu ing ood secu i y and p omo ing
sus ainable ag icul u al de elopmen in China and o he de eloping coun ies.
Resea ch on he economic impac s o iscal suppo o ag icul u e p ima ily ocused
on aspec s such as a me income (Gao e al., 2013), ag icul u al economic g ow h (S ads
& Bein ema, 2015), and a me consump ion (De con e al., 2009; Fan e al., 2000).
Exis ing s udies ha e ound ha iscal suppo o ag icul u e can e ec i ely imp o e he
ag icul u al p oduc ion en i onmen and condi ions, enhance ag icul u al p oduc ion
capaci y, and consequen ly p omo e income g ow h o a me s (Gao e al., 2013). Fiscal
suppo o ag icul u e plays a signi ican ole in s imula ing ag icul u al economic
g ow h in low- and middle-income coun ies (Idoko & Ja o, 2018). Fiscal suppo o
ag icul u e ele a es he consump ion le el o a me s, al hough he e exis empo al and
spa ial he e ogenei ies (Blanca d e al., 2006). On he o he hand, ex ensi e esea ch
examined he ac o s in luencing g ain p oduc ion echnical e iciency om bo h mic o
and mac o pe spec i es. A he mic o le el, ac o s such as land inpu (Wu e al., 2005),
e ilize inpu (Lie e e al., 2003), machine y inpu (Monchuk e al., 2010; Wu e al.,
2021), e ec i e i iga ion a ea (Hassan e al., 2000), labou inpu , and cha ac e is ics o
a me s (Li & Sicula , 2013; Tian & Wan, 2000) ha e been ound o a ec g ain p oduc-
ion echnical e iciency. A he mac o le el, exis ing esea ch mainly ocused on egional
economic de elopmen le els (Kuang e al., 2021), en i onmen al pollu ion (Hoang &
1
In o ma ion sou ce: Po al o he Cen al People’s Go e nmen o he People’s Republic o China (h ps://www.go .cn/
xinwen/2020–12/23/con en _5572857.h m).
2Z. WANG ET AL.
Coelli, 2011; Li e al., 2022), clima e condi ions (Chen & Gong, 2021), echnological
changes (Jin e al., 2002, 2010), and ag icul u al in as uc u e (Chen & Ding, 2007;
Te uel & Ku oda, 2005) as ac o s in luencing g ain p oduc ion e iciency. Fo ins ance,
S. Chen and Gong (2021) u ilized 35 yea s o coun y-le el panel da a in China o assess
he impac o global wa ming on China’s ag icul u al o al ac o p oduc i i y (TFP) and
ound ha ex emely high empe a u es educe China’s ag icul u al TFP, while clima e
adap a ion can mi iga e his nega i e impac . Al hough some s udies ha e a emp ed o
es ablish a linkage be ween iscal suppo o ag icul u e and mac o-le el g ain p oduc-
ion e iciency (Zhang e al., 2021), hey ha e o e looked he in es iga ion o mic o-le el
a ms’ g ain p oduc ion echnical e iciency and he explo a ion o unde lying
mechanisms.
Based on he li e a u e abo e, despi e a conside able numbe o empi ical s udies ha
ha e ex ensi ely examined iscal suppo o ag icul u e and g ain p oduc ion echnical
e iciency, he e a e se e al sho comings in he exis ing esea ch. Fi s ly, p e ious
s udies on g ain p oduc ion echnical e iciency p ima ily ocused on mac o-le el mea-
su emen s a he p o incial and coun y le els, lacking in es iga ion o g ain p oduc ion
echnology e iciency a he a m le el o a na ional sample. Secondly, he examina ions o
he e ec s o iscal suppo o ag icul u e in exis ing esea ch a e s ill no comp ehen-
si e, mainly concen a ing on a me income, consump ion, and ag icul u al economic
g ow h. The e is a lack o esea ch e alua ing he economic consequences o iscal
suppo o ag icul u e om he pe spec i e o g ain p oduc ion echnical e iciency.
Thi dly, al hough some s udies explo ed he ela ionship be ween iscal suppo o
ag icul u e and g ain p oduc ion echnical e iciency, he e is s ill a lack o esea ch
ha empi ically examines he mechanisms, such as inpu ac o s, inancing cons ain s,
and in as uc u e, h ough which iscal suppo o ag icul u e a ec s g ain p oduc ion
echnical e iciency.
This s udy p esen s an empi ical analysis o he e ec o iscal suppo o ag icul u e
on a m-le el g ain p oduc ion echnical e iciency in China. By cons uc ing a panel
da ase a he a m le el om 2007 o 2012, and employing he s ochas ic on ie analysis
model, we measu e he Chinese a m’s g ain p oduc ion echnical e iciency.
Fu he mo e, we in es iga e he e ec s and mechanisms h ough which iscal suppo
o ag icul u e a ec s a m-le el g ain p oduc ion echnical e iciency by u ilizing a wo-
way ixed e ec s model. This s udy makes se e al signi ican con ibu ions o he
li e a u e. Fi s ly, his s udy compu es g ain p oduc ion echnical e iciency using
mic o-le el da a, allowing us o examine he e ec o ci y-le el iscal suppo o
ag icul u e on a m-le el g ain p oduc ion echnical e iciency, hus add essing he
limi a ions o exis ing mac o-le el s udies. Secondly, his s udy en iches and expands
he empi ical esea ch on he impac s o iscal suppo o ag icul u e, p o iding aluable
insigh s o a comp ehensi e assessmen o he policy design o iscal suppo o
ag icul u e. These indings bea s ong policy implica ions o go e nmen e o s in
p omo ing ag icul u al mode niza ion and sus ainable g ow h in g ain p oduc ion.
Las ly, his s udy elucida es he mechanisms h ough which iscal suppo o ag icul u e
a ec s g ain p oduc ion echnical e iciency, hus con ibu ing o he exis ing li e a u e
on he impac o public policies on g ain p oduc ion e iciency and o e ing aluable
e idence om a de eloping coun y con ex ega ding he ela ionship be ween iscal
suppo o ag icul u e and g ain p oduc ion echnical e iciency.
JOURNAL OF APPLIED ECONOMICS 3
The emainde o he a icle is o ganized as ollows. Sec ion 2 p esen s he concep ual
amewo k, ou lining he heo e ical ounda ion o his s udy. Sec ion 3 desc ibes he
empi ical s a egy and econome ic model. Sec ion 4 p o ides a comp ehensi e desc ip-
ion o he da a and a iables in his s udy. Sec ion 5 p esen s he esul s o baseline
es ima ions, obus ness checks, he e ogenei y analyses, mechanism es s, and ou discus-
sion. Sec ion 6 is ou conclusion.
2. Concep ual amewo k
Ag icul u al p oduc ion se es as he ounda ion o a na ion’s sus enance and de elop-
men (Ellis, 2008). Ne e heless, ag icul u e is inhe en ly cha ac e ized by ex e nali ies
and dual isk a ibu es, making i a sec o wi h weakened compe i i eness (Rea don e
al., 1994). Smi h (1776) a gued ha ag icul u e canno unc ion unde a s ic ly specia-
lized sys em. Nu kse (1952) iden i ied he exis ence o a po e y-d i en cycle in unde -
de eloped egions. The Lewis dualis ic economic model p esupposes ag icul u e as a low-
quali y sec o (Lewis, 1954). Schul z (1964), in con as o p io schola s, p oposed ha
mode n ag icul u e could p omo e economic g ow h, con ingen upon he mode niza-
ion o he adi ional “poo and ine icien ” ag icul u al sec o . Howe e , he p o i -
d i en dynamics o ma ke s sugges ha he ma ke mechanism alone canno acili a e
he ansi ion om adi ional o mode n ag icul u e (Da id e al., 2000; Timme , 1995).
The e o e, inc easing go e nmen egula ion in he ag icul u al sec o o compensa e o
he inhe en de iciencies o he ma ke mechanism has become an impo an a ea o
s udy in mode n public iscal heo y (Kelly e al., 2003; Rubens ein e al., 2003). Al hough
ag icul u al de elopmen can b ing subs an ial social bene i s, due o he non- i al and
non-excludable cha ac e is ics o he public goods i p o ides, he p i a e sec o will lack
he incen i e o in es in i (Fan & Zhang, 2008). Consequen ly, go e nmen suppo
h ough iscal measu es in ag icul u e has eme ged as a c i ical app oach o ensu ing
na ional ood secu i y, pa icula ly in he ace o inc easing ood demand and esou ce
and en i onmen al cons ain s (Fan e al., 2021; Qaim, 2020). The a o emen ioned
heo ies o public iscal and ag icul u al ex e nali ies p o ide heo e ical suppo o
iscal policies aimed a imp o ing ag icul u al p oduc i i y and ein o cing in e na ional
ood compe i i eness.
Fiscal suppo o ag icul u e can a ec he g ain p oduc ion echnical e iciency
h ough a ious channels. To s a wi h, i con ibu es o enhancing mode n inpu ac o s
u ilized in g ain p oduc ion. Agains he backd op o indus ializa ion and u baniza ion,
due o he highe e u ns o non-ag icul u al indus ies, he e has been an o e all ou low
o ag icul u al labo . The subs i u ion o mode n inpu ac o s ep esen ed by ag icul-
u al machine y o adi ional ac o s has become an ine i able end in ag icul u al
p oduc ion (Huang e al., 2012). The p ocess o ag icul u al de elopmen in China since
he e o m and opening up also e lec s he abo e-men ioned pa e n (Gong, 2018a).
Fi s ly, iscal suppo o ag icul u e can alle ia e p oblems such as insu icien ac o
in es men due o a me s’ inancial cons ain s and isk expec a ions (Ka lan e al.,
2014), hus e ec i ely augmen ing he mode n inpu ac o s in ag icul u al p oduc ion
p ocesses. Fo ins ance, subsidies o he pu chase o ag icul u al machine y can guide
a me s in engaging specialized se ice p o ide s o deli e mechanized ope a ions
essen ial o he en i e g ain p oduc ion p ocess, enabling a me s o e icien ly
4Z. WANG ET AL.
accomplish hei asks (Laba he & Lau en , 2013; Ma e al., 2018). Secondly, iscal
expendi u es, se ing as in es men s in echnological esea ch and de elopmen , can
s imula e echnological ad ancemen s, he eby enhancing he quali y o mode n ag i-
cul u al inpu ac o s like machine y and e ilize s (McA hu & McCo d, 2017).
Thi dly, iscal suppo o ag icul u e can al e he ela i e p ices o ag icul u al inpu
ac o s h ough a ious subsidy me hods, u he enhancing he alloca ion a io o
mode n ag icul u al inpu ac o s (Adamopoulos & Res uccia, 2014; Dea on & Dea on,
2020), he eby op imizing he combina ion among machine y, land, labou , and o he
di e en ac o s. P e ious s udies sugges ha mode n inpu ac o s such as ag icul u al
mechaniza ion can imp o e he e iciency o esou ce alloca ion, educe he a e age g ain
p oduc ion cos s, enhance specialized p oduc ion p ac ices, ul ima ely os e ing
imp o emen s in bo h echnological le els and p oduc i i y in g ain p oduc ion (Ma e
al., 2018; Wang e al., 2020). Consequen ly, iscal suppo o ag icul u e can acili a e
imp o ed g ain p oduc ion echnical e iciency by ele a ing mode n ag icul u al inpu
ac o s.
Fu he mo e, iscal suppo o ag icul u e con ibu es o inc easing household
income le els and acili a es asse accumula ion among a me s, he eby educing hei
inancial cons ain s and imp o ing accessibili y o loans (Islam & Luo, 2018; Kassou i &
Kacou, 2022). Due o he ulne abili y o ag icul u al p oduc ion and ag icul u al
ope a o s hemsel es, he p o i -seeking na u e o inancial ins i u ions, and he imbal-
ance in he alloca ion o unds be ween u ban and u al a eas, ag icul u e aces se ious
inancing cons ain s, hinde ing ag icul u al de elopmen and ans o ma ion
(Gui kinge & Bouche , 2008). Ag icul u al c edi subsidies wi hin iscal suppo o
ag icul u e p o ide inancial in o ma ion o a ious s ages such as p oduc ion, p ocu e-
men , wa ehousing, and anspo a ion o g ains. By o e ing imely and e ec i e inan-
cing channels o g ain p oduce s, ag icul u al c edi subsidies can educe he inancing
cos s associa ed wi h g ain p oduc ion. Nume ous empi ical s udies indica e ha ag i-
cul u al c edi plays a acili a ing ole in enhancing he g ain p oduc ion echnical
e iciency (Ali e al., 2014; Balana e al., 2022; Z. Liu & Zhuang, 2000). Fo ins ance,
Liu and Zhuang (2000) explained he impo ance o u al c edi o a me s’ echnical
e iciency by elaxing liquidi y cons ain s. Consequen ly, iscal suppo o ag icul u e
can enhance he g ain p oduc ion echnical e iciency by alle ia ing c edi cons ain s.
Finally, as a undamen al ool o na ional egula ion o ag icul u al p oduc ion, iscal
suppo o ag icul u e can e ec i ely add ess he issue o inadequa e supply o public
goods in he ag icul u al p oduc ion p ocess (Hazell & Va angis, 2020). Compa ed o he
needs o ag icul u al and u al mode niza ion, he e is insu icien in es men in ag i-
cul u al in as uc u e, and issues o misalloca ion exis . The incidence o ag icul u al
wa e and d ough disas e s emains ela i ely high (Pins up-Ande sen & Shimokawa,
2006). Fiscal suppo o ag icul u e implies he low o inancial and iscal esou ces
om u ban and non-ag icul u al sec o s owa ds ag icul u e sec o . One c ucial di ec-
ion o his suppo in ol es ampli ying in es men s in ag icul u al in as uc u e such as
a mland and i iga ion. This se es o enhance he ag icul u al p oduc ion en i onmen
and ele a e he le el o ag icul u al in as uc u e cons uc ion (Zhong e al., 2019).
Ag icul u al in as uc u e has bo h scale expansion e ec s, inc easing he quan i y and
quali y o a able land, and echnological p og ess e ec s, op imizing he inpu s uc u e
o g ain p oduc ion o achie e highe c op yields wi h ewe expensi e inpu s (Finge e
JOURNAL OF APPLIED ECONOMICS 5
al., 2019; Pan e al., 2021). In as uc u e plays a c ucial ole in economic g ow h, and he
ag icul u al sec o , which hea ily elies on ex e nal inancial suppo , pa icula ly bene-
i s om ag icul u al in as uc u e cons uc ion as a p ecu so o de elopmen
(Donaldson, 2018; Shamdasani, 2021). Se e al empi ical s udies sugges ha imp o ing
ag icul u al in as uc u e cons uc ion e ec i ely lowe s ag icul u al p oduc ion cos s,
p omo es an inc ease in g ain p oduc ion e iciency, subsequen ly aising g ain yield and
compe i i eness (Te uel & Ku oda, 2005; Yuan e al., 2021). The e o e, iscal suppo o
ag icul u e can enhance g ain p oduc ion echnical e iciency by ele a ing he le el o
ag icul u al in as uc u e (as shown in Figu e 1).
In summa y, iscal suppo o ag icul u e is c ucial o ag icul u al de elopmen .
Based on he concep ual amewo k abo e, iscal suppo o ag icul u e migh enhance
inpu ac o s, alle ia e inancial cons ain s, and s eng hen in as uc u e, he eby os-
e ing an imp o emen in g ain p oduc ion echnical e iciency. Howe e , whe he iscal
suppo o ag icul u e ine i ably enhances g ain p oduc ion echnical e iciency, and
whe he he co esponding inc ease in ag icul u al iscal expendi u e ine i ably p o-
mo es he imp o emen o g ain p oduc ion echnical e iciency h ough he enhance-
men o hese h ee channels, equi es u he empi ical e i ica ion h ough quan i a i e
analy ical ools.
3. Empi ical s a egy and econome ic model
3.1. S ochas ic on ie analysis
S ochas ic on ie analysis (SFA) and da a en elopmen analysis (DEA), a e he mos
popula s a is ical ools in he line o e iciency analysis (Coelli & Rao, 2005; Ru an,
2002). Some s udies (Rezi is, 2010; Suha iyan o & Thi le, 2008) ha e applied he DEA o
es ima e ag icul u al e iciency. Howe e , some schola s (Headey e al., 2010; Nin e al.,
Fiscal
suppo o
ag icul u e
Alle ia e insu icien mode n ac o s
Enhance mode n ac o s quali y
Imp o e ac o s alloca ion a io
Realize asse accumula ion
Imp o e loan accessibili y
Financial low o public goods
In es a mland and i iga ion
G ain
p oduc ion
echnical
e iciency
Inc ease
mode n inpu
ac o s
Ease
inancing
cons ain s
Imp o e
in as uc u e
Figu e 1. Concep ual amewo k. No es: Concep ual amewo k o he impac o iscal suppo o
ag icul u e on g ain p oduc ion echnical e iciency. Sou ce: P oduced by he au ho s.
6Z. WANG ET AL.
2003) also claimed ha DEA-based e iciency measu es always d aw anomalous esul s as
compa ed wi h hose om o he measu es o ag icul u al de elopmen , since DEA
canno dis inguish e iciency om whi e noise and measu emen e o . Yuan e al.
(2021) s a ed ha whi e noise and measu emen e o a e signi ican challenges and
p oblems in ag icul u al e iciency analysis. Ne e heless, nei he o he wo issues can be
ully add essed by DEA. Compa ed wi h DEA, SFA imposes assump ions o pa ame ic
unc ional o ms and can ca y ou s a is ical es s on he esul s (Nguyen e al., 2016),
which means ha SFA is able o deal wi h bo h p oblems and ule hem ou . Headey e al.
(2010) also poin ed ou ha SFA es ima ions a e signi ican ly mo e s able and plausible
han hose de i ed by DEA. In summa y, SFA model is mo e sui able o his s udy o
es ima e g ain p oduc ion echnical e iciency.
The o iginal SFA model was ini ially p oposed by Aigne e al. (1977) and Meeusen
and an Den B oeck (1977), hen de eloped by Schmid and Sickles (1984) unde a panel
da a se ing:
Whe e yi means he ou pu alue o a m i a ime in loga i hms; and xki measu es
he k h inpu o a m i a ime in loga i hms; Xi is he ec o o all K ypes o inpu s,
including Labou (numbe o days o labou equi ed), Fe ilize ( o al amoun o pu e
e ilize used), Machine y (cos o mechanical ope a ion), and O he in e media e inpu
(includes managemen cos , inancial cos , insu ance cos , seed cos , pes icide cos , and
i iga ion cos ).ui is he non-nega i e andom e m ha indica es he echnical
ine iciency; i is he ypical dis u bance; αi¼αui. Xi
ð Þ is he p oduc ion unc ion,
which desc ibes he p oduc ion on ie .
The T anscenden al Loga i hmic (T-L) p oduc ion unc ion and he Cobb Douglas
(C-D) p oduc ion unc ion a e wo widely used p oduc ion unc ions. Hence, i is
impo an o adop scien i ic and igo ous es s o decide whe he o choose he T-L
p oduc ion unc ion o he C-D p oduc ion unc ion as he p oduc ion unc ion o his
pape . Wallach and Go ine (1989) poin ed ou ha he mean squa ed e o (MSE) is a
easonable c i e ion o model quali y and can be used o de e mine model selec ion. The
MSE is de ined by: MSE ¼1
nPn
i¼1ðyib
yiÞ2, whe e yi is he obse ed alues o he
a iable, wi h b
yi being he p edic ed alues. Simila o he MSE, he e a e also he mean
absolu e e o (MAE ¼1
nPn
i¼1yib
yi
ð Þj j) and he mean absolu e pe cen age e o
(MAPE ¼1
nPn
i¼1
yibyi
�
yi
��������). The MSE, MAE and MAPE can be used o e lec he e o s
be ween he obse ed alues and he p edic ed alues o he model, and hen e lec he
accu acy o he model. Many s udies ha e used hese indica o s o conside he selec ion
o models (De My enae e e al., 2016; Köksoy, 2006; Willmo & Ma suu a, 2005).
The e o e, his pape also adop s he MSE, MAE and MAPE o de e mine he p oduc ion
unc ion. By calcula ing and compa ing hese h ee indica o s o he wo unc ions’
es ima ion esul s, we ind ha he C-D p oduc ion unc ion has less e o s and is
mo e sui able o he da a in his pape (See Table A1 in Appendix).
Equa ion (1) shows he SFA model wi h he ime-in a ian echnical ine iciency.
Howe e , i is no app op ia e o assume ha echnical ine iciency is ime-in a ian
when analysing he panel da a wi h a long- ime span. Hence, many schola s ha e
JOURNAL OF APPLIED ECONOMICS 7
a io is signi ican ly posi i e. A e g adually adding con ol a iables and ixed e ec s,
he coe icien emains o be signi ican ly posi i e. This indica es ha he impac o iscal
suppo o ag icul u e on echnical e iciency is obus .
We p e e he coe icien in column (7) because he eg ession esul in column (7)
has he mos comp ehensi e con ol a iables. Speci ically, a 1% inc ease in he iscal
suppo a io esul s in a 0.2573% inc ease in g ain p oduc ion echnical e iciency. In
ou s udy sample, he mean g ain p oduc ion echnical e iciency is 0.545, sugges ing
ha a 10% inc ease in he iscal suppo a io leads o a 0.014 inc ease in g ain
p oduc ion echnical e iciency. Ou indings a e consis en wi h B. Gong’s (2018a)
es ima es based on p o incial-le el da a, whe e he sugges s ha a 1% inc ease in
ag icul u al public expendi u e could lead o a 0.006% imp o emen in ag icul u al
p oduc i i y. Mo eo e , by u ilizing a m-le el da a, we e ec i ely con ol o
Table 1. Summa y s a is ics.
Va . De ini ion (Uni ) Mean S.D. Min Max
Ou pu a iable
Ou pu alue Ou pu alue ( housand yuan/hec a e) 11.419 3.284 3.642 22.413
Inpu a iables
Labou Numbe o days o labou equi ed (day/hec a e) 123.492 49.897 36.750 342.300
Fe ilize To al amoun o pu e e ilize used (kilog am/
hec a e)
315.282 100.306 96.300 651.150
Machine y Cos o mechanical ope a ion (yuan/hec a e) 1,032.622 652.896 16.500 3,080.148
O he in e media e
inpu
O he cos excep mechanical cos (yuan/hec a e) 2,668.922 928.006 1,181.195 6,701.064
Independen a iable
Fiscal suppo a io Fiscal suppo o ag icul u e expendi u e/To al
budge expendi u e
0.116 0.037 0.030 0.445
Con ol a iables
Income Ne income o u al esiden s ( housand yuan/pe
capi a)
5.142 1.595 1.595 13.457
P ima y indus y
a io
Added alue o p ima y indus y/GDP 0.175 0.079 0.014 0.417
Employee a io Numbe o employees in p ima y indus y/To al
employmen
0.438 0.124 0.054 0.919
Cul i a ed a ea Cul i a ed a ea ( housand hec a e) 360.229 281.330 22.570 2,237.300
I iga ion a ea E ec i e i iga ion a ea ( housand hec a e) 225.116 132.250 37.310 562.325
Obs. = 22,879. Machine y is adjus ed by ag icul u al machine y p ice index. O he in e media e inpu is adjus ed by
ag icul u al means o p oduc ion p ice index. Income is adjus ed by consume p ice index. Ou pu alue is adjus ed by
consume p ice index. O he in e media e inpu includes managemen cos , inancial cos , insu ance cos , seed cos ,
pes icide cos , and i iga ion cos .
Table 2. Es ima ion esul s o he p oduc ion unc ion.
Dep. Va .
Ln(Ou pu alue)
Es . S.E.
Ln(Labou ) 0.0716*** (0.0126)
Ln(Fe ilize ) 0.0390*** (0.0097)
Ln(Machine y) 0.0308*** (0.0041)
Ln(O he in e media e inpu ) 0.0887*** (0.0141)
σ2
u0.0779
σ2
0.0174
γ¼σ2
u=σ2
uþσ2
� 0.8177
Obs. = 22,879. σ2
u is he a iance o echnical ine iciency. σ2
is he a iance o ypical dis u bance. S anda d e o s a e
gi en in pa en heses. As e isks *, **, and *** deno e signi icance a he 10%, 5%, and 1% le els, espec i ely.
14 Z. WANG ET AL.
Table 3. Main eg ession esul s.
Dep. Va .
Ln(Technical e iciency)
(1) (2) (3) (4) (5) (6) (7)
Fiscal suppo a io 0.2746*** 0.3794*** 0.6429*** 0.5011*** 0.6087*** 0.4487*** 0.2573***
(0.0478) (0.0530) (0.0496) (0.0526) (0.0536) (0.0572) (0.0512)
Ln(Income) 0.1806*** 0.2149*** 0.2051*** 0.1585*** 0.2913***
(0.0058) (0.0081) (0.0080) (0.0096) (0.0262)
P ima y indus y a io 0.0399 0.0169 −0.0023 −0.1350**
(0.0353) (0.0355) (0.0357) (0.0627)
Employee a io 0.1210*** 0.1493*** 0.1041*** 0.0766***
(0.0225) (0.0225) (0.0229) (0.0264)
Ln(Cul i a ed a ea) −0.0624*** −0.0611*** 0.0131
(0.0047) (0.0047) (0.0096)
Ln(I iga ion a ea) 0.0531*** 0.0568*** 0.1076***
(0.0048) (0.0049) (0.0130)
In e cep −0.6724*** −0.6846*** −2.2498*** −2.5846*** −2.4462*** −2.0353*** −3.7920***
(0.0057) (0.0061) (0.0751) (0.0739) (0.0886) (0.2328) (0.0061)
Indi idual FE NO YES NO NO NO NO YES
Yea FE NO YES NO NO NO YES YES
Obs. 22,879 22,879 22,879 22,879 22,879 22,879 22,879
R-squa ed 0.0014 0.8692 0.0433 0.0451 0.0536 0.0636 0.8715
Robus s anda d e o s a e gi en in pa en heses. As e isks *, **, and *** deno e signi icance a he 10%, 5%, and 1% le els, espec i ely.
JOURNAL OF APPLIED ECONOMICS 15
indi idual ixed e ec s, he eby mi iga ing he unde es ima ion issue caused by
omi ed a iables.
Fu he mo e, he R-squa ed o he eg ession in column (7) is 0.8715, indica ing ha
he eg ession model can explain 87.15% o he a ia ion in g ain p oduc ion echnical
e iciency and highligh ing he accu acy o he eg ession model.
5.3. Robus ness checks
In his sec ion, we conduc a se ies o obus ness checks o alida e ou baseline esul s
and mi iga e conce ns ega ding po en ial biases. Table 4 p esen s he esul s o ou
obus ness checks.
Fi s ly, in ou baseline eg ession, we use he a io o iscal suppo o ag icul u e
expendi u e in o al budge expendi u e (Fiscal suppo a io) as he independen a iable
o measu e iscal suppo o ag icul u e. Howe e , di e en ways o measu ing iscal
suppo o ag icul u e may lead o di e en es ima ion esul s. To e i y whe he ou
es ima ion esul s a e a ec ed by he measu emen o he independen a iable, we use
Table 4. Robus ness checks.
Ln(Technical e iciency)
Al e na i e
measu emen o iscal
suppo
Clus e a
coun y
Clus e a p e ec u e- le el
ci y T-L BC95
Dep. Va . (1) (2) (3) (4) (5) (6)
Fiscal suppo a io 0.2573** 0.2573** 0.2680*** 0.1590***
(0.1166) (0.1184) (0.0508) (0.0387)
Ln(Fiscal suppo ) 0.0191***
(0.0057)
Fiscal suppo
a io’
0.2335***
(0.0361)
Ln(Income) 0.2946*** 0.2980*** 0.2913*** 0.2913*** 0.2782*** 0.1266***
(0.0261) (0.0260) (0.0709) (0.0763) (0.0259) (0.0214)
P ima y indus y
a io
−0.1350** −0.0546 −0.1350 −0.1350 −0.0926 0.1795***
(0.0626) (0.0632) (0.2116) (0.2237) (0.0629) (0.0494)
Employee a io 0.0747*** 0.0800*** 0.0766 0.0766 0.0697*** 0.0702***
(0.0264) (0.0265) (0.0655) (0.0714) (0.0270) (0.0225)
Ln(Cul i a ed a ea) 0.0128 0.0130 0.0131 0.0131 0.0095 −0.0342***
(0.0095) (0.0096) (0.0220) (0.0239) (0.0094) (0.0070)
Ln(I iga ion a ea) 0.1030*** 0.1032*** 0.1076*** 0.1076*** 0.0967*** 0.0707***
(0.0134) (0.0131) (0.0353) (0.0390) (0.0132) (0.0084)
In e cep −3.8108*** −3.8401*** −3.7920*** −3.7920*** −3.6209*** −1.5534***
(0.2336) (0.2323) (0.6409) (0.7080) (0.2305) (0.1917)
Indi idual FE YES YES YES YES YES YES
Yea FE YES YES YES YES YES YES
Obs. 22,879 22,879 22,879 22,879 22,879 22,879
R-squa ed 0.8713 0.8716 0.8715 0.8715 0.8696 0.7767
The dependen a iable is Ln(Technical e iciency) de i ed om he model (CD-SFA/CSS) o column (1)-(4), Ln(Technical
e iciency) de i ed om he model (TL-SFA/CSS) o column (5), and Ln(Technical e iciency) de i ed om he model
(CD-SFA/BC95) o column (6). The obus s anda d e o s a e adop ed o column (1), (2), (5) and (6). The s anda d
e o s clus e ed a coun y le el a e adop ed o column (3). The s anda d e o s clus e ed a p e ec u e-ci y le el a e
adop ed o column (4). Fiscal suppo a io’ is de i ed by Fiscal suppo o ag icul u e expendi u e/Added alue o
p ima y indus y. S anda d e o s a e gi en in pa en heses. As e isks *, **, and *** deno e signi icance a he 10%, 5%,
and 1% le els, espec i ely.
16 Z. WANG ET AL.
he loga i hm o iscal suppo o ag icul u e expendi u e (Ln(Fiscal suppo )) and he
a io o iscal suppo o ag icul u e expendi u e in he added alue o he p ima y
indus y (Fiscal suppo a io’) as al e na i e p oxies o iscal suppo o ag icul u e in
columns (1) and (2) o Table 4. The esul s in columns (1) and (2) indica e ha bo h he
loga i hm o iscal suppo o ag icul u e expendi u e and he a io o iscal suppo o
ag icul u e expendi u e in he added alue o he p ima y indus y s ill ha e a signi i-
can ly posi i e impac on g ain p oduc ion echnical e iciency. This sugges s ha ou
baseline esul s a e obus and no d i en by he measu emen o he independen
a iable.
Secondly, in ou baseline eg ession, he e oscedas ici y- obus s anda d e o s a e
adop ed o de e mine he signi icance o he es ima ed coe icien s, as hese s anda d
e o s do no ely on any speci ic assump ions unde la ge sample sizes. To check he
obus ness o coe icien signi icance, we make di e en assump ions abou he a iance
dis ibu ion o he e o e m o he eg ession model in columns (3) and (4) o Table 4.
In column (3), we assume ha he a iance o he e o e m is he e oscedas ic ac oss
coun ies and use clus e ed s anda d e o s a coun y le el. In column (4), we assume ha
he a iance o he e o e m is he e oscedas ic ac oss p e ec u e-le el ci ies and use
clus e ed s anda d e o s a he p e ec u e-ci y le el. The esul s in columns (3) and (4)
indica e ha he impac o iscal suppo o ag icul u e on he g ain p oduc ion echnical
e iciency is s ill signi ican ly posi i e a 5% le el. This sugges s ha ou baseline esul s
a e obus and no d i en by he assump ions abou he a iance dis ibu ion o he e o
e m o he eg ession model.
Las ly, we use he SFA model based on he C-D p oduc ion unc ion o es ima e he
g ain p oduc ion echnical e iciency in ou baseline eg ession. Hence, in column (5) o
Table 4, we use he SFA model based on he T-L p oduc ion unc ion o es ima e he new
g ain p oduc ion echnical e iciency and eplace he o iginal dependen a iable wi h he
new T-L echnical e iciency. Fu he mo e, in ou baseline eg ession, we employ he CSS
es ima o o es ima e he SFA model based on he C-D p oduc ion unc ion. To examine
whe he ou empi ical esul s a e a ec ed by he choice o es ima o , we employ he BC95
es ima o (Ba ese & Coelli, 1995) and adop size ( he ac eage o he a m) and a ie y ( he
dummy a iable o c op a ie ies) o de ine he echnical ine iciency e ec s. Then we use
he BC95 es ima o o es ima e he SFA model based on he C-D p oduc ion unc ion and
ob ain he new g ain p oduc ion echnical e iciency. In column (6) o Table 4, we eplace
he o iginal dependen a iable wi h he new BC95 echnical e iciency. The esul s in
columns (5) and (6) con i m ou baseline esul s once again. A e using new echnical
e iciency, he impac o iscal suppo o ag icul u e on he g ain p oduc ion echnical
e iciency is s ill signi ican ly posi i e. This indica es ha ou baseline esul s a e obus and
no d i en by he measu emen o he dependen a iable.
5.4. He e ogenei y analyses
In his sec ion, we conduc he e ogenei y analyses on he esul s o baseline eg ession by
in oducing in e ac ion e ms. The esul s a e shown in Table 5.
On he basis o conside ing he di e ences in esou ce endowmen s and he de elop-
men o g ain p oduc ion among p o inces, he Chinese go e nmen di ided each
p o ince in o majo g ain-p oducing a eas (MPGA) o non-majo g ain-p oducing
JOURNAL OF APPLIED ECONOMICS 17
a eas. The majo g ain-p oducing a eas unde ake he main g ain p oduc ion asks and
play a co e ole in ensu ing na ional ood secu i y. Hence, he impac o iscal suppo o
ag icul u e on he g ain p oduc ion echnical e iciency may be di e en be ween majo
g ain-p oducing a eas and non-majo g ain-p oducing a eas. Resul s in column (1) o
Table 5 show ha he coe icien o he in e ac ion e m o iscal suppo a io and MGPA
is signi ican ly posi i e, while he coe icien o iscal suppo a io is no signi ican .
These esul s sugges ha he p omo ion e ec o iscal suppo o ag icul u e on g ain
p oduc ion echnical e iciency is only p esen in majo g ain-p oducing a eas. In majo
g ain-p oducing a eas, whe e g ain p oduc ion is he p ima y ocus, go e nmen iscal
suppo o ag icul u e can e ec i ely implemen measu es ha enhance g ain p oduc-
ion, he eby imp o ing g ain p oduc ion echnical e iciency. These indings unde sco e
he impo ance o a ge ed policies ha conside egional he e ogenei y in p omo ing
sus ainable and e icien g ain p oduc ion in China’s ag icul u al sec o .
The ela ionship be ween a m size and ag icul u al p oduc ion echnical e iciency
has been a ocal poin in he ield o ag icul u al economics, bu a consensus has no been
eached. Small-scale a ms ha e he ad an age o p o iding mo e amily labou and he
abili y o adjus ag icul u al p oduc ion s a egies, he eby imp o ing ag icul u al p o-
duc ion echnical e iciency (Be is & Ba e , 2020; Rada & Fuglie, 2019). Howe e , la ge-
Table 5. He e ogenei y esul s by majo g ain-p oducing a eas and by a m size.
Ln(Technical e iciency)
MGPA Fa m size MGPA and Fa m size
Dep. Va . (1) (2) (3)
Fiscal suppo a io 0.0764 0.0626 −0.1191
(0.0837) (0.0622) (0.0937)
MGPA
Fiscal suppo a io × MGPA 0.2842*** 0.2805***
(0.0263) (0.0991)
Size −0.0023*** −0.0024***
(0.0008) (0.0008)
Fiscal suppo a io × Size 0.0279*** 0.0285***
(0.0056) (0.0054)
Ln(Income) 0.2842*** 0.2328*** 0.2263***
(0.0263) (0.0295) (0.0295)
P ima y indus y a io −0.1316** −0.2122*** −0.2090***
(0.0627) (0.0629) (0.0630)
Employee a io 0.0787*** 0.0689*** 0.0710***
(0.0264) (0.0265) (0.0265)
Ln(Cul i a ed a ea) 0.0098 0.0061 0.0028
(0.0096) (0.0095) (0.0096)
Ln(I iga ion a ea) 0.1057*** 0.0916*** 0.0899***
(0.0130) (0.0135) (0.0135)
In e cep −3.7095*** −3.1376*** −3.1067***
(0.2344) (0.2661) (0.2635)
Indi idual FE YES YES YES
Yea FE YES YES YES
Obs. 22,879 22,479 22,479
R-squa ed 0.8715 0.8720 0.8720
No es: MGPA = 1 i a ms a e in he majo g ain-p oducing a eas; MGPA = 0 i a ms a e no in he majo g ain-p oducing
a eas. Size is measu ed by he cul i a ed a ea o he a ms and is conside ed as a con inuous a iable. The coe icien o
MGPA is abso bed by he indi idual FE. Robus s anda d e o s a e gi en in pa en heses. As e isks *, **, and *** deno e
signi icance a he 10%, 5%, and 1% le els, espec i ely.
18 Z. WANG ET AL.
scale a ms ha e g ea e scale- ela ed budge capabili ies and he abili y o in es in
ad anced machine y, allowing hem o be e bene i om echnological ad ancemen s
(Sheng & Chancello , 2019; Tan e al., 2010; Wang e al., 2015). O e all, he imp o emen
o echnical e iciency g ow h ha a ms bene i om iscal suppo o ag icul u e may
a y wi h hei a m size. Resul s in column (2) o Table 5 show ha he coe icien o he
in e ac ion e m o iscal suppo a io and size is signi ican ly posi i e. This indica es
ha la ge a ms bene i mo e om he p omo ional e ec o iscal suppo o ag icul-
u e on g ain p oduc ion echnical e iciency.
Mo eo e , we ake he MGPA and Size in o he same eg ession in column (3) and
ind ha he coe icien s o in e ac ion e ms don’ change much. This indica es ha he
he e ogenei y in MGPA and a m size o he impac o iscal suppo o ag icul u e on
echnical e iciency is obus .
5.5. Mechanisms
We ha e al eady iden i ied he posi i e impac o go e nmen iscal suppo o ag icul-
u e on g ain p oduc ion echnical e iciency in ou baseline eg ession. In his sec ion,
we will u he explo e he speci ic mechanisms h ough which iscal suppo o ag i-
cul u e a ec s g ain p oduc ion echnical e iciency. Table 6 p esen s he esul s o ou
mechanisms es s.
Fi s ly, an inc ease in he p opo ion o mode n ag icul u al p oduc ion inpu ac o s
con ibu es o he imp o emen o g ain p oduc ion echnical e iciency (Y. Zhang &
B ümme , 2011). We measu e he mode n ag icul u al p oduc ion inpu ac o s using
he na u al loga i hm o o al ag icul u al machine y powe . The esul s epo ed in
column (1) o Table 6 indica e ha o e e y 1% inc ease in he iscal suppo a io, he e
is a co esponding 0.2459% inc ease in he ag icul u al machine y powe . I indica es ha
inc eased go e nmen iscal suppo o ag icul u e enhances g ain p oduc ion echnical
e iciency by acili a ing he alloca ion o mode n ag icul u al inpu ac o s and op imiz-
ing he alloca ion o ag icul u al p oduc ion esou ces. We did no include mode n
ag icul u al p oduc ion ac o s as con ol a iables in ou baseline eg ession, because
in his s udy, iscal suppo o ag icul u e can a ec g ain p oduc ion echnical e iciency
by in luencing mode n ag icul u al p oduc ion ac o s. Including he mechanism a i-
ables in he eg ession would lead o an unde es ima ion o ou baseline es ima ion
esul s.
Fu he mo e, due o he high isk and low liquidi y o ag icul u al ac i i ies, as well as
he lack o high-quali y colla e al o ag icul u al c edi , many a me s o en ace di i-
cul ies in ob aining su icien c edi loans, which hinde s hei abili y o engage in
op imal ag icul u al p oduc ion and imp o e ag icul u al p oduc ion echnical e iciency
(Ali e al., 2014; Balana e al., 2022). We measu e he inancing cons ain aced by
a me s using he ag icul u al loan a e, which is calcula ed as he a io o ag icul u al
loans o he o al loan amoun . The esul s epo ed in column (2) o Table 6 demons a e
ha a 1% inc ease in he iscal suppo a io leads o a 0.0232% inc ease in he ag icul u al
loan a io. The inc ease in go e nmen iscal suppo o ag icul u e alle ia es inancing
cons ain s aced by a me s, enabling hem o make g ain p oduc ion decisions wi hou
being limi ed by inancial cons ain s. Consequen ly, his enhances g ain p oduc ion
echnical e iciency.
JOURNAL OF APPLIED ECONOMICS 19
Las ly, he imp o emen in ag icul u al in as uc u e con ibu es o enhancing ag i-
cul u al p oduc ion condi ions, educing p oduc ion cos s, and u he imp o ing g ain
p oduc ion echnical e iciency (Te uel & Ku oda, 2005; Yuan e al., 2021). We employ
wo a iables, u al elec ic powe consump ion and pe capi a highway mileage, o
measu e he le el o in as uc u e cons uc ion in he espec i e egions, which a e
loga i hmically ans o med p io o eg ession analysis. The esul s in columns (3) and
(4) o Table 6 demons a e ha a 1% inc ease in he iscal suppo a io leads o a 0.7521%
inc ease in u al elec ici y powe consump ion and a 0.1893% inc ease in pe capi a
highway mileage. The inc ease in go e nmen iscal suppo o ag icul u e p omo es he
de elopmen o ag icul u e- ela ed in as uc u e, he eby enhancing g ain p oduc ion
echnical e iciency.
6. Conclusion
Based on he panel da a o a ms and p e ec u e-le el ci ies om 2007 o 2012, we employ
he SFA model o es ima e he g ain p oduc ion echnical e iciency a he a m le el.
Addi ionally, we use a wo-way ixed e ec s model o empi ically examine he impac o
go e nmen iscal suppo o ag icul u e on g ain p oduc ion echnical e iciency.
Fu he mo e, a se ies o obus ness checks a e conduc ed o ensu e he s abili y o he
baseline es ima es. We also do he e ogenei y analyses by majo g ain-p oducing a eas
and a m size. Finally, ollowing he concep ual amewo k, we explo e he mechanisms
h ough which go e nmen iscal suppo o ag icul u e a ec s g ain p oduc ion ech-
nical e iciency, ocusing on h ee aspec s: mode n ag icul u al inpu ac o s, inancing
cons ain s, and ag icul u al p oduc ion in as uc u e.
Table 6. Mechanism es s.
Dep. Va .
Inpu ac o Financing cons ain In as uc u e
Ln(Machine y powe ) Loan a io Ln(Elec ic powe ) Ln(Highway mileage)
(1) (2) (3) (4)
Fiscal suppo a io 0.2459*** 0.0232*** 0.7521*** 0.1893***
(0.0344) (0.0090) (0.0738) (0.0540)
Ln(Income) 0.0303** 0.0152** −0.1290*** −0.3868***
(0.0134) (0.0067) (0.0269) (0.0402)
P ima y indus y a io −1.0895*** 0.0738*** −1.2137*** −2.6408***
(0.0569) (0.0278) (0.0748) (0.0706)
Employees a io 0.0957*** 0.0034 −0.1239*** 0.3510***
(0.0209) (0.0074) (0.0414) (0.0548)
Ln(Cul i a ed a ea) 0.1330*** 0.0135*** 0.0997*** 0.0328***
(0.0106) (0.0017) (0.0112) (0.0101)
Ln(I iga ion a ea) 0.1842*** 0.1436*** 0.5447*** 0.2369***
(0.0149) (0.0082) (0.0185) (0.0133)
In e cep 3.7012*** −0.8225*** 9.1414*** 5.5482***
(0.1263) (0.0533) (0.2236) (0.3603)
Indi idual FE YES YES YES YES
Yea FE YES YES YES YES
Obs. 16,363 10,329 22,176 12,412
R-squa ed 0.9866 0.8938 0.9760 0.9669
The dependen a iable is he loga i hm o he o al powe o ag icul u al machine y o column (1), Loan a io de i ed by
Ag icul u al loans/To al loans o column (2), he loga i hm o u al elec ic powe consump ion o column (3), and he
loga i hm o pe capi a highway mileage o column (4). Robus s anda d e o s a e gi en in pa en heses. As e isks *, **,
and *** deno e signi icance a he 10%, 5%, and 1% le els, espec i ely.
20 Z. WANG ET AL.
We ind ha du ing he pe iod o 2007–2012, he a e age g ain p oduc ion
echnical e iciency o Chinese a ms was 0.545 signi ican ly below he maximum
alue o 1. This indica es ha he e is subs an ial oom o imp o emen in he g ain
p oduc ion echnical e iciency o Chinese a ms. Fu he mo e, he esul s o he
baseline eg ession show ha go e nmen iscal suppo o ag icul u e can signi i-
can ly enhance g ain p oduc ion echnical e iciency. Fo 1% inc ease in he a io o
iscal suppo o ag icul u e expendi u e o he o al budge expendi u e, he g ain
p oduc ion echnical e iciency is obse ed o imp o e by 0.2573%. Mo eo e , he
obus ness checks demons a e ha he baseline eg ession esul s emain obus
unde di e en measu es o iscal suppo o ag icul u e, a iance assump ions,
and echnical e iciency es ima ion me hods. Addi ionally, he he e ogenei y analyses
e eal ha he p omo ion e ec o go e nmen iscal suppo on g ain p oduc ion
echnical e iciency is only signi ican in majo g ain-p oducing a eas. Fu he mo e,
la ge -scale a ms expe ience a g ea e p omo ion e ec . Las ly, he mechanism
analysis indica es ha go e nmen iscal suppo o ag icul u e can enhance g ain
p oduc ion echnical e iciency by p omo ing mode n ag icul u al p oduc ion inpu
ac o s, alle ia ing inancing cons ain s aced by households, and imp o ing ag icul-
u al p oduc ion in as uc u e.
This s udy add esses he limi a ions o exis ing li e a u e by u ilizing unique a m-
le el da a o es ima e g ain p oduc ion echnical e iciency, he eby p o iding a mo e
comp ehensi e unde s anding o ag icul u al p oduc ion echnical e iciency a he
mic o le el. In addi ion o measu ing g ain p oduc ion echnical e iciency a he a m
le el, his s udy empi ically examines he impac and mechanisms o go e nmen iscal
suppo o ag icul u e on g ain p oduc ion echnical e iciency. I complemen s he
exis ing li e a u e on he economic e ec s o go e nmen iscal suppo o ag icul u e
and he ac o s in luencing g ain p oduc ion echnical e iciency. Fu he mo e, his s udy
deepens ou unde s anding o he ela ionship be ween go e nmen iscal suppo o
ag icul u e and g ain p oduc ion echnical e iciency. The indings highligh he signi i-
cance o iscal suppo in g ain p oduc ion and o e aluable insigh s o o he de el-
oping coun ies seeking o enhance g ain p oduc ion echnical e iciency and o mula e
ag icul u al iscal suppo policies.
Acknowledgmen s
The au ho s acknowledge Zhejiang Uni e si y o i s suppo o his s udy.
Disclosu e s a emen
No po en ial con lic o in e es was epo ed by he au ho (s).
Funding
This s udy was suppo ed by he Na ional Na u al Science Fund o China (G an No. 71973123)
and he Na ional Social Science Fund o China (G an No. 22&ZD081).
JOURNAL OF APPLIED ECONOMICS 21
No es on con ibu o s
Zu Wang is a Ph.D. candida e in Ag icul u al Economics and Managemen om Zhejiang
Uni e si y. His esea ch in e es s include ag icul u al echnical p og ess and ood secu i y.
Zhihao Wu is a PhD candida e in Ag icul u al Economics and Managemen om Zhejiang
Uni e si y. He esea ch in e es s include ag icul u al ade, digi al ag icul u e, and u al
de elopmen .
Longbao Wei is a dis inguished p o esso a Zhejiang Uni e si y, whose esea ch ield mainly
ocuses on ood economics, ag ibusiness managemen , and digi al economy.
ORCID
Zhihao Wu h p://o cid.o g/0000-0002-6249-2069
Au ho con ibu ions
All au ho s con ibu ed o he s udy concep ion and design. Ma e ial p epa a ion, da a collec ion
and analysis we e pe o med by Zu Wang. The i s d a o he manusc ip was w i en by Zhihao
Wu and all au ho s commen ed on p e ious e sions o he manusc ip . All au ho s ead and
app o ed he inal manusc ip .
A ailabili y o da a and ma e ials
The i s da ase is a ailable om he i s au ho on easonable eques . The second da ase
analysed du ing he cu en s udy is a ailable in he China Ci y S a is ical Yea book eposi o y,
h ps://da a.s a s.go .cn/easyque y.h m?cn=C01.
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