Islam, Md Rohidul; A olabi, Joshua Adeyemi
A icle — Published Ve sion
Ci cula ma e ial use and egional club con e gence o
esou ce p oduc i i y in he Eu opean union
Le e s in Spa ial and Resou ce Sciences
P o ided in Coope a ion wi h:
Sp inge Na u e
Sugges ed Ci a ion: Islam, Md Rohidul; A olabi, Joshua Adeyemi (2025) : Ci cula ma e ial use and
egional club con e gence o esou ce p oduc i i y in he Eu opean union, Le e s in Spa ial and
Resou ce Sciences, ISSN 1864-404X, Sp inge , Be lin, Heidelbe g, Vol. 18, Iss. 1,
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Le e s in Spa ial and Resou ce Sciences (2025) 18:20
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Abs ac
This s udy in es iga es whe he Ci cula Ma e ial Use (CMU) os e s con e gence
in esou ce p oduc i i y ac oss he 27 Eu opean Union (EU) coun ies, elying on
da a spanning 2000-2023. Using Phillips and Sul’s club con e gence amewo k,
we iden i y ou dis inc con e gence clubs, demons a ing pe sis en dispa i ies.
Ou o de ed p obi analysis e eals ha highe CMU signi ican ly inc eases a coun-
y’s likelihood o belonging o a highe esou ce p oduc i i y club. These indings
unde sco e CMU’s ole in b idging esou ce p oduc i i y gaps and emphasize he
need o a ge ed ci cula economy policies.
Keywo ds Resou ce p oduc i i y · Club con e gence · Ci cula ma e ial use ·
Eu opean union
JEL Classi ica ion Q32 · Q38 · Q50
1 In oduc ion
In he pu sui o sus ainable de elopmen , he Eu opean Union (EU) has inc easingly
p io i ized enhancing esou ce p oduc i i y h ough ci cula ma e ial use (CMU)
(Rybá o á 2024). CMU, de ined as he sha e o ma e ials de i ed om ecycled o
eused sou ces, plays a pi o al ole in imp o ing esou ce p oduc i i y (measu ed
as GDP di ided by domes ic ma e ial consump ion). I boos s esou ce p oduc i -
i y by decoupling economic g ow h om ma e ial use, educing eliance on i gin
esou ces h ough inc eased ecycling and euse, hus lowe ing en i onmen al impac
Recei ed: 15 Ap il 2025 / Accep ed: 13 July 2025
© The Au ho (s) 2025
Ci cula ma e ial use and egional club con e gence o
esou ce p oduc i i y in he Eu opean union
Md RohidulIslam1· Joshua AdeyemiA olabi1
Md Rohidul Islam
[email p o ec ed]
Joshua Adeyemi A olabi
[email p o ec ed]
1 Ilmenau Uni e si y o Technology, Ilmenau, Ge many
1 3
M. R. Islam, J. A. A olabi
and enabling mo e economic ou pu pe uni o ma e ial inpu (Blomsma and B ennan
2017; Mo aga e al. 2022). O e he pas wo decades, he EU has achie ed a ema k-
able 44% inc ease in esou ce p oduc i i y (Eu os a 2024b), ollowing he in oduc-
ion o s a egic policies such as he Roadmap o a Resou ce-E icien Eu ope in 2011
and he Ci cula Economy Ac ion Plan in 2015 (Domenech and Bahn-Walkowiak
2019). These policies, oge he wi h o he na ional policies, exp ess he EU’s unda-
men al in e es in subs an ially imp o ing esou ce p oduc i i y and ci cula econ-
omy wi hin i s e i o y. Despi e his p og ess, signi ican dispa i ies pe sis among
membe s a es. Fo ins ance, in 2023, esou ce p oduc i i y index a ied widely
ac oss EU coun ies, wi h Spain epo ing 266 compa ed o Romania’s 63.9, while
CMU a es anged om 30.6% in he Ne he lands o jus 1.3% in Romania, which
highligh s s uc u al dispa i ies in bo h esou ce p oduc i i y and ecycling capaci y,
wi h Romania anking las in bo h indica o s (Eu os a 2024a, b). These dispa i ies
aise c i ical ques ions: Does esou ce p oduc i i y con e ge ac oss EU coun ies, o
do dis inc con e gence clubs eme ge? And how does CMU in luence his p ocess?
The empi ical ela ionship be ween esou ce p oduc i i y and CMU emains
unde explo ed, wi h exis ing s udies o en assuming uni o m con e gence pa hs
o o e looking he e ogenei y (Cui and Zhang 2022; Di Maio e al. 2017; Rybá o á
2024). While some s udies highligh ed he en i onmen al bene i s o CMU (Alola
and Adebayo 2023; Musha iq and P usak 2023) o iden i ied d i e s o esou ce p o-
duc i i y (Rybá o á 2024), hey do no accoun o he po en ial o ma ion o con-
e gence clubs, g oups o coun ies wi h simila ajec o ies in esou ce p oduc i i y
g ow h. Exis ing s udies ypically adop a one-size- i s-all app oach by assuming ha
all coun ies a e p og essing along simila de elopmen al ajec o ies. Howe e , such
assump ions may obscu e c i ical di e ences in economic s uc u es, policy ame-
wo ks, and le els o esou ce p oduc i i y. As a esul , hey ail o accoun o he
po en ial eme gence o con e gence clubs, which a e dis inc g oups o coun ies
exhibi ing in e nally cohe en bu ex e nally di e gen pa e ns o esou ce p oduc-
i i y g ow h. This gap is signi ican because unde s anding club con e gence, a he
han uni o m con e gence, is essen ial o ailo ing policy in e en ions o speci ic
g oups o coun ies wi hin he EU o align wi h he concep o a mul i-speed Eu ope
(Vale 2024).
This pape ills hese esea ch gaps and con ibu es o he li e a u e in wo signi i-
can ways. Fi s , unlike p e ious s udies ha assume uni o m con e gence pa hs, we
explici ly es o he exis ence o con e gence clubs in esou ce p oduc i i y ac oss
EU membe s a es. We employ he Phillips and Sul (2007, 2009) me hodology o
iden i y con e gence clubs, which p o ides a obus amewo k o unde s anding
he e ogeneous g ow h pa e ns and accoun s o s uc u al and policy di e ences ha
may lead o pe sis en dispa i ies in esou ce p oduc i i y. This app oach has been
applied o classi y EU coun ies based on domes ic ma e ial consump ion and ma e-
ial oo p in (Ka akaya e al. 2021) and ind egional dispa i ies in inancial inclusion
in Tu key (Takmaz e al. 2024). Howe e , he cu en pape ex ends he app oach o
ocus on esou ce p oduc i i y as he ou come a iable. Second, his s udy inco po-
a es CMU as a cen al explana o y a iable in analyzing con e gence clubs. While
ea lie wo ks ha e examined he en i onmen al and economic bene i s o CMU
(Mo aga e al. 2022) and de eloped measu es o esou ce e iciency (Di Maio e al.
1 3
20 Page 2 o 12
Ci cula ma e ial use and egional club con e gence o esou ce…
2017), hey ha e no explo ed i s ole in shaping con e gence pa e ns. Ou analysis
b idges his gap by quan i ying he impac o CMU on esou ce p oduc i i y con e -
gence. The o de ed p obi model is employed o assess he in luence o CMU. This
app oach add esses po en ial issues o c oss-sec ional dependence and endogenei y
(Bangjun e al. 2023), which a e o en o e looked in esou ce p oduc i i y s udies.
The esul s e eal ou dis inc con e gence clubs among EU coun ies and con-
i m CMU as a key d i e o esou ce p oduc i i y. Impo an ly, he indings sugges
ha inc easing in es men in CMU can help coun ies con e ge owa d highe p o-
duc i i y le els. Gi en a ying s a ing poin s, he policy implica ion is ha while
CMU de elopmen should be p io i ized ac oss he EU, e o s should also conside
na ional di e ences in cu en CMU pe o mance o maximize e ec i eness.
The emainde o his pape is s uc u ed as ollows: Sec . 2 p esen s he da a
and me hodology, Sec . 3 discusses he esul s, and Sec . 4 concludes wi h policy
implica ions.
2 Me hodology and da a desc ip ion
2.1 Club con e gence
This s udy applies he Phillips and Sul (2007, 2009) club con e gence app oach,
which, unlike adi ional
β
- and
σ
-con e gence es s ha assume uni o m g ow h
pa e ns, accoun s o he e ogeneous ansi ional dynamics. Resou ce p oduc i i y
(
RPi
) o coun y i a ime is modeled as:
RPi =δi µ
(1)
whe e
δi
is a ime- a ying componen cap u ing coun y-speci ic de ia ions, and
µ
ep esen s he common end ac oss all coun ies. Full con e gence equi es:
lim
→∞ δ
i
=δ, ∀i.
(2)
To isola e he idiosync a ic componen and assess whe he
δi
con e ges o a cons an
δ
, Phillips and Sul (2007) de ine he ela i e ansi ion pa ame e
hi
as:
h
i =
RP
i
1
N∑
N
j=1
RPj
=
δ
i
1
N∑
N
j=1
δj
.
(3)
which measu es each coun y’s de ia ion om he egional end. Unde ull con-
e gence,
hi
app oaches uni y, and i s c oss-sec ional a iance declines o e ime,
which is de ined as:
H
=1
N
N
∑
i=1
(hi −1)2
,
(4)
1 3
Page 3 o 12 20
M. R. Islam, J. A. A olabi
The log eg ession es o con e gence is hen es ima ed as:
log (
H1
H
)−
2 log(log )=ˆa+ˆ
blog +ε
.
(5)
Following Phillips and Sul (2007), he log - es is conduc ed o e
=[ T],[ T]+1,...,T
wi h
=0.3
, whe e he con e gence a e coe icien is
gi en by
β=2α
. The null hypo hesis o ull con e gence (
H0:ˆ
b≥0
) is ejec ed i
he -s a is ic alls below
−1.65
a he 5% signi icance le el, indica ing di e gence.
I ull con e gence is ejec ed, we es o club con e gence using he Phillips
and Sul (2009) algo i hm. Coun ies a e anked by esou ce p oduc i i y, and a co e
g oup wi h s ong con e gence endencies is selec ed. The log - es is applied i e a-
i ely, adding coun ies i hei -s a is ic exceeds ze o; o he wise, hey o m sepa a e
clubs. The p ocess epea s un il all coun ies a e classi ied. The implemen a ion ol-
lows Du (2017) in S a a.
2.2 Impac s o CMU: o de ed p obi model
Li e a u e on he de e minan s o club con e gences uses bo h o de ed p obi
(Bangjun e al. 2023; Zhu and Lin 2020) as well as o de ed logi (Wang e al. 2014;
Bha acha ya e al. 2020; Bai e al. 2019; Takmaz e al. 2024). The choice be ween
o de ed p obi and o de ed logi models depends on he assumed dis ibu ion o he
e o e m in he la en a iable model. The o de ed p obi assumes a s anda d no mal
dis ibu ion, whe eas he o de ed logi assumes a logis ic dis ibu ion (Woold idge
2010). In ou case, he o de ed p obi model is mo e app op ia e because o con inu-
ous na u e o esou ce p oduc i i y di e ences ac oss clubs, aligning well wi h he
assump ions o he no mal dis ibu ion. Mo eo e , in small samples, he p obi model
is less sensi i e o ex eme alues and p oduces sligh ly mo e conse a i e es ima es,
which imp o es obus ness in se ings like ou s wi h limi ed obse a ions (N=27
coun ies). The o de ed p obi model akes he ollowing o m:
Y
∗
i=X′
iβ+εi,Y
i=
1,i Y∗
i
≤k
1
,
2,i k1<Y∗
i≤k2,
.
.
.
J,
i
Y∗
i
>k
J−1
.
(6)
whe e
Y∗
i
is he la en a iable ep esen ing a coun y’s p opensi y o belong o a
highe -p oduc i i y club, whe e
Xi
is a ec o o explana o y a iables, including he
CMU a e and con ol a iables, and
β
is he co esponding coe icien ec o .
2.3 Da a desc ip ion
The da ase co e s 27 EU coun ies. Fo he club con e gence analysis, we use
esou ce p oduc i i y, measu ed as GDP (in 2015 cons an PPP eu os) pe uni o
Domes ic Ma e ial Consump ion (DMC), co e ing he pe iod om 2000 o 2023.
Resou ce p oduc i i y da a a e sou ced om Eu os a ’s Ma e ial Flow and Resou ce
1 3
20 Page 4 o 12
Ci cula ma e ial use and egional club con e gence o esou ce…
P oduc i i y da abase (Eu os a 2024b). To mi iga e dis o ions caused by business
cycle luc ua ions, we ex ac he end componen o esou ce p oduc i i y using he
Hod ick–P esco il e (Hod ick and P esco 1997) wi h a smoo hing pa ame e o
6.25 (Ra n and Uhlig 2002) be o e applying he club con e gence algo i hm.
Ou p ima y a iable o in e es in he o de ed p obi analysis is Ci cula Ma e ial
Use (CMU), de ined as he pe cen age sha e o ecycled ma e ials in o al ma e ial
consump ion. CMU da a, co e ing 2010-2023, a e ob ained om Eu os a ’s Ci cula
Economy Indica o s da abase (Eu os a 2024a)1.
Addi ionally, we include se e al con ol a iables in ou o de ed p obi model.
D awing on exis ing li e a u e (Ke ne and Wendle 2022; A olabi 2023; Fang e al.
2024), hese a iables a e o al na u al esou ce en s (exp essed as a pe cen age o
GDP), GDP pe capi a (measu ed in cons an 2015 US dolla s), indus ial s uc u e
(p oxied by indus y alue added as a pe cen age o GDP), and u baniza ion (cap-
u ed as he sha e o he u ban popula ion in he o al popula ion). The con ol a i-
ables span he pe iod om 2000 o 2021 and a e ob ained om he Wo ld Bank’s
Wo ld De elopmen Indica o s da abase (Wo ld Bank 2025). Following common
p ac ice in he li e a u e (e.g., Bai e al., 2019; Bha acha ya e al., 2020; Zhu and
Lin, 2020; Takmaz e al., 2024), hese a iables a e a e aged o e he sample pe iod.
This a e aging app oach is consis en wi h he c oss-sec ional na u e o ou depen-
den a iable, which is s a ic and does no a y o e ime.
3 Resul s
The log - es esul s, p esen ed in he panel A o Table 1, ejec he null hypo hesis
o ull con e gence a he 5% signi icance le el, as he -s a is ic (
−
29.047) alls
well below he c i ical h eshold o
−
1.65. This esul indica es ha esou ce p o-
duc i i y does no ollow a single, uni ied g ow h pa h ac oss EU coun ies. How-
e e , ejec ing he null hypo hesis does no au oma ically p eclude he p esence o
con e gence clubs, whe e subse s o coun ies may s ill ollow dis inc con e gen
ajec o ies (Phillips and Sul 2007). To in es iga e his, we apply he Phillips and Sul
(2009) clus e ing algo i hm and iden i y ou ini ial con e gence clubs. Panel B o
Table 1 highligh s how di e en coun y g oups exhibi a ying deg ees o con e -
gence. Club 1 comp ises high-income Wes e n Eu opean coun ies such as Ge many,
F ance, he Ne he lands, and o he s. Club 2 includes Aus ia, Czechia, Sweden, and
o he s. Club 3, consis ing o 5 Bal ic and Eas e n Eu opean coun ies such as Es onia,
Hunga y, La ia, and o he s, while Club 4 consis s o 2 coun ies, including Bulga ia
and Romania.
Since he Phillips and Sul (2007) me hod ends o o e es ima e he numbe o
clubs, we es whe he adjacen clubs end o me ge in o la ge g oups. The Panel C
o Table 1 p esen s he club me ge es esul s. The null hypo hesis o con e gence
was ejec ed o all club pai s, con i ming ha he ou iden i ied clubs emain dis-
inc , wi h signi ican di e gence p e en ing me ge s. The inal classi ica ion, shown
1 EU-le el CMU da a a e a ailable om 2004 onwa d. Due o he s able ajec o y o EU-le el CMU o e
ime, we assume minimal a ia ion i ea lie coun y-le el da a we e a ailable.
1 3
Page 5 o 12 20
M. R. Islam, J. A. A olabi
in Table 2, con i ms ou s able clubs wi h dis inc con e gence speeds, as measu ed
by he
ˆ
b
coe icien . Club 1 consis s o 10 coun ies, including Belgium, F ance, Ge -
many, G eece, and se e al o he s, exhibi ing ela i ely slow con e gence, as indi-
ca ed by a
ˆ
b
coe icien o 0.040 and a -s a is ic o 1.351. Club 2, also comp ising
10 coun ies such as Aus ia, Czechia, Denma k, and Sweden and se e al o he s,
ollows a sligh ly s onge con e gence ajec o y, wi h a
ˆ
b
coe icien o 0.063 and a
-s a is ic o 1.750. Club 3, which includes 5 coun ies - Es onia, Finland, Hunga y,
La ia, and Li huania - demons a es a mo e p onounced con e gence p ocess, as
e lec ed in i s highe
ˆ
b
coe icien o 0.426 and a -s a is ic o 2.282. Finally, Club 4,
consis ing only o Bulga ia and Romania, exhibi s he s onges con e gence end,
wi h a
ˆ
b
coe icien o 3.768 and a -s a is ic o 2.497.
The a e age esou ce p oduc i i y ends o he inal clubs, depic ed in Fig. 1,
e eal dis inc pa e ns o g ow h among he clubs. Club 1, which comp ises high-
income wes e n EU coun ies, exhibi s a s able and ela i ely high inc ease in
esou ce p oduc i i y, e lec ing a ma u e s age o e iciency imp o emen s. Club 2
ollows a slowe g ow h ajec o y and a s eady ad ancemen in esou ce p oduc i -
Table 1 Ini ial Club Con e gence and Me ge Analysis
Club[N] Con e gence Speed (
ˆ
b
) -S a is ic (
ˆ
b
)
Panel A: Log - es on he whole sample
Full Sample [27]
−
0.710
−
29.047
Panel B: Ini ial classi ica ion
Club 1 [10] 0.040 1.351
Club 2 [10] 0.063 1.750
Club 3 [5] 0.426 2.282
Club 4 [2] 3.768 2.497
Panel C: Club Me ge Tes s
Club 1 + 2 [20]
−
0.435
−
11.435
Club 2 + 3 [15]
−
0.491
−
22.121
Club 3 + 4 [7]
−
0.491
−
28.418
No e: Numbe s in b acke s indica e coun ies pe club. Con e gence speed (
ˆ
b
) measu es he a e o
con e gence; -s a is ic (
ˆ
b
) es s signi icance. Full sample con e gence is ejec ed i
ˆ
b<−1.65
(5%
le el); A nega i e con e gence speed (
ˆ
b
) and highly nega i e -s a is ic in he me ge es s sugges ha
clubs emain sepa a e
Club Coun ies Con e -
gence
Speed (
ˆ
b
)
-S a-
is ic
(
ˆ
b
)
Club 1 Belgium, F ance, Ge many,
G eece, I eland, I aly, Luxem-
bou g, Mal a, Ne he lands, Spain
0.040 1.351
Club 2 Aus ia, C oa ia, Cyp us, Czechia,
Denma k, Poland, Po ugal, Slo a-
kia, Slo enia, Sweden
0.063 1.750
Club 3 Es onia, Finland, Hunga y, La ia,
Li huania
0.426 2.282
Club 4 Bulga ia, Romania 3.768 2.497
Table 2 Final Con e gence
Clubs
1 3
20 Page 6 o 12
Ci cula ma e ial use and egional club con e gence o esou ce…
i y. In con as , Club 3 and Club 4 demons a e a decline in esou ce p oduc i i y
g ow h, al hough he la e has a slowe pace. This indica es ha hese coun ies may
s ill be unde going s uc u al adjus men s and economic ans o ma ions ha a ec
hei esou ce p oduc i i y pa e ns.
Be o e es ima ing he o de ed p obi model, we ank he con e gence clubs
acco ding o hei a e age esou ce p oduc i i y le els. Following s anda d p ac ices
in he li e a u e (e.g., Bai e al., 2019; Bha acha ya e al., 2020), we assign he high-
es nume ic ank o he con e gence club wi h he highes a e age esou ce p o-
duc i i y and he lowes ank o he club wi h he lowes p oduc i i y. Club Rank 4
co esponds o Club 1, he highes a e age p oduc i i y; Club Rank 3 is assigned o
Club 2, which exhibi s mode a ely high a e age esou ce p oduc i i y; Club Rank 2
is a ibu ed o Club 3; and Club Rank 1 co esponds o Club 4, he lowes a e age
esou ce p oduc i i y.
Table 3 shows he esul s o he o de ed p obi model, es ima ing he impac o
he CMU a e on con e gence club membe ship. The coe icien o CMU is posi-
i e and s a is ically signi ican a he 1% le el. This indica es ha coun ies wi h
highe CMU a es ha e a g ea e p obabili y o belonging o con e gence clubs wi h
highe esou ce p oduc i i y. Thus, such coun ies a e mo e likely o all in o Club
Table 3 O de ed P obi Reg ession Resul s: Impac o CMU on Club Membe ship
Va iables Coe icien s Ma ginal E ec
Club 1 Club 2 Club 3 Club 4
CMU 0.143*** 0.041***
−
0.002
−
0.021**
−
0.018
(0.051) (0.010) (0.008) (0.009) (0.011)
N27 Pseudo R
2
0.146
χ2
9.79*** Log likelihood
−
28.60
No e: *** and ** ep esen signi icance le els a 1%, 5%, espec i ely; alues in pa en heses a e obus
s anda d e o s. The dependen a iable is an o dinal anking o con e gence clubs om 1 o 4, whe e a
highe ank indica es a club wi h highe esou ce p oduc i i y. N ep esen s numbe o coun ies. CMU
deno es he ci cula ma e ial use a e
Fig. 1 A e age con e gence pa h
1 3
Page 7 o 12 20
M. R. Islam, J. A. A olabi
Rank 4 (i.e., he highes p oduc i i y g oup, co esponding o Club 1) o Club Rank 3
(second-highes p oduc i i y, co esponding o Club 2), while coun ies wi h lowe
CMU a es a e mo e likely o belong o lowe -p oduc i i y g oups, namely Club
Ranks 1 o 2 (co esponding o Club 4 o Club 3 espec i ely). The ma ginal e ec s
u he e eal ha an inc ease in CMU signi ican ly aises he p obabili y o a coun y
belonging o Club 1, he highes esou ce p oduc i i y club, by 4.1 pe cen age poin s.
Con e sely, a highe CMU signi ican ly educes he likelihood o being in Club 3 by
2.1 pe cen age poin s, whe eas i s impac s on Clubs 2 and 4 a e nega i e bu s a is i-
cally insigni ican . These indings unde sco e he ole o ci cula economy policies in
d i ing p oduc i i y imp o emen s and b idging e iciency gaps ac oss EU coun ies.
We ex end ou eg ession analysis by con olling o he e ec o con ol a i-
ables - na u al esou ce en , GDP pe capi a, indus ial s uc u e, and u baniza ion.
Na u al esou ce en cap u es esou ce-abundance, as esou ce- ich economies may
ely on ex ac ing p ima y ma e ials and could ha e lowe incen i es o use esou ces
e icien ly (A olabi 2023), a phenomenon analogous o he classic esou ce cu se,
whe e abundan na u al esou ces can impede e iciency and inno a ion (Fang e al.
2024). GDP pe capi a measu es he le el o economic de elopmen , as mo e de el-
oped economies ypically ha e mo e ad anced echnologies and s onge ins i u ions,
which can enhance esou ce p oduc i i y and a e o en associa ed wi h membe ship
in high-p oduc i i y con e gence clubs (Ke ne and Wendle 2022). Coun ies wi h
a la ge indus ial sec o end o ha e mo e ma e ial-in ensi e p oduc ion (Baba unde
and A olabi 2024). Las ly, u baniza ion has an ambiguous e ec , as highly u banized
coun ies may bene i om economies o scale in ecycling and esou ce manage-
men , ye may also expe ience highe esou ce consump ion and was e p oduc ion
due o u ban li es yles. (Mendez e al. 2023).
Table 4 p esen s he esul s o he ex ended o de ed p obi eg ession, which
yields se e al no ewo hy insigh s. Fi s , he coe icien on CMU emains posi i e
and s a is ically signi ican a he 5% le el, a e in oducing all con ol a iables.
Table 4 O de ed P obi Reg ession Resul s: Impac o CMU on Club Membe ship
Va iables Coe icien s Ma ginal E ec
Club 1 Club 2 Club 3 Club 4
CMU 0.165** 0.029***
−
0.002
−
0.016
−
0.011*
(0.075) (0.009) (0.007) (0.010) (0.006)
RR
−
1.391**
−
0.241*** 0.019 0.131 0.091**
(0.550) (0.082) (0.062) (0.064) (0.046)
lnGDP 0.882 0.153
−
0.012
−
0.083*
−
0.058
(0.573) (0.102) (0.043) (0.050) (0.042)
IS
−
0.074
−
0.013 0.001 0.007 0.005
(0.061) (0.010) (0.003) (0.006) (0.004)
UP
−
0.018
−
0.003 0.0003 0.002 0.001
(0.028) (0.005) (0.001) (0.003) (0.002)
N27 Pseudo R
2
0.442
χ2
29.63*** Log likelihood
−
18.69
No e: ***, **, and * ep esen signi icance le els a 1%, 5%, and 10%, espec i ely; alues in pa en heses
a e obus s anda d e o s. RR = Resou ce Ren s; lngdp = Na u al log o GDP pe Capi a; IS = Indus ial
S uc u e; UP = U ban Popula ions
1 3
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