Zhang, Dansha; Mas on, Tajul A i in; Lu, Xiu uan
A icle
The impac o digi aliza ion on o eign di ec in es men
in lows in o ci ies in China
Cogen Economics & Finance
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The impac o digi aliza ion on o eign di ec
in es men inflows in o ci ies in China
Dansha Zhang, Tajul A iffin Mas on & Xiu uan Lu
To ci e his a icle: Dansha Zhang, Tajul A iffin Mas on & Xiu uan Lu (2024) The impac o
digi aliza ion on o eign di ec in es men inflows in o ci ies in China, Cogen Economics &
Finance, 12:1, 2330458, DOI: 10.1080/23322039.2024.2330458
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© 2024 The Au ho (s). Published by In o ma
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DEVELOPMENT ECONOMICS | RESEARCH ARTICLE
The impac o digi aliza ion on o eign di ec in es men in lows in o
ci ies in China
Dansha Zhang
a
, Tajul A i in Mas on
b
and Xiu uan Lu
a
a
School o Finance and Economics, Nanning College o Voca ional Technology, Nanning, China;
b
School o Managemen ,
Uni e si i Sains Malaysia, Minden, Malaysia
ABSTRACT
This pape explo es he impac o digi aliza ion on Fo eign Di ec In es men (FDI)
in lows in 270 Chinese ci ies om 2012 o 2019, ocusing on egional dispa i ies in
income le els. Employing he Sys em Gene alized Momen Me hod (GMM), i aims o
b idge he gap in unde s anding how digi aliza ion in luences FDI in lows ac oss
egions wi h di e en income le els. The indings indica e a posi i e co ela ion in
low-income ci ies whe e digi aliza ion signi ican ly a ac s FDI, bu his e ec is lim-
i ed in medium and high-income ci ies. These esul s highligh ha incen i es and
digi al in as uc u e de elopmen could be c ucial o enhancing FDI in lowe -income
egions, making digi aliza ion a po en ial s a egic ool o economic g ow h.
IMPACT STATEMENT
This pape explo es he impac o digi aliza ion on Fo eign Di ec In es men (FDI)
in lows in 270 ci ies in China om 2012 o 2019, ocusing on egional dispa i ies in
income le els by employing he Sys em Gene alized Momen Me hod (GMM). As p e-
ious esea ch on digi aliza ion’s impac on FDI in lows especially a he ci y le el in
China is sca ce, his s udy makes wo con ibu ions: i s ly, we apply he gene alized
me hod o momen s (GMM) o analyze he digi aliza ion-FDI nexus, p o iding empi -
ical insigh s a he ci y le el, which has o en been o e looked in p e ious s udies.
Secondly, by ca ego izing ci ies based on income le els, ou s udy e eals a ia ions
in how digi aliza ion impac s FDI ac oss di e en economic le els. The esul s o his
s udy o e solu ions o economic g ow h in low-income ci ies, as he esea ch ind-
ings show a posi i e co ela ion be ween digi aliza ion and FDI a ac ion in low-
income ci ies, al hough his e ec is limi ed in middle - and uppe -income ci ies. I is
emphasized ha he de elopmen o digi al in as uc u e is c ucial o boos ing FDI
in low-income egions, he eby making digi aliza ion a po en ial s a egic ool o
enhancing economic g ow h in hese a eas.
ARTICLE HISTORY
Recei ed 8 Sep embe 2023
Re ised 20 Feb ua y 2024
Accep ed 10 Ma ch 2024
KEYWORDS
Digi aliza ion; o eign di ec
in es men ; egional
analysis; GMM; China
REVIEWING EDITOR
Goodness Aye, Academic
Edi o , Uni e si y o
Ag icul u e, Maku di Benue
S a e, Nige ia
SUBJECTS
Economics; Poli ical
Economy; In e na ional
Economics
JEL
F23; O33; O53
In oduc ion
China’s Fo eign Di ec In es men (FDI) landscape has wi nessed a signi ican ans o ma ion ollowing
he coun y’s accession o he Wo ld T ade O ganisa ion (WTO) in 2001. Despi e global economic unce -
ain ies, including he COVID-19 pandemic, China has seen esilien g ow h in FDI in lows, which
inc eased annually by o e 6% om 2020 o 2022 (Minis y o Comme ce o China, 2022). The impo -
ance o FDI o hos economies has been well documen ed in he li e a u e (see Be mejo Ca bonell &
We ne , 2018; Bo ensz ein e al., 1998; Iamsi a oj, 2016), including he case o China (see Su & Liu, 2016;
Tang e al., 2008; Zhang, 2001). Howe e , he in lows o FDI ha e been une enly dis ibu ed, as shown
in Figu e 1, wi h a p onounced concen a ion in coas al egions (Lee & Chang, 2009; Rod ik, 1999), and
could be he answe o he indings o un a o able e ec o FDI on income inequali y speci ically in
China as obse ed by G ies and Redlin (2009), Zheng e al. (2021), Chen and Wu (2005), and Miyamo o
and Liu (2005) amid he posi i e ou come o FDI o hos a eas.
1
CONTACT Tajul A i in Mas on [email p o ec ed] School o Managemen , Uni e si i Sains Malaysia, 11800 Minden, Penang, Malaysia
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, 2330458
h ps://doi.o g/10.1080/23322039.2024.2330458
Wi h he impo ance o FDI in lows as a sou ce o capi al, echnology, and managemen skills o local
indus y o he de elopmen o he hos economy o a ea,
2
lagged a eas in China could ge mo e ad an-
ageous by mo e in lows o FDI. Se e al s a egies ha e been s a ed as he key o he success o China in
lu ing a high olume o FDI and becoming among he wo ld la ges ecipien s o FDI could be la ge
domes ic ma ke , labo quali y (as well as cos ), agglome a ion, in as uc u e and ins i u ional quali y
(Boe mans e al., 2011; Li & Pa k, 2006; Na & Ligh oo , 2006), le e aging on he apid de elopmen o
digi aliza ion ac oss he globe, we a e in e es ed on he ole o digi al ans o ma ion on FDI in lows in o
ci ies in China. While adi ional heo ies like he Eclec ic Pa adigm (Dunning, 1977;Dunning,2002)ha e
laid a ounda ional unde s anding o loca ion and in e naliza ion ad an ages, hey la gely o e look he
inc easingly pi o al ole o digi al in as uc u e in he economic landscapes o ci ies. Digi aliza ion can be
de ined by B ennen and K eiss (2016) as he adop ion o inc eased use o digi al o compu e echnology
ac oss a ious sec o s. F om Figu e 2, we can obse e a apid imp o emen in digi al ans o ma ion ac oss
China be ween 2012 and 2019, bu mainly occu s in he coas al and Eas e n a eas. Se e al s udies examine
he e ec s o digi aliza ion, s a ing om he ole o go e nmen ini ia i es o encou age mo e p i a e
inno a ion (Wang e al., 2023), ill he applica ion in a ious a eas such as economic de elopmen (S. Luo
e al., 2023; Wang, e al., 2022), egional dispa i ies (Liu e al., 2024), manu ac u ing sec o (Miao, 2022),
small and medium en e p ises (Zhou & Liao, 2024) and en i onmen al issue (J. Wang, Dong, e al., 2022).
Howe e , esea ch on digi aliza ion’s impac on FDI in lows especially a he ci y le el in China, o ou lim-
i ed eading, is sca ce. A cen al ques ion ou esea ch seeks o answe is whe he inc easing digi aliza ion
ac oss all ci ies in China will help p omo e FDI in lows.
This s udy has wo con ibu ions. We i s apply he gene alized me hod o momen s (GMM) o ana-
lyze he digi aliza ion-FDI nexus, o e ing empi ical insigh s a he ci y le el—a scale o en o e looked in
p e ious s udies (Noussan & Tagliapie a, 2020). Second, by ca ego izing ci ies based on income le els,
ou s udy e eals a ia ions in how digi aliza ion impac s FDI ac oss di e en economic le els.
This pape analyses he de e minan s o FDI in he con ex o ci y-le el digi aliza ion in China om
2012 o 2019. The emainde o he pape is o ganized as ollows. The ela ed li e a u e is b ie ly
e iewed in Sec ion 2. The model cons uc ion and me hodology a e ou lined in Sec ion 3. The empi ical
esul s and discussion a e p esen ed in Sec ions 4 and 5, and Sec ion 6 concludes he s udy.
Figu e 1. Spa ial dis ibu ion o China’s FDI in lows pe o mance in 2012 and 2019.
Sou ce: China U ban S a is ics Yea book (2013–2020).
Figu e 2. Spa ial dis ibu ion o China’s digi aliza ion in 2012 and 2019.
Sou ce: China U ban S a is ics Yea book (2013–2020).
2 D. ZHANG ET AL.
Li e a u e e iew
This li e a u e e iew discusses wo c i ical s eams o esea ch ele an o ou s udy, which include he-
o e ical amewo ks and he empi ical e iew o he ela ionship be ween digi aliza ion and FDI, o ind
he gaps in he exis ing esea ch and p opose he hypo heses o his pape .
Theo e ical amewo k o digi aliza ion and FDI
Theo e ically, many classical heo ies can explain FDI ac i i ies, in which Owne ship, Loca ion and
In e naliza ion (OLI) F amewo k o Eclec ic Pa adigm (Dunning, 1981,1988; Dunning, 1977) explains why
MNEs choose o in es in pa icula hos coun ies, o why hey choose o in es in pa icula loca ions
wi hin pa icula hos coun ies. The loca ion ad an ages o hos coun ies a e igno ed by heo ies like
Monopoly Ad an age Theo ies. Meanwhile, In e naliza ion Theo ies, which is aken in o accoun by he
Eclec ic Pa adigm, sugges he loca ion ad an ages o hos coun ies as an impo an in luencing ac o
in o he esea ch amewo k.
The heo y o loca ion ad an age uses policies, economic a iables, and p oduc ion cos s o explain
why di e en loca ions a e mo e o less a ac i e o FDI. Acco ding o loca ion ad an age, loca ion
de e minan s like ma ke size, labo cos , in as uc u e de elopmen , go e nmen incen i es, loca ion,
and openness a e conside ed he main loca ion de e minan s o FDI. Based on he Eclec ic Pa adigm
and o he ele an heo ies, his pape d aws on p e ious s udies o a iable selec ion.
Digi aliza ion can be ega ded as echnological p og ess, which g ea ly changes he way and speed
o in o ma ion ansmission (Cenamo e al., 2017; Ciampi e al., 2022), and o mul ina ional en e -
p ises, digi aliza ion helps o imp o e he e u n on in es men (Luo, 2021). In es men income is he
ocus o mul ina ional en e p ises when making in es men (Dunning & Lundan, 2010). Digi aliza ion
imp o es he a ac i eness o FDI by educing he in o ma ion cos equi ed by en e p ises o c oss-
bo de in es men . Speci ically, i he local digi aliza ion le el is high, ma ke phenomena ha e been
o med and eco ded in he ins i u ion, which means mo e da a sou ces and makes i easie o
in es o s o ob ain and analyze, he collec ion o da a on newly de eloped ma ke s will also become
mo e p ecise, which is also con enien o en e p ises o make ele an in es men plans (Geo ge &
Schillebeeckx, 2022; Luo, 2021). Digi alized equiped egion also enables o eign en e p ises o quickly
ge amilia wi h he ma ke and social si ua ion o he hos coun y (Wo ld Economic Fo um, 2021).
In addi ion, digi aliza ion educes he cos o alen sea ch. Regions wi h highe le els o digi al
de elopmen o en mean s onge echnological and alen s eng h, which p o ides a la ge numbe
o human esou ces o mul ina ional en e p ises (G impe e al., 2023). Mo eo e , digi aliza ion can
ely on in o ma ion and communica ion echnology o sho en he communica ion dis ance, e ec -
i ely educe he communica ion cos caused by geog aphical dis ance in business ac i i ies o en e -
p ises, and enable en e p ises o be e in eg a e in o he ma ke en i onmen o he hos coun y.
Dunning’s Eclec ic Pa adigm is based on he old eali y o he 1970s and 1980s. These eali ies and
he assump ions behind hem ha e changed conside ably. The e o e, his pape belie es ha heo e ic-
ally, digi aliza ion can be in oduced in o he loca ion ad an age o OLI amewo k as a new mo i a ion
o ansna ional in es men .
In addi ion o digi aliza ion as a ac o a ec ing FDI in lows, heo ies such as Neoclassical g ow h he-
o y, In es men De elopmen Pa h (IDP) heo y and New ade heo y also belie e ha o he ac o s
also ha e an impo an impac on FDI in lows. In Neoclassical G ow h Theo y, pe capi a GDP g ow h
a e is conside ed o ep esen he o e all imp o emen o he economy, indica ing ha a coun y has a
good in es men en i onmen and po en ial la ge ma ke demand, which is one o he ac o s consid-
e ed by mul ina ional en e p ises o choose in es men places. The In es men De elopmen Pa h
Theo y highligh s he impo ance o go e nmen spending, especially in in as uc u e, in a ac ing FDI
(Na ula & Dunning, 2010). This aligns wi h he ‘loca ion ad an ages’in he Eclec ic Pa adigm. Wha is
mo e, u baniza ion also plays a signi ican ole in a ac ing in e na ional capi al, as New ade heo y
indica ed ha agglome a ion economies in u ban a eas a e key ac o s in luencing FDI (Fuji a & Thisse,
1996; Guima ~
aes e al., 2000; Tuan & Ng, 2004). Pas s udies ha e also shown ha he exchange a e has
an unce ain impac on FDI in lows. Cushman (1985) and F oo and S ein (1991) a gue ha he
COGENT ECONOMICS & FINANCE 3
dep ecia ion o he Renminbi p omo ed he in low o o eign di ec in es men (FDI) based on he
‘Rela i e P oduc ion Cos Theo y’(Cushman, 1985) and he ‘Rela i e Weal h Hypo hesis Theo y’(F oo &
S ein, 1991). They belie ed ha he dep ecia ion o a coun y’s cu ency would lowe he p oduc ion
cos s o local goods, inc ease he e u n on FDI, and enhance he ela i e weal h o o eign in es o s.
Empi ical e iews o digi aliza ion and FDI
Wi h China’s eme gence as a digi al leade (Woe zel e al., 2017), he ole o digi aliza ion in FDI has
gained empi ical a en ion. S udies indica e ha digi al ac o s like communica ion acili ies and in e ne
in as uc u e a ac FDI (Boe mans e al., 2011; Mensah & T ao e, 2022). Acco ding o Ha and Huyen
(2022), digi aliza ion is c ucial in a ac ing FDI in he sho - and long- e m. They asse ha digi aliza ion
can help o e come he challenges posed by he COVID-19 pandemic by le e aging da a in 23 Eu opean
coun ies. Some s udies ha e shown ha indi idual indica o s o digi aliza ion ha e a posi i e a ac i e-
ness o FDI, such as communica ion acili ies and elecommunica ion le el (Boe mans e al., 2011;
Mensah & T ao e, 2022). Boe mans e al. (2011) show ha p o inces wi h good communica ion acili ies
a ac o eign in es o s. Mensah and T ao e (2022) indica e ha making high-speed in e ne an in a-
s uc u e quali y indica o induces FDI in he banking and echnology sec o in he Bel and Road
Ini ia i e (BRI) coun ies. In ano he s udy, Sinha and Sengup a (2019) disce ned ha In o ma ion and
communica ion echnology signi ican ly enhances p oduc i i y, e iciency, FDI in lows, and economic
g ow h in de eloping coun ies.
Digi aliza ion can be measu ed using a ious ele an indica o s, some o which a e discussed as
ollows. Fi s is in e ne co e age, an indica o o en used o measu e digi aliza ion. Choi (2003)
selec s 14 in es ing coun ies and 53 hos coun ies o examine he ela ionship be ween he
In e ne and FDI. The s udy inds ha o e e y 10% inc ease in in e ne - ela ed indica o s, FDI
inc eases by a leas 2%. Second, he p opo ion o compu e se ices and so wa e employees
e lec s he deg ee o which an economy le e ages digi al echnologies and employs wo ke s wi h
digi al skills o d i e g ow h, inno a ion, and p oduc i i y (K aus e al., 2021). The inc easing use o
digi al echnologies in businesses has led o a g owing demand o wo ke s wi h skills in compu e
se ices and so wa e de elopmen (M€
olle e al., 2020). Thi d, elecommunica ions se ices, including
ad anced se ices such as b oadband in e ne , mobile da a, and o he high-speed connec i i y se -
ices, ep esen he ex en o which people can access elecommunica ions se ices such as elephone,
in e ne , and o he communica ion echnologies. Na as-Saba e e al. (2002) discuss how access o
in o ma ion and communica ions echnologies has become c ucial o sus ainable economic de elop-
men and po e y educ ion agendas. Fou h, he pos al se ice amoun can e lec he e iciency and
co e age o a ci y’s pos al se ice. This also e lec s digi aliza ion h ough e-comme ce. E icien pos al
se ices can p omo e he low o goods and documen s, which may a ac o eign in es o s seeking
o es ablish a business in a ci y. Fi h is mobile phone pene a ion, an indica o used o measu e
digi aliza ion (Co oche & O danini, 2002). Acco ding o China In e ne Ne wo k In o ma ion Cen e ,
in 2020, 99.7% o China’s In e ne use s (986 million) accessed he in e ne ia hei mobile phones,
while 32.8% and 28.2% o hem accessed he in e ne ia desk op and lap op, espec i ely (Wang &
Liu, 2021). Six h is go e nmen science and echnology (S&T) expendi u e, an essen ial cha ac e is ic
o digi aliza ion. I e e s o he alloca ion o iscal e enue by he easu y o ul ill he goal o S&T
ans o ma ion and inno a ion. Knowles e al. (2021) discuss ha ounda ional esea ch so wa e in a-
s uc u e is c i ical o accele a ing science bu is o en unsus ainably unded. Sol ing his p oblem
equi es an app ecia ion o he impo ance o digi al public goods and a commi men o in es in
go e nmen .
Howe e , some s udies belie e ha digi aliza ion can ha e s ochas ic in luences on FDI in lows.
Fo example, Sang oya e al. (2010) indica e ha adop ing a cloud compu ing pa adigm may ha e
ad e se e ec s on he da a secu i y o se ice consume s. B ougham and Haa (2018) show ha
employees’awa eness o echnological ad ancemen s is nega i ely ela ed o o ganiza ional com-
mi men and job sa is ac ion. Thus, FDI may become less a ac i e in he eyes o domes ic
egions.
4 D. ZHANG ET AL.
Despi e he ex ensi e li e a u e, he e is a gap in unde s anding he comp ehensi e impac o digi al-
iza ion, pa icula ly a he ci y le el, on FDI in China. Exis ing s udies o en ely on single indica o s o
digi aliza ion. Ou s udy add esses his gap by employing a comp ehensi e digi aliza ion index, conside -
ing a ious digi al aspec s. We hypo hesize a posi i e ela ionship be ween he le el o digi aliza ion and
FDI in lows ha will be es ed using a dynamic model ac oss mos ci ies in China.
Me hodology
In de eloping ou model o examine he ela ionship be ween digi aliza ion and FDI in lows in Chinese
ci ies, we ha e d awn upon ex ensi e li e a u e and empi ical s udies, which has guided ou a iable
selec ion, ensu ing ha each a iable is ele an and p o ides meaning ul insigh s in o he dynamics o
FDI. The model aims o quan i y he ac o s in luencing FDI in lows in o Chinese ci ies empi ically. The
li e a u e has highligh ed he impo ance o go e nmen spending, GDP g ow h, u baniza ion, and
exchange a es in de e mining FDI in lows. To inco po a e hese insigh s, he FDI in lows model is speci-
ied as ollows:
FDIi, ¼b0þb1GOVSi, þb2GDPCi, þb3URBPi, þb4EXRTi, þli, (1)
Whe e bs a e pa ame e s o be es ima ed. lis he s ochas ic e m, i and e e o ci ies and yea s,
espec i ely. FDI deno es FDI in lows o Chinese ci ies, he amoun o o eign capi al u ilized annually.
GOVS is go e nmen spending, GDPC ep esen s he annual pe capi a eal GDP g ow h a e, URBP
s ands o he p opo ion o pe manen u ban esiden s in he ci y’s popula ion, including he ci y’s
u ban and u al a eas, EXRT is he exchange a e, he amoun o 1 US dolla in Chinese yuan. Aiming o
explo e he ela ionship be ween digi aliza ion and FDI in lows, he model is expanded o include digi -
aliza ion (DIG) as a key a iable:
FDIi, ¼b0þb1GOVSi, þb2GDPCi, þb3URBPi, þb4EXRTi, þb5DIGi, þli, (2)
T adi ional panel es ima o s such as pooled OLS, andom e ec , and ixed e ec , can be biased and
inconsis en due o he co ela ion be ween he lagged dependen a iable and he e o e m (Ib ahim
& Law, 2014). Conside ing hese econome ic conce ns, his s udy applies a panel da a model using he
Gene alized Me hod o Momen s (GMM) echnique o es ima ion, add essing endogenei y and biases
associa ed wi h non-exogenous explana o y a iables. This me hod aligns wi h me hodologies ou lined
in s udies by Hol z-Eakin e al. (1988), A ellano and Bond (1991), A ellano and Bo e (1995), and Blundell
and Bond (1998). Hence, in GMM o ma , se ing X o ep esen he ec o o explana o y a iables, he
dynamic panel model can be simpli ied as ollows:
FDIi, ¼b0þcFDIi, −1þaDIGi, þbXi, þk þdiþli, (3)
In his s udy, all a iables en e in na u al loga i hmic as indica ed by ln, whe e cis he coe icien o
he lagged dependen a iable, ais he coe icien o he co e a iable DIG, X is a se o o he independ-
en a iables, and k is a pe iod-speci ic e ec common o all coun ies diis he ci y-speci ic e ec , li, is
he andom a iable.
The GMM es ima o uses lagged explana o y a iables as ins umen al a iables, which a e used o
add ess he possible co ela ion be ween he lagged dependen a iable and he e o e m as well as
he endogenei y o he explana o y a iables. This me hod, known as i s -o de di e ence GMM, ans-
o ms Equa ion (2) in o i s -o de di e ences and helps o elimina e ci y-speci ic e ec s. Howe e ,
A ellano and Bo e (1995), Blundell and Bond (1998) and o he s udies ha e shown ha using lagged
le els o a iables as di e en ial eg ession ins umen s may lead o weak ins umen al a iables and
biased es ima es. In addi ion, Bun and Ki ie (2006) show ha his me hod would lead o signi ican
de ia ions in he i s -o de di e ence GMM o highly pe sis en a iables.
To add ess hese issues, his s udy employs a wo-s ep sys em GMM es ima o , which is mo e e icien
han he i s -o de di e ence GMM es ima o . Newey and Wes (1987) p opose he wo-s ep GMM es i-
ma o using he op imal weigh ing ma ix, which showed be e e iciency compa ed wi h he one-s ep
es ima o . Windmeije (2005) also ag ees on he e ec i eness o wo-s ep sys em GMM es ima o .
COGENT ECONOMICS & FINANCE 5
I is necessa y o conduc speci ica ion es s and diagnos ic p ocedu es o ensu e he consis ency o
he GMM es ima o . The Hansen o e iden i ying es ic ion es is included o assess he alidi y o he
ins umen and model speci ica ion. In addi ion, i s -o de se ial co ela ion (AR(1)) and second-o de
se ial co ela ion (AR(2)) need o be pe o med. When he model is alid, he null hypo hesis o he
absence o i s -o de se ial co ela ion should be ejec ed, bu he null hypo hesis o he absence o
second-o de se ial co ela ion should no be ejec ed.
This digi aliza ion (DIG) index measu emen in ol es h ee s ages. The i s s age in ol es iden i ying
six indica o s as he dimensions in digi aliza ion. They a e In e ne pene a ion a e (INT), he p opo ion
o compu e se ices and so wa e employees pe 100 People (COM), o al elecommunica ions se ice
amoun pe 100 people (TEL), pos al se ice amoun pe 100 people (POS), mobile phone use pene a-
ion a e (MPH), science and echnology expendi u e o go e nmen (SCE). The sub-indica o s o he
DIG index we e selec ed wi h he suppo o ele an li e a u e, including Boe mans e al. (2011) and
Mensah and T ao e (2022), who emphasized he impo ance o digi al in as uc u e elemen s such as
elecommunica ions acili ies and mobile connec i i y, and he ollowing: S udies by Ha and Huyen
(2022) and Choi (2003) highligh he impo ance o In e ne pene a ion, e-comme ce, and go e nmen
echnology in es men as indica o s o digi iza ion in a ac ing in es men .
In he second s age, each o hem is o be no malized in o a s anda d measu emen by u ning hem
in o an index ollowing he same o mula by he Uni ed Na ions in he cons uc ion o he Human
De elopmen Index (HDI) as ollows:
3
Dimension index ¼ac ual alue-minimum alue
maximum alue-minimum alue 100
The hi d s ep, ollowing he me hodology used in cons uc ing he HDI index, he hi d s ep in ol es
compu ing he composi e DIG index by a e aging he six dimensions:
DIG ¼INT þCOM þTEL þPOS þMPH þSCE
6
The es o he a iables a e discussed in Table 1. The s udy uses panel da a collec ed om 270
Chinese ci ies be ween 2012 and 2019. Table 1 displays he a iables u ilized in he s udy and hei
espec i e da a sou ces.
Mos a iables u ilized in his s udy a e sou ced om he China U ban S a is ics Yea book o 2013–
2020. Exchange a e da a is ob ained om China’s Na ional Bu eau o S a is ics o he same pe iod.
Resul s
Desc ip i e and co ela ion analyses a e omi ed o conse e space bu a e a ailable upon eques . This
s udy p esen s ull-sample eg ession analysis esul s using he wo-s ep sys em GMM es ima o , as
shown in Table 2. To assess he con ibu ion o each sub-indica o , sepa a e eg ession analyses o sub-
indica o s we e conduc ed in models 2a o 2 . Speci ica ion es s conduc ed be o e in e p e ing he
eg ession esul s show ha he lagged dependen a iable is signi ican and posi i e ac oss a ious es i-
ma ions, alida ing he dynamic model. The null hypo hesis o i s -o de au oco ela ion (AR1) is no
accep ed, while he absence o second-o de au oco ela ion (AR2) canno be ejec ed. The Hansen es
suppo s he alidi y o he ins umen s used in he s udy.
The analysis e eals ha o e all, DIG signi ican ly posi i ely impac s FDI, sugges ing ha an inc ease
in he digi aliza ion le el co esponds wi h a ise in FDI. Wi hin he eg ession esul s o sub-indica o s,
Table 1. Lis o a iables, desc ip ions, and sou ces.
Va iables P oxy Desc ip ions Sou ces
FDI FDI in lows Amoun o o eign di ec in es men u ilized pe yea China U ban S a is ics
Yea book (2013–2020)GOVS Go e nmen spending Amoun o go e nmen iscal spending
GDPC Economy de elopmen Annual pe capi a eal GDP g ow h a e
URBP U baniza ion le el P opo ion o pe manen u ban esiden s in he ci y’s popula ion
EXRT Exchange a e Pe iod a e age p ice, Chinese Yuan (CNY) pe 1 US Dolla (USD)
DIG Digi aliza ion Index Digi aliza ion Index is calcula ed by UNDP me hod
6 D. ZHANG ET AL.
go e nmen Science and Technology Expendi u e (SCE) no ably posi i ely in luences FDI, indica ing SCE
as a po en ac o wi hin he con ex o digi aliza ion’s impac on FDI a ac ion.
Ce ain sub-indica o s like INT, COM, and POS did no signi ican ly impac FDI in he whole sample,
po en ially due o low digi aliza ion le els in ci ies. Add essing his, squa ed (DIG
2
) and cubic (DIG
3
)
e ms o digi aliza ion we e in oduced
4
. Resul s in Table 3 indica e ha e en wi h hese adjus men s,
he impac o squa ing he digi aliza ion le el on FDI a ac i eness emained insigni ican in model 3(a,
c, e, g, and i). Adding he cubic e m o DIG in model 3(b, d, , h, and j) e eals ha sub-indica o s such
as INT and COM nega i ely a ec FDI a ac i eness a highe DIG le els, while o he sub-indica o s
emain s a is ically insigni ican .
Disc epancies in he en i e sample esul s may be a ibu ed o egional imbalances (Li & Pa k, 2016).
To u he explo e his, he model was e-es ima ed by di iding he o al sample based on he a e age
pe capi a income om 2012 o 2019 in o low- (Table 4), middle- (Table 5), and high-income (Table 6)
ci y sub-samples.
Table 2. Reg ession Resul s o he Full sample [DV: FDI].
Model 1:
DIG ¼DIG
Model 2a:
DIG ¼INT
Model 2b:
DIG ¼COM
Model 2c:
DIG ¼POS
Model 2d:
DIG ¼MPH
Model 2e:
DIG ¼SCE
Model 2 :
DIG ¼TEL
FDI
-1
0.755 0.743 0.732 0.693 0.745 0.753 0.721
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
DIG 0.5120.081 0.144 0.082 0.059 0.136−0.010
(0.068) (0.360) (0.194) (0.150) (0.633) (0.059) (0.849)
GOVS 0.337 0.369 0.408 0.403 0.346 0.257 0.429
(0.049) (0.012) (0.020) (0.005) (0.015) (0.133) (0.005)
GDPC −0.042 −0.039 −0.052−0.036 −0.030 −0.051−0.052
(0.187) (0.171) (0.086) (0.226) (0.352) (0.065) (0.084)
URBP −0.051 0.356 0.412 0.363 0.327 0.054 0.686
(0.894) (0.375) (0.343) (0.352) (0.453) (0.856) (0.089)
EXRT −2.250 −2.737−3.139 −2.723−2.259 −1.451 −3.802
(0.248) (0.081) (0.100) (0.077) (0.125) (0.475) (0.015)
Model c i e ia
#Obs 1,512 1,512 1,512 1,512 1,512 1,512 1,512
#Ci ies 270 270 270 270 270 270 270
AR(1) 0.000 0.000 0.000 0.000 0.000 0.000 0.000
AR(2) 0.924 0.973 0.985 0.969 0.978 0.995 0.978
Hansen 0.030 0.078 0.112 0.111 0.081 0.308 0.128
No e. The p- alues a e epo ed in pa en heses. ,, and deno e 10%, 5%, and 1% le el o signi icance, espec i ely. The model is es i-
ma ed wi h 2-s ep sys em GMM.AR and Hansen e e o p- alue.
Table 3. Reg ession esul s o he ull sample wi h squa e and cubic [DV: FDI].
Model 3a Model 3b Model 3c Model 3d Model 3e Model 3 Model 3g Model 3h Model 3i Model 3j
DIG ¼INT DIG ¼COM DIG ¼POS DIG ¼MPH DIG ¼TEL
FDI
-1
0.753
(0.000)
0.746
(0.000)
0.801
(0.000)
0.750
(0.000)
0.758
(0.000)
0.755
(0.000)
0.743
(0.000)
0.735
(0.000)
0.691
(0.000)
0.692
(0.000)
DIG 0.511
(0.091)
0.343
(0.048)
0.175
(0.041)
0.245
(0.024)
−0.107
(0.638)
0.030
(0.805)
0.038
(0.760)
0.208
(0.177)
−0.329
(0.469)
−0.164
(0.519)
DIG
2
−0.069
(0.136)
−0.068
(0.278)
0.017
(0.431)
−0.005
(0.951)
0.023
(0.458)
DIG
3
−0.009
(0.049)
−0.050
(0.067)
0.000
(0.751)
−0.063
(0.134)
0.001
(0.482)
GOVS 0.330
(0.010)
0.327
(0.010)
0.314
(0.019)
0.297
(0.023)
0.257
(0.044)
0.277
(0.034)
0.276
(0.010)
0.257
(0.036)
0.358
(0.008)
0.348
(0.009)
GDPC −0.022
(0.513)
−0.021
(0.527)
−0.043
(0.194)
−0.057
(0.075)
−0.025
(0.388)
−0.025
(0.393)
−0.037
(0.301)
−0.038
(0.356)
−0.047
(0.225)
−0.047
(0.211)
URBP 0.340
(0.327)
0.342
(0.331)
0.190
(0.544)
0.436
(0.192)
0.267
(0.467)
0.290
(0.425)
0.470
(0.188)
0.354
(0.365)
0.888
(0.023)
0.892
(0.022)
EXRT −2.639
(0.063)
−2.476
(0.073)
−2.233
(0.139)
−2.066
(0.159)
−1.108
(0.465)
−1.533
(0.307)
−1.678
(0.100)
−1.182
(0.362)
−2.517
(0.103)
−2.611
(0.072)
Model
c i e ia
#Obs 1,512 1,512 1,512 1,512 1,512 1,512 1,512 1,512 1,512 1,512
#Ci ies 270 270 270 270 270 270 270 270 270 270
AR(1) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
AR(2) 0.976 0.969 0.952 0.989 0.972 0.976 0.957 0.966 0.963 0.966
Hansen 0.117 0.127 0.309 0.288 0.199 0.183 0.0561 0.0933 0.0267 0.0255
No e. The p- alues a e epo ed in pa en heses. ,, and deno e 10%, 5%, and 1% le el o signi icance, espec i ely. The model is es i-
ma ed wi h 2-s ep sys em GMM. AR and Hansen e e o p- alue.
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