Accoun and Financial Managemen Jou nal e-ISSN: 2456-3374
Volume 10 Issue 10 Oc obe 2025, Page No.-3771-3776
DOI: 10.47191/a mj/ 10i10.06, Impac Fac o : 8.167
© 2025, AFMJ
3771
D . Cane Dilbe , AFMJ Volume 10 Issue 10 Oc obe 2025
Capi al and Technology In es men s as De e minan s o Labo P oduc i i y:
A Panel Da a Analysis o OECD Coun ies
D . Cane Dilbe
Assis an P o esso , Facul y O Economics And Adminis a i e Sciences, Labo Economics and Indus ial Rela ions Depa men ,
Cankı ı Ka a ekin Uni e si y. Tu kiye
ABSTRACT: This s udy in es iga es he e ec s o capi al and echnology in es men s on labo p oduc i i y o selec ed OECD
coun ies be ween 2000 and 2021 using panel da a analysis. Two sepa a e panel equa ions we e es ed in he s udy. The i s model
was cons uc ed o measu e he impac o capi al and echnology in es men s on labo p oduc i i y. In he second model, capi al
and echnology in es men s we e squa ed and he esul s a e epo ed. Da a we e included in he analysis in ull loga i hmic o m
o cap u e diminishing ma ginal e u ns. The s udy i s in es iga ed c oss-sec ional dependence and hen conduc ed a uni oo es .
Homogenei y and coin eg a ion analyses we e hen conduc ed. Finally, he Common Co ela ed E ec s (CCE) es ima o (Pesa an,
2006) was used o es ima e he coe icien . Resul s show ha a 1% inc ease in capi al in es men inc eases labo p oduc i i y by
app oxima ely 0.17%, and a 1% inc ease in echnology in es men inc eases i by 0.02%. When capi al and echnology in es men s
a e squa ed and scaled, ma ginal e u ns dec ease by nea ly 50%, suppo ing he hypo hesis o diminishing e u ns. The s udy is
limi ed o 15 OECD coun ies wi h a ailable consis en da a and does no include sec o al decomposi ions. The indings sugges
ha inc easing in es men amoun s alone is insu icien o sus ainable g ow h in OECD coun ies, emphasizing he need o
complemen a y human capi al and ins i u ional capaci y.
KEYWORDS: Labo p oduc i i y, Capi al in es men , Technology in es men , OECD, Panel da a analysis.
INTRODUCTION
Labo p oduc i i y is widely acknowledged as a c ucial d i e
o sus ainable de elopmen and long- e m economic g ow h,
enabling coun ies o inc ease pe capi a income while
enhancing hei compe i i eness in he global ma ke (Cahuc
e al., 2014; OECD, 2001). Wi hin his con ex , capi al
in es men s and echnology in es men s a e conside ed key
ools o boos ing p oduc i i y (Çe in, 2012; A ekin & E bay,
2025). Capi al in es men s, such as in as uc u e, machine y,
and indus ial capaci y expansions, enhance p oduc ion
po en ial, he eby inc easing ou pu pe wo ke (Çe in, 2012).
Empi ical s udies in Tu key and he Eu opean Union ha e
demons a ed signi ican long- e m ela ionships be ween
capi al in es men s and economic g ow h, indi ec ly
in luencing labo p oduc i i y (Şahbaz, 2014). Simila ly,
capi al accumula ion has been ound o suppo
en i onmen al sus ainabili y while inc easing alue-added in
indus ial and se ice sec o s in he Balkan economies (Mi ic
e al., 2020).
Howe e , ecen s udies emphasize ha he e ec o capi al
in es men s on p oduc i i y may no be s ic ly linea and ha
he e ec i eness o in es men s depends on app op ia e
planning and in eg a ion wi h echnological capaci y
(Boamah e al., 2018; T peski e al., 2019; Koce e al., 2019).
Technology in es men s, including digi al ans o ma ion,
R&D, and a i icial in elligence applica ions, play a pi o al
ole in enhancing lexibili y and e iciency in p oduc ion
p ocesses, hus inc easing ou pu pe wo ke (Uslu, 2019; Cao
e al., 2022; Na in, 2023). Ne e heless, he p oduc i i y
gains om hese in es men s o en ely on o ganiza ional
ans o ma ion and he wo k o ce’s abili y o adap o new
echnologies (Yıldız & Ay ekin, 2019; Zhao e al., 2020).
Empi ical e idence sugges s ha he impac o capi al and
echnology in es men s on labo p oduc i i y may exhibi
diminishing ma ginal e u ns once ce ain h esholds a e
su passed (Huisman & Ko , 2003; Doğane , 2022).
Consequen ly, examining he nonlinea e ec s o capi al and
echnology in es men s on labo p oduc i i y using ad anced
panel da a me hods p o ides aluable insigh s o
policymake s aiming o achie e sus ainable p oduc i i y
g ow h in OECD coun ies.
This s udy con ibu es o he li e a u e by analyzing how
capi al accumula ion and echnological ad ancemen
in luence labo p oduc i i y ac oss selec ed OECD coun ies,
u ilizing a ully loga i hmic model o cap u e elas ici ies
Addi ionally, by inco po a ing quad a ic e ms, he s udy
empi ically es s he hypo hesis o diminishing e u ns, as
sugges ed by neoclassical g ow h heo y (Solow, 1956) and
endogenous g ow h heo ies (Rome , 1990; Lucas, 1988),
which posi ha while capi al and echnology can d i e
g ow h, hei ma ginal con ibu ions decline wi hou adequa e
complemen a y ac o s such as human capi al and
“Capi al and Technology In es men s as De e minan s o Labo P oduc i i y: A Panel Da a Analysis o OECD
Coun ies”
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D . Cane Dilbe , AFMJ Volume 10 Issue 10 Oc obe 2025
ins i u ional capaci y (Acemoglu & Res epo, 2022). By
add essing he me hodological gap in he li e a u e h ough
he applica ion o second-gene a ion panel da a echniques,
including he Wes e lund panel coin eg a ion es and he
Common Co ela ed E ec s (CCE) es ima o , his s udy aims
o p o ide obus e idence on he nonlinea impac s o capi al
and echnology in es men s on labo p oduc i i y in he
con ex o OECD coun ies. The indings a e in ended o
guide policymake s in designing balanced in es men
s a egies ha in eg a e human capi al de elopmen and
ins i u ional s eng hening wi h capi al and echnological
in es men s o os e sus ainable p oduc i i y g ow h.
Addi ionally, by inco po a ing quad a ic e ms, he s udy
empi ically es s he hypo hesis o diminishing e u ns, as
sugges ed by neoclassical g ow h heo y (Solow, 1956) and
endogenous g ow h heo ies (Rome , 1990; Lucas, 1988),
which posi ha while capi al and echnology can d i e
g ow h, hei ma ginal con ibu ions decline wi hou adequa e
complemen a y ac o s such as human capi al and
ins i u ional capaci y (Acemoglu & Res epo, 2022). By
add essing he me hodological gap in he li e a u e h ough
he applica ion o second-gene a ion panel da a echniques,
including he Wes e lund panel coin eg a ion es and he
Common Co ela ed E ec s (CCE) es ima o , his s udy aims
o p o ide obus e idence e ec on capi al and echnology
in es men s on labo p oduc i i y in he con ex o OECD
coun ies. The indings a e in ended o guide policymake s in
designing balanced in es men s a egies ha in eg a e
human capi al de elopmen and ins i u ional s eng hening
wi h capi al and echnological in es men s o os e
sus ainable p oduc i i y g ow h.
REVIEW OF LITERATURE
Empi ical s udies ha e demons a ed ha capi al in es men s
in in as uc u e and machine y can inc ease ou pu pe
wo ke , con ibu ing signi ican ly o p oduc i i y g ow h
ac oss coun ies (Çe in, 2012; Şahbaz, 2014). Fo ins ance,
s udies ocusing on Tu key and Eu opean Union coun ies
ha e con i med he posi i e long- e m ela ionship be ween
capi al o ma ion and economic g ow h, indi ec ly enhancing
labo p oduc i i y (Şahbaz, 2014). Mo eo e , in he Balkan
economies, capi al accumula ion has been associa ed wi h
inc eased alue-added in he indus ial and se ice sec o s,
con ibu ing o en i onmen al sus ainabili y (Mi ic e al.,
2020).
Howe e , some s udies a gue ha he ela ionship be ween
capi al in es men s and p oduc i i y may exhibi nonlinea
dynamics, as in es men s beyond ce ain h esholds may
esul in diminishing ma ginal e u ns due o ine iciencies,
unde u iliza ion, o inadequa e human capi al
complemen a i y (Boamah e al., 2018; Koce e al., 2019).
These indings align wi h he law o diminishing e u ns,
which sugges s ha while capi al inc eases p oduc i i y
ini ially, he inc emen al bene i s decline as he s ock o
capi al g ows wi hou complemen a y ac o s. Simila ly,
echnology in es men s ha e become inc easingly c i ical o
p oduc i i y gains, pa icula ly in he e a o digi al
ans o ma ion. In es men s in digi al in as uc u e, R&D,
and a i icial in elligence can enhance lexibili y, educe
p oduc ion cos s, and inc ease ou pu pe wo ke
(B ynjol sson & McA ee, 2014; Cao e al., 2022). S udies
indica e ha echnology in es men s d i e p oduc i i y
imp o emen s by acili a ing au oma ion, imp o ing p oduc
quali y, and educing ansac ion cos s (Na in, 2023; Zhao e
al., 2020). Ne e heless, he impac o echnology
in es men s on p oduc i i y can also exhibi diminishing
ma ginal e u ns, especially when o ganiza ional s uc u es
and human capi al a e insu icien o suppo echnological
in eg a ion (Yıldız & Ay ekin, 2019; Huisman & Ko , 2003).
B ynjol sson and McElhe an (2019) and DeS e ano e al.
(2023) highligh ha i ms wi h highe digi al ma u i y end
o ealize mo e signi ican p oduc i i y gains, indica ing he
need o complemen a y in es men s in skills and
o ganiza ional change.
Recen s udies ha e also emphasized he impo ance o
ad anced econome ic app oaches in analyzing he capi al-
echnology-p oduc i i y nexus, gi en he complexi ies o
c oss-sec ional dependence and he e ogenei y ac oss
coun ies. Panel da a me hods, such as he Wes e lund
coin eg a ion es and Common Co ela ed E ec s (CCE)
es ima o , allow o obus es ima ion in he p esence o hese
complexi ies, p o iding mo e eliable e idence o policy
ecommenda ions (Wes e lund, 2007; Pesa an, 2006).
While he li e a u e acknowledges he impo ance o capi al
and echnology in es men s in d i ing p oduc i i y g ow h,
he e emains a gap in empi ical s udies explo ing hei
nonlinea impac s using second-gene a ion panel da a
me hods wi hin he OECD con ex . This s udy aims o add ess
his gap by analyzing how capi al and echnology in es men s
in luence labo p oduc i i y ac oss selec ed OECD coun ies,
explici ly es ing he hypo hesis o diminishing e u ns using
ully loga i hmic models wi h quad a ic e ms. By doing so,
he s udy con ibu es o he li e a u e wi h policy- ele an
indings, o e ing insigh s in o designing balanced in es men
s a egies o sus ainable p oduc i i y g ow h in de eloped
economies.
RESEARCH METHODOLOGY
In his s udy, panel da a om 15 OECD coun ies co e ing
he pe iod 2000–2021 we e u ilized. The selec ion o he
numbe o coun ies and he ime ame was de e mined based
on he c i e ion o maximizing he numbe o coun ies and
he leng h o he pe iod wi h comple e da a a ailabili y. The
coun ies selec ed based on hese c i e ia a e p esen ed in
Table 1.
“Capi al and Technology In es men s as De e minan s o Labo P oduc i i y: A Panel Da a Analysis o OECD
Coun ies”
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D . Cane Dilbe , AFMJ Volume 10 Issue 10 Oc obe 2025
Table 1. Coun ies Included in he S udy
Uni ed S a es
Belgium
Po ugal
Iceland
Canada
Luxembou g
Swi ze land
No way
F ance
Ge many
Aus ia
Spain
Ne he lands
I aly
Sweden
In he s udy, he a iable ep esen ing labo p oduc i i y is
measu ed as eal GDP pe employee. The a iable
ep esen ing capi al in es men pe employee is measu ed as
eal g oss ixed capi al o ma ion pe employee, and he
a iable ep esen ing echnology in es men pe employee is
measu ed as eal echnology- ela ed in es men s pe
employee. The desc ip ions and da a sou ces o hese
a iables a e p esen ed in Table 2.
Table 2. Desc ip ion o Va iables
Va iable
Desc ip ion
Da a Sou ce
P oduc i i y
Real GDP pe employee
Wo ld Bank
peRGSFC
Real g oss ixed capi al
o ma ion pe employee
Wo ld Bank
peTI
Real echnology- ela ed
in es men s pe
employee
OECD Going
Digi al Toolki ,
Wo ld Bank
No e: Calcula ions o hese a iables we e conduc ed by he
au ho s.
In his esea ch, wo di e en model es ima ions a e
conduc ed. In he i s model, he e ec o capi al in es men
pe employee and echnology in es men pe employee on
labo p oduc i i y is examined using a ully loga i hmic (log-
log) model. In he second model, he analysis explo es how
he e ec on labo p oduc i i y changes when he squa e o
capi al in es men pe employee and he squa e o echnology
in es men pe employee a e included, again wi hin a ully
loga i hmic amewo k.
The equa ions examined in he s udy a e p esen ed below.
𝑙𝑛𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦𝑖𝑡 = 𝛽0+ 𝛽1𝑙𝑛𝑝𝑒𝑅𝐺𝑆𝐹𝐶
+ 𝛽2𝑙𝑛𝑝𝑒𝑇𝐼𝑖𝑡 + 𝜀𝑖𝑡
(1)
𝑙𝑛𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦𝑖𝑡 = 𝛽0+ 𝛽1𝑙𝑛𝑝𝑒𝑅𝐺𝑆𝐹𝐶𝑖𝑡
2
+ 𝛽2𝑙𝑛𝑝𝑒𝑇𝐼𝑖𝑡
2+ 𝜀𝑖𝑡
(2)
RESULT AND DISCUSSION
Selec ing an app op ia e es ima ion model o he panel
da ase is c ucial, as conduc ing es ima ions wi hou es ing
o c oss-sec ional dependence, uni oo s, and slope
homogenei y may esul in spu ious ela ionships. In his
s udy, he Pesa an (2021) CD es was i s employed o assess
c oss-sec ional dependence, and he esul s a e p esen ed in
Table 3.
Table 3. Pesa an CD Tes Resul s o C oss-sec ional
Dependence
Va iable
CD S a is ic
p- alue
lnp oduc i i y
23,91
0,000***
lnRGSFC
13,82
0,000***
lnTI
18,51
0,000***
ln(RGSFC)2
13,82
0,000***
ln(TI)2
18,51
0,000***
The CD es esul s ejec he null hypo hesis o weak c oss-
sec ional dependence o all a iables, indica ing signi ican
c oss-sec ional dependence ha should be accoun ed o in
he es ima ion p ocess. Gi en hese indings, i is app op ia e
o employ second-gene a ion panel uni oo es s ha
conside c oss-sec ional dependence. Acco dingly, he
Pesa an (2007) CADF es was used o es o s a iona i y,
and he esul s a e shown in Table 4.
Table 4. Uni Roo Tes Resul s (Pesa an CADF)
Le el
Va iable
Model
-ba
CV%10
CV%5
CV%1
Z[ -ba ]
p- alue
lnP oduc i i y
Cons an
-1,379
-2,140
-2,250
-2,450
1,560
0,941
Cons an T end
-2,295
-2,660
-2,760
-2,960
0,105
0,542
lnRGSFC
Cons an
-1,364
-2,140
-2,250
-2,450
1,619
0,947
Cons an T end
-2,025
-2,660
-2,760
-2,960
1,227
0,890
lnpeTI
Cons an
-1,520
-2,140
-2,250
-2,450
0,998
0,841
Cons an T end
-2,787
-2,660
-2,760
-2,960
-1,944
0,026
lnRGSFC2
Cons an
-1,364
-2,140
-2,250
-2,450
1,619
0,947
Cons an T end
-2,025
-2,660
-2,760
-2,960
1,227
0,890
lnpeTI2
Cons an
-1,520
-2,140
-2,250
-2,450
0,998
0,841
Cons an T end
-2,787
-2,660
-2,760
-2,960
-1,944
0,026
Fi s Di e ence
lnP oduc i i y
Cons an
-2,633
-2,140
-2,250
-2,450
-3,444
0,000
Cons an T end
-2,791
-2,660
-2,760
-2,960
-1,962
0,000
“Capi al and Technology In es men s as De e minan s o Labo P oduc i i y: A Panel Da a Analysis o OECD
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D . Cane Dilbe , AFMJ Volume 10 Issue 10 Oc obe 2025
lnRGSFC
Cons an
-2,522
-2,140
-2,250
-2,450
-3,001
0,000
Cons an T end
-2,577
-2,660
-2,760
-2,960
-1,071
0,000
lnpeTI
Cons an
-3,552
-2,140
-2,250
-2,450
-7,117
0,000
Cons an T end
-3,676
-2,660
-2,660
-2,660
-5,646
0,000
lnRGSFC2
Cons an
-2,522
-2,140
-2,250
-2,450
-3,001
0,000
Cons an T end
-2,577
-2,660
-2,660
-2,660
-1,071
0,000
lnpeTI2
Cons an
-3,552
-2,140
-2,250
-2,450
-7,117
0,000
Cons an T end
-3,676
-2,660
-2,660
-2,660
-5,646
0,000
A he le el, mos a iables exhibi uni oo s, excep o
lnTechnology In es men and ln(TI)2 unde he cons an end
model, which a e s a iona y a he 5% signi icance le el.
Howe e , upon i s di e encing, all a iables become
s a iona y a he 1% signi icance le el unde bo h cons an
and cons an end models, indica ing ha he a iables a e
in eg a ed o o de one [I(1)]. This ou come aligns wi h
common p ac ices in he li e a u e, whe e he majo i y o es
esul s guide he conclusion (Bal agi & Pi o e, 2010), and he
a iables a e hus conside ed I(1) in his s udy.
Following he uni oo es s, slope homogenei y was assessed
using he Pesa an and Yamaga a (2008) homogenei y es ,
wi h esul s shown in Table 5.
Table 5. Slope Homogenei y Tes Resul s
Model 1
Model 2
S a is ic
p- alue
S a is ic
p- alue
∆
5,491
0,000
∆
5,491
0,000
∆
ajd
6,103
0,000
∆ajd
6,103
0,000
The es esul s indica e he e ogenei y ac oss he slope
coe icien s o c oss-sec ional uni s. The e o e, i is essen ial
o employ an es ima ion app oach ha accoun s o
he e ogenei y in he panel da ase .
Gi en he p esence o c oss-sec ional dependence and
he e ogenei y, he Wes e lund (2007) ECM panel
coin eg a ion es was employed, and he esul s a e epo ed
in Table 6.
Table 6. Wes e lund Panel Coin eg a ion Tes Resul s
Model 1
Model 2
S a is ic
Z-S a is ic
p- alue
S a is ic
Z-S a is ic
p- alue
G
-2,021
-2,365
0,000
G
-2,021
-2,365
0,000
Ga
-8,401
-1,825
0,034
Ga
-8,401
-1,825
0,034
P
-7,548
-2,949
0,000
P
-7,548
-2,949
0,000
Pa
-8,133
-4,452
0,000
Pa
-8,133
-4,452
0,000
The coin eg a ion es esul s con i m he exis ence o a long-
un ela ionship among he a iables. Iden ical alues o bo h
models a e due o Model 2 including only he squa ed e ms
o he independen a iables wi hou addi ional da a
ans o ma ion.
Following hese diagnos ics, he Common Co ela ed E ec s
(CCE) es ima o (Pesa an, 2006), which accoun s o c oss-
sec ional dependence and he e ogenei y, was selec ed o he
inal es ima ion Table 7.
Table 7. CCE Es ima ion Resul s
Dependen Va iable: lnp oduc i i y
Model 1
Va iable
Coe icien
S d.
de ia ion
z
p
[95% Con idence
In e al]
lnRGSFC
,1792227
,0325856
5,50
0,000***
,1153562
,2430893
lnpeTI
,0210334
,0108744
1,93
0,053**
-,0002802
,0423469
Dependen Va iable: lnp oduc i i y
Model 2
Va iable
Coe icien
S d.
de ia ion
z
p
[95% Con idence
In e al]
lnRGSFC2
,0896114
,0162928
5,50
0,000***
,0576781
,1215446
lnpeTI2
,0105167
,0054372
1,93
0,053**
-,0001401
,0211734
No e: *** p < 0.01, * p < 0.10
“Capi al and Technology In es men s as De e minan s o Labo P oduc i i y: A Panel Da a Analysis o OECD
Coun ies”
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D . Cane Dilbe , AFMJ Volume 10 Issue 10 Oc obe 2025
The es ima ion esul s indica e ha bo h capi al in es men
pe employee and echnology in es men pe employee ha e
posi i e and signi ican e ec s on labo p oduc i i y in
OECD coun ies. Speci ically, Model 1 sugges s ha a 1%
inc ease in capi al in es men pe employee leads o a 0.17%
inc ease in labo p oduc i i y, while a 1% inc ease in
echnology in es men pe employee leads o a 0.02%
inc ease in p oduc i i y.
In Model 2, when he squa ed e ms o he in es men
a iables a e included, he e ec s on p oduc i i y dec ease o
0.08% o capi al in es men and 0.01% o echnology
in es men pe 1% inc ease, demons a ing diminishing
ma ginal e u ns. This indica es ha con inuously inc easing
capi al and echnology in es men s does no esul in
p opo ional inc eases in p oduc i i y, consis en wi h he law
o diminishing e u ns, as he ma ginal p oduc o capi al and
echnology dec eases when in es men s a e expanded u he .
CONCLUSION
This s udy con ibu es o he li e a u e by examining he
e ec s o capi al and echnology in es men s on labo
p oduc i i y using panel da a o selec ed OECD coun ies.
The indings e eal ha while capi al and echnology
in es men s a e essen ial ools o enhancing labo
p oduc i i y, he pace o p oduc i i y gains declines beyond a
ce ain h eshold, exhibi ing diminishing ma ginal e u ns
(Acemoglu & Res epo, 2021). This ou come con i ms he
law o diminishing e u ns, indica ing ha con inuous
inc eases in capi al and echnology in es men s do no lead o
p opo ional inc eases in labo p oduc i i y.
The esul s indica e ha capi al in es men s exe a s onge
impac on labo p oduc i i y compa ed o echnology
in es men s, consis en wi h he indings o Ba o and Sala-i-
Ma in (2004) and Jones (2005), who emphasize ha physical
capi al accumula ion p o ides signi ican con ibu ions o
p oduc i i y g ow h in he sho and medium e m.
Speci ically, in his s udy, a 1% inc ease in capi al
in es men s leads o a 0.17% inc ease in p oduc i i y, while
a simila inc ease in echnology in es men s esul s in a
0.02% inc ease, aligning wi h Jo genson and Vu’s (2016)
indings ha he ma ginal con ibu ions o echnology
in es men s decline beyond ce ain le els. These insigh s a e
consis en wi h he OECD’s “Going Digi al” policy, which
emphasizes s eng hening digi al in as uc u e, accele a ing
i m-le el digi aliza ion p ocesses, and equipping he
wo k o ce wi h digi al skills o suppo sus ainable g ow h
(OECD, 2021). Howe e , achie ing sus ainable p oduc i i y
gains om digi al in es men s depends on he p esence o
complemen a y elemen s, such as he quali y o human
capi al, o ganiza ional capaci y, and ins i u ional
in as uc u e (Acemoglu & Res epo, 2021). Thus,
inc easing digi al in es men s alone is insu icien , and
in es men s should be in eg a ed wi h human capi al
de elopmen , ins i u ional capaci y, and R&D ini ia i es
(Masou a & Male aki, 2023).
The s udy also ound ha including capi al and echnology
in es men s squa ed in he model leads o a educ ion in
ma ginal e u ns o app oxima ely 50%, indica ing ha he
ma ginal p oduc o capi al and echnology dec eases
signi ican ly as in es men le els inc ease. This obse a ion
aligns wi h he indings o Tambe e al. (2019) and Mindell
and Reynolds (2023), who emphasize he need o imp o e
wo k o ce skills o ealize he po en ial p oduc i i y gains
om digi aliza ion. Gi en he aging popula ions in OECD
coun ies and he need o imp o e wo k o ce quali y,
p io i izing educa ion and skills ans o ma ion p og ams o
maximize he e ec i eness o capi al and echnology
in es men s becomes c ucial (B ynjol sson and McA ee,
2014).
In conclusion, his s udy demons a es ha while capi al and
echnology in es men s play a c i ical ole in enhancing labo
p oduc i i y in OECD coun ies, hey exhibi diminishing
ma ginal e u ns beyond a ce ain h eshold. To achie e
sus ainable p oduc i i y g ow h, in es men s should be
planned in conjunc ion wi h digi al capaci y building, human
capi al de elopmen , and ins i u ional in as uc u e
imp o emen s. This holis ic app oach will con ibu e o he
de elopmen o balanced capi al- echnology in es men
s a egies, suppo ing sus ainable g ow h and p oduc i i y
enhancemen in OECD coun ies o e he long e m.
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