A ica’s Economic T ans o ma ion
A Big Da a Pe spec i e
Sebas ian K an z
*
Doc o al Disse a ion in Quan i a i e Economics
Uni e si y o Kiel, in Pa ne ship wi h he
Kiel Ins i u e o he Wo ld Economy
No embe 11, 2024
Abs ac
A ica is a con inen o g ea economic po en ial. Wi h a popula ion o 1.5 billion
a a median age o 19 oday, p ojec ed o each 2.5 billion by 2050, as na u al and
mine al esou ces, ye a sha e o only ∼3% in global GDP and ade, A ica’s economic
ans o ma ion mus ma e ialize o p o ide oppo uni ies o i s you h and o os e a
mo e balanced, equi able, and secu e wo ld o de in he ace o sha ed global challenges.
The A ican Union’s Agenda 2063 se s an ambi ious pa h o achie e his, and wi h he
o mal enac men o a con inen al ee ade a ea, a subs an ial landma k has been
passed. Howe e , ade and egional alue chains (RVCs) mus pick up signi ican ly
o gene a e he desi ed economic ans o ma ion, suppo ed by indus ializa ion and
inc eases in p oduc i i y. Fo his, mac oeconomic and inancial s abili y a e needed
alongside indus ial/RVC policies and la ge-scale in es men s in in as uc u e and
human capi al. This doc o al disse a ion con ibu es o ou unde s anding o hese
c i ical ing edien s. I documen s he con inen ’s p og ess in mac oeconomic s abili y
and in es iga es i s d i e s. I also dissec s A ica’s in eg a ion in o global and egional
alue chains, elici ing p og ess, bene i s, po en ials, and challenges owa ds/o egional
economic in eg a ion. Las bu no leas , i zooms in on he con inen ’s in as uc u e,
u ilizing geospa ial big da a and mode n s uc u al and empi ical me hods o p o ide
e idence on local and global in es men po en ials a unp eceden ed spa ial de ail and
scale. By combining igo ous quan i a i e economics and (causal) machine lea ning wi h
he iches da a on global p oduc ion, ade, in as uc u e, and economic geog aphy
a ailable a he ime o w i ing, i p oduces e y de ailed and subs an i e e idence on
c i ical aspec s o A ica’s p esen and u u e economic ans o ma ion. I hus enhances
ou academic unde s anding o he con inen , i s economic po en ial and challenges, bu
also in o ms policies o accele a e economic p og ess and ans o ma ion a scale.
Keywo ds: A ica, economic ans o ma ion and de elopmen , in as uc u e,
oads, spa ially op imal in es men s, egional in eg a ion, ade, GVCs,
RVCs, EAC, macoeconomic s abili y, g ow h, ola ili y, s uc u al change,
big da a, pa ial and gene al equilib ium, causal ML, explainable AI
JEL Classi ica ion: F14; F15; O11; O18; R42; R10; O10; O11; E30; E60
*
Kiel Ins i u e o he Wo ld Economy
Add ess: Haus Wel -Club, Dues e nb ooke Weg 148, D-24105 Kiel
E-mail: sebas ian.k an[email p o ec ed] o [email p o ec ed]h
Websi e: sebas iank an z.com and i w-kiel.de/expe s/sebas ian-k an z
Academic Supe iso
: P o . Tobias Heidland
Re iewe
: P o . Ch is oph T ebesch
Da e o O al Examina ion:
21.11.2024
Con en s
1 In oduc ion 1
2 Mac oeconomic S abiliza ion 6
2.1 A ica’s G ea Mode a ion ............................. 7
3 Regional and Global Economic In eg a ion 66
3.1 A ica’s Regional and Global In eg a ion .................... 67
3.2 Pa e ns o Global and Regional In eg a ion in he Eas
A ican Communi y ................................. 86
4 In as uc u e o T ade and S uc u al T ans o ma ion 137
4.1 Mapping A ica’s In as uc u e Po en ial wi h Geospa ial
Big Da a and Causal ML .............................. 138
4.2 Op imal In es men s in A ica’s Road Ne wo k ............... 199
A Acknowledgemen s 285
B Decla a ions 287
Chap e 1
In oduc ion
A ica, a as con inen o 30.4 million km2, home o 1.5 billion di e se peoples in 54 ecognized
s a es, he median o which is 19 yea s o age oday, has g own economically a an a e age a e
o 4.1% be ween 2001 and 2023 and is p ojec ed by he IMF’s Wo ld Economic Ou look (WEO)
o g ow a a es a ound 4.2% in 2024-2029 (K an z (2023b), own calcula ions). This pe o mance
mus be compa ed o g ow h o only 2.1% in 1980-2000. In pe -capi a e ms, he g ow h a e was
∼0% in 1980-2000, ose o ∼1.9% in 2001-2023, and is p ojec ed o emain a ha le el h ough
2029. Thus, A ica is g owing, and A icans a e becoming weal hie . They ha e also become
heal hie and mo e educa ed, wi h an inc ease in li e expec ancy om 54.4 yea s in 2000 o 62
yea s in 2021 and an inc ease in expec ed yea s o schooling om 7.9 o 10.8 (K an z (2023b),
UNDP da a). Despi e a la ge COVID shock educing human de elopmen in 2020 and 2021, hese
long- e m inc eases in human capi al con ibu e o sus aining u u e pe -capi a g ow h.
Ano he eason o assume mo e sus ained g ow h in A ica a e he enhanced business condi ions
in many coun ies, suppo ed by a long p ocess o mac oeconomic s abiliza ion, i.e., a pe sis en
decline in he ola ili y o eal pe -capi a g ow h and in la ion a es wi hin A ican economies. The
i s pape in his disse a ion (K an z,2023a) documen s his A ican G ea Mode a ion in key
mac oeconomic agg ega es and in es iga es i s co ela es wi hin and ac oss coun ies. The indings
sugges ha concu en global mode a ion, imp o ed mac oeconomic policy amewo ks/economic
ins i u ions, and domes ic inancial deepening ha e con ibu ed owa ds mac oeconomic s abiliza ion
in A ica. Classical s uc u al change, which inc eased he sha e o he mo e s able se ice sec o ,
and changes in ag icul u al echnologies, inducing a decline in ag icul u al ou pu ola ili y, also
played a ole. The mo e s able mac oeconomic en i onmen inspi es g ea e business con idence
and mo e secu e p i a e in es ing in A ica. I s p ese a ion and o i ica ion, despi e high public
deb le els, is an essen ial ing edien owa ds success ul economic ans o ma ion on he con inen .
Ye , wi h a high annual popula ion g ow h o cu en ly 2.3% and a o al popula ion p ojec ion
o each 2.5 billion by 2050, g ea e economic impe us and ans o ma ion a e needed o gene a e
signi ican income inc eases ha can only be suppo ed by complex economic ac i i ies.
Such ans o ma ion has long been called o . ”A ica mus uni e” is he man a and i le o
a 1963 book by Kwame Nk umah (Nk umah e al.,1963), he pan-A icanis i s p ime minis e
o Ghana, an A ican champion in in as uc u e and indus ializa ion, and a co- ounde o he
O ganiza ion o A ican Uni y (OAU) es ablished in he same yea . Commemo a ing he OAU,
in 2013, i s successo o ganiza ion, he A ican Union (AU), and A ican heads o s a e signed
he 50 h Anni e sa y Solemn Decla a ion1, e-dedica ed A ica owa ds he a ainmen o he
Pan A ican Vision o ”An in eg a ed, p ospe ous and peace ul A ica, d i en by i s own ci izens,
ep esen ing a dynamic o ce in he in e na ional a ena”. Wi h Agenda 20632 he AU o mula ed
a conc e e mani es o owa ds he a ainmen o his ision wi hin 50 yea s, guided by 10-yea
implemen a ion plans and 15 lagship p ojec s3including a con inen al ee ade a ea (A CFTA),
A ican commodi ies s a egy, single ai anspo ma ke (SAATM), in eg a ed high-speed ain
ne wo k, he G and Inga Dam, a pan-A ican E-ne wo k and E-uni e si y, an A ican economic
o um, A ican inancial ins i u ions, and ee mo emen o people.
As emphasized, key lagship p ojec s e ol e a ound ad ancing con inen al economic in eg a ion
and building he necessa y in as uc u e. Wi h he en y in o o ce o he A ican Con inen al
F ee T ade Ag eemen (A CFTA) in May 2019 and i s a i ica ion by 47 A ican s a es as o July
2024, a signi ican lagship p ojec has been ins iga ed. Wo ld Bank Es ima es sugges ha by
2035, A CFTA will boos A ican incomes by 7% (450B USD), o al A ican expo s by 29%, and
inne -A ican expo s by 81% (Wo ld Bank G oup,2020). Ye , ading unde he ag eemen has
s a ed sluggishly. Cu en ly, only 8 coun ies ade a hand ul o p oduc s unde he Guided T ade
Ini ia i e, and many a i educ ions a e ou s anding. Coun ies’ hesi a ion o s a ading unde
he ag eemen may pa ly be explained by conce ns abou na ional indus ies and alue addi ion.
Thus, mo e e idence is needed o he op imal u iliza ion o he ag eemen and he planned
o mula ion o an A ican Commodi ies S a egy. Towa ds his end, a de ailed and o wa d-looking
1h ps://au.in /documen s/20130613/50 h-anni e sa y-solemn-decla a ion-2013
2h ps://au.in /en/agenda2063/o e iew
3h ps://au.in /en/agenda2063/ lagship-p ojec s
CHAPTER 1. INTRODUCTION Page 2

analysis o he con inen ’s cu en in eg a ion in o global and egional p oduc ion and ade is
highly in o ma i e. The second pa o his disse a ion conduc s such analysis u ilizing he mos
de ailed da a on he global economy a ailable o da e. No ably, he EMERGING Mul i-Region
Inpu -Ou pu (MRIO) Tables (Huo e al.,2022) co e 245 economies in 135 sec o s, inco po a ing
IO ables o 23 A ican economies ep esen ing 84% o A ican GDP, and sec o al GDP o
27 A ican economies. The i s pape (K an z,2024a), a a he sho wo king pape , analyzes
A ican economic in eg a ion h ough ade, global and egional alue chains (GVCs and RVCs)
and p o ides he con inen al con ex o he second pape . I inds ha inne -A ican ade has
inc eased s eadily and highligh s p ecious s ones and me als, mining (pe oleum), pe ochemicals,
and ood p ocessing as high-po en ial sec o s d i ing RVCs, wi h scope o u he RVC expansion
and deepening. The second pape (K an z,2024d) zooms in on ea lie e o s o egional in eg a ion
and p esen s a igo ous case s udy o global and egional in eg a ion in he Eas A ican Communi y
(EAC). I sugges s ha egional in eg a ion is mo e bene icial han global in eg a ion bu has
dis ibu ional side e ec s, leading o a loss o compe i i eness in smalle manu ac u ing sec o s
and a ou ing he egional manu ac u ing hegemon (Kenya). Hence, A ican coun ies should
align domes ic indus ial s a egies wi h ade libe aliza ion and deepe engagemen in ading
unde A CFTA and join ly s i e o indus ial policy coo dina ion.
Con inen al ade and compe i i e p oduc ion and alue chains in A ica will also equi e
signi ican in es men s in in as uc u e, including oads, ailways, and po s o anspo a ion,
powe , communica ions, and educa ion o p oduc ion, as well as heal h and public se ices o
he gene al well-being o wo ke s. Howe e , many A ican go e nmen s a e unde inancial s ain
o in es in in as uc u e p ojec s, wi h GDP-weigh ed a e age gene al go e nmen g oss-deb
le els a 64%. The A ican De elopmen Bank es ima es sugges ha A ica’s in as uc u e needs
amoun o
$
130-170 billion (2018 USD) a yea , wi h a inancing gap in he ange o
$
68-108
billion (A ican De elopmen Bank,2018). In e na ional e o s such as he EU’s Global Ga eway
Ini ia i e ha e so a also ailed o gene a e signi ican in as uc u e in es men s, and Chinese
in es men s ca y high in e es a es. This implies a igh public esou ce alloca ion p oblem bo h
ac oss in as uc u e sec o s and ac oss space, e.g., whe e exac ly do addi ional oads, powe lines,
schools, communica ions owe s, e c., gene a e he highes economic e u ns? Ve y li le e idence
exis s o he spa ial dimension o he esou ce alloca ion p oblem, le alone bo h dimensions
combined.
To p o ide e idence add essing hese challenges, he hi d pa o his disse a ion collec s and
analyzes e y de ailed geospa ial da a on in as uc u e, economic ac i i y, and household wel a e.
The i s pape (K an z,2024b) builds a g anula A ica In as uc u e Da abase4comp ising
∼15.1 million indi idual objec s (places o in e es ) ca ego ized in o 47 economic ca ego ies (26
simpli ied ones) and ∼4.4 million km o ne wo k in as uc u e (mainly oads, wa e ways, powe
lines, and ailways). I join ly analyzes his da a wi h nonpa ame ic and ML me hods, ocusing
on unco e ing spa ial pa e ns in in as uc u e alloca ion and complex ela ionships be ween
in as uc u e and household wel a e. I hen employs no el ’causal ML’ me hods o es ima e
he ma ginal e ec s o di e en ypes o in as uc u e on household wel a e, bo h o e all and
spa ially, ollowed by an examina ion o he co ela es and spa ial pa e ns o hese ma ginal e ec s.
Spa ial analysis is done a 9.7km esolu ion o >100,000 popula ed loca ions in A ica. Findings
imply ha he local bene i s o addi ional in as uc u e a e highly a iable and con ex -speci ic.
The esul s b oadly sugges ha ’ha d in as uc u e,’ such as pa ed oads, powe , anspo ,
and communica ions, is mo e bene icial in ci ies, whe eas ’social in as uc u e,’ such as educa ion,
heal h, public se ices, and u ili ies, is mo e c i ical in u al a eas. Ma ke access and agglome a ion
e ec s a e impo an o ces go e ning hese e u ns. Desc ip i e analysis u he e eals ha
in as uc u e in A ica is concen a ed in u ban a eas and o en ine icien ly alloca ed. A ican
ci ies exhibi ma ked he e ogenei y in in as uc u e, public se ices, and economic ac i i ies.
P edic i e ML models sugges ha oads a e he o e all mos signi ican in as uc u e p edic o
o household wel a e in A ica. Many policy epo s also highligh he economic signi icance o oads
in A ica, e.g., he A ican Economic Ou look 2024 (A ican De elopmen Bank,2024) es ima es
ha anspo a ion ( oads) accoun s o 72.9% o he es ima ed in as uc u e inancing needs
4A ailable a h ps://d i e.google.com/d i e/ olde s/1hpROhpjQ3UHzOTY zPwnJdEs5dpZP584?usp=sha ing
CHAPTER 1. INTRODUCTION Page 3
un il 20305, and a Wo ld Bank epo (Fos e & B ice˜no-Ga mendia,2010) no es a low pa ed oad
densi y o 31 pa ed oad km pe 100km2o land in A ica compa ed o 134km in o he low-income
coun ies and u ges Sub-Saha an A ican coun ies o spend 1% o GDP on oads. Roads a e also
he mos complex in as uc u e, connec ing people and loca ions a he han p o iding a local
se ice. ML app oaches only obse ing local quan i ies o oads a e hus no su icien o analyze
hei ma ginal bene i s, le alone he bene i s o g ea e in es men s ac oss mul iple loca ions.
This mo i a ed an addi ional pape add essing A ica’s oad ne wo k and spa ially op imal
in es men s in o i (K an z,2024c). Using a ou ing engine o compu e 144 million ou es be ween
>12,000 loca ions ∼50km apa enables a p ecise app aisal o he oad ne wo k’s local and global
e iciency. The pape hen conside s 447 ci ies wi h mo e han 100,000 people and 53 in e na ional
po s and de i es a ne wo k g aph om as es ca ou es be ween hem, comp ising 315,000km
o anspo oads. On his ne wo k, i cha ac e izes bo h ma ke access and wel a e-maximizing
in es men s, including cos -bene i analysis o indi idual links and la ge in es men packages.
Impo an ly, i also examines he e ec s o c oss-bo de ic ions on ma ke access and op imal
oad in es men s and akes in o accoun ade h ough po s. Findings imply ha c oss-bo de
ic ions and ade elas ici ies signi ican ly shape op imal oad in es men s. Reducing ic ions
yields he g ea es bene i s, ollowed by oad upg ades and new cons uc ion. Sequencing ma e s,
as educed ic ions gene ally inc ease in es men e u ns. Re u ns o upg ading key oads/links a e
la ge, e en unde ic ions. Due o he combina ion o mul iple kinds o da a in o a uni ied spa ial
amewo k, including no el s a egies o de elop accu a e ne wo k ep esen a ions, cos -bene i
analysis, analysis o bo de ic ions, and he use o quan i a i e spa ial models wi h endogenous
in as uc u e, his pape is he la ges and mos sophis ica ed wo k included in his disse a ion.
No ably, i is able o connec local oad in es men s wi h con inen al economic ou comes and
compu es economically op imal spa ial oad in es men alloca ions a con inen al scale.
In summa y, his disse a ion includes ou and a hal a icles ha expound, in g ea de ail,
on c i ical elemen s o A ica’s economic ans o ma ion, suppo ed by ambi ious da a collec ion
and empi ical igou . These a icles indi idually con ibu e o di e en economic li e a u es on
mac oeconomic mode a ion, global and egional alue chains, economic e u ns o in as uc u e
in es men s, and spa ially op imal in as uc u e in es men s, as elucida ed u he in he espec i e
a icles. A common con ibu ion is ha all a icles a e, in se e al espec s, he mos de ailed and
da a-in ensi e examina ions o he A ican con ex wi hin hei espec i e li e a u es. They also
include se e al me hodological ad ances; in pa icula , he pape s on in as uc u e de elop and
combine me hodologies o join ly examine e y ich and he e ogeneous geospa ial in as uc u e
da a in ela ion o local and con inen al economic ou comes. My hope is ha hese wo ks o
economic li e a u e no only con ibu e o a deepe academic unde s anding o he con inen bu
also inspi e conc e e policies and, in he case o he in as uc u e wo k, echnological solu ions
and app oaches o u ilize g anula geospa ial da a o economic in as uc u e policymaking.
5See also blog pos a h ps://www.a db.o g/en/news-and-e en s/scaling- inancing-key-accele a ing-a icas-
s uc u al- ans o ma ion-73244
CHAPTER 1. INTRODUCTION Page 4
Re e ences
A ican De elopmen Bank. (2018). A ica’s in as uc u e: G ea po en ial bu li le impac on
inclusi e g ow h. A ican Economic Ou look. Re ie ed om h ps://www.a db.o g/ ileadmin/
uploads/a db/Documen s/Publica ions/A ican Economic Ou look 2018 - EN.pd
A ican De elopmen Bank. (2024). D i ing a ica’s ans o ma ion: The e o m o he global
inancial a chi ec u e. A ican Economic Ou look. Re ie ed om h ps://www.a db.o g/en/
documen s/a ican-economic-ou look-2024
Fos e , V., & B ice˜no-Ga mendia, C. (2010). A ica’s in as uc u e: a ime o ans-
o ma ion. Wo ld Bank. Re ie ed om h ps://documen s1.wo ldbank.o g/cu a ed/en/
246961468003355256/pd /521020PUB0EPI1101O icial0Use0Only1.pd
Huo, J., Chen, P., Hubacek, K., Zheng, H., Meng, J., & Guan, D. (2022). Full-scale, nea eal- ime
mul i- egional inpu –ou pu able o he global eme ging economies (EMERGING). Jou nal o
Indus ial Ecology,26(4), 1218–1232.
K an z, S. (2023a, 12). A ica’s G ea Mode a ion. Jou nal o A ican Economies,33(5), 515-537.
Re ie ed om h ps://doi.o g/10.1093/jae/ejad021 doi: 10.1093/jae/ejad021
K an z, S. (2023b). The Kiel Ins i u e A ica Moni o . Kiel Ins i u e o he Wo ld Economy.
Re ie ed om h ps://a icamoni o .i w-kiel.de/
K an z, S. (2024a). A ica’s egional and global in eg a ion. SSRN Wo king Pape . Re ie ed
om h ps://ss n.com/abs ac =4929189
K an z, S. (2024b). Mapping A ica’s in as uc u e po en ial wi h geospa ial big da a and causal
ML. Kiel Wo king Pape (2276). Re ie ed om h ps://www.i w-kiel.de/publica ions/mapping
-a icas-in as uc u e-po en ial-wi h-geospa ial-big-da a-and-causal-ml-31834/
K an z, S. (2024c). Op imal in es men s in A ica’s oad ne wo k. Kiel Wo king
Pape s(2272). Re ie ed om h ps://www.i w-kiel.de/publica ions/op imal-in es men s-in
-a icas- oad-ne wo k-33157/
K an z, S. (2024d). Pa e ns o global and egional in eg a ion in he eas a ican communi y.
Re iew o Wo ld Economics, 1–80. Re ie ed om h ps://doi.o g/10.1007/s10290-024-00558-0
Nk umah, K., A igoni, R., & Napoli ano, G. (1963). A ica mus uni e. Heinemann London.
Wo ld Bank G oup. (2020). The A ican con inen al ee ade a ea: Economic and dis ibu ional
e ec s. Washing on, DC: Wo ld Bank. Re ie ed om h ps://www.wo ldbank.o g/en/ opic/
ade/publica ion/ he-a ican-con inen al- ee- ade-a ea
CHAPTER 1. INTRODUCTION Page 5
Chap e 2
Mac oeconomic S abiliza ion
Iden i y gi es he a iance o a sum o andom a iables,6 hus aking he a iance o Eq. 2yields
a (%∆Y) = X
k∈KX
j∈K
co ∆yk
Y( −1)
,∆yj
Y( −1) ≈X
k∈KX
j∈K
¯
θk¯
θjco (%∆yk,%∆yj),(3)
whe e ¯
θk=1
T−1PT
=2 θk is he a e age lagged ou pu sha e o sec o ko e he obse ed pe iod
T. I he e is no s uc u al change du ing he pe iod o obse a ion (θk =¯
θk∀ ∈T), he igh
side o Eq. 3becomes an iden i y as well.
Compu ing Eq. 3o e he en i e 1990-2019 pe iod (Table C6), con i ms ha ag icul u e is he
mos ola ile sec o , ollowed closely by indus y. The co a iances a e nega i e and signi ican ly
smalle , wi h he la ges ela ionship be ween ag icul u e and indus y and he smalles be ween
ag icul u e and se ices. These pa e ns a e b oadly p ese ed when conside ing con ibu ions
o agg ega e ola ili y. Accoun ing wi h obus es ima es om Table C6 yields ha agg ega e
ola ili y in he median A ican coun y is composed o 23.6% ag icul u e, 31.7% indus y, and
59% se ices ola ili y, and hei sum is educed -14.6% by nega i e co a iances.
I now conside he change in agg ega e pe -capi a g ow h ola ili y ∆ a (%∆Y)τ, compu ed
be ween wo pe iods τ1= 1990 −2004 and as τ2= 2005 −2019. Following Eq. 3, his equals
he sum o changes in he a iances and co a iances o he sec o al con ibu ions o agg ega e
g ow h. To gua d agains ou lie s, I also use a comedian-based es ima e7as a obus al e na i e
o he classical es ima o . Ano he me hodological ambigui y ega ds agg ega ion. Sec o al sha es
can ei he be compu ed a he coun y le el be o e agg ega ion o a e agg ega ing he ola ili y
di e ences ac oss coun ies. One would hink he o me is be e , bu he da a o some coun ies
is o e y poo quali y. I hus implemen bo h app oaches and only epo coun y-le el sha es
agg ega ed ac oss coun ies using he median. Toge he wi h he choice o co a iance es ima o ,
his leads o 6 di e en es ima ion s a egies, epo ed in Table 4.
Table 4shows ha he educ ion in ola ili y ∆ a (%∆Y)τis due o bo h educ ions in
sec o al a iances and sec o al co a iances.8The esul s di e a bi depending on he me hodology:
es ima es in ol ing Pea son’s co a iance gene ally sa is y he equa ion much mo e closely bu a e
also mos a ec ed by ou lie s. To gene a e a ep esen a i e es ima e summa izing he exe cise, I
compu e he median ac oss all 6 s a egies and epo i in he inal ow o Table 4.
Table 4: Sec o al Con ibu ion o Mode a ion in GDP Vola ili y
Co Es AggFun Fi ∆ a (%∆Y)τAGR IND SRV Pco jk 2AI 2AS 2IS
Sec o al Sha es Compu ed A e Agg ega ion
Pea son Mean 100% -16.18 35% 3% 48% 13% 28% -6.2% -8.5%
Comedian Mean 95% -6.53 49% -24% 37% 38% 9.7% 4.9% 24%
Pea son Median 62% -5.82 29% 0.84% 40% 31% 2.5% 11% 17%
Comedian Median 29% -1.25 45% 0.71% 18% 37% 7.9% 8.7% 20%
Sec o al Sha es Compu ed Be o e Agg ega ion
Pea son Median 100% -5.82 23% 14% 46% 17% 3.9% 3.5% 9.6%
Comedian Median 68% -1.25 20% 23% 31% 26% 5.9% 14% 6.8%
Median o 6 Es ima es: 81% -5.82 32% 1.9% 38% 28% 6.9% 6.8% 13%
No es: The ’Fi ’ column signi ies how closely Eq. 3is sa is ied. Columns AGR, IND, and SRV gi e he sec o al con ibu ion o he
agg ega e ola ili y educ ion in pe cen age e ms, and Pco jk gi es he combined con ibu ion o all co a iance e ms, which a e
also indi idually b oken down in columns 2AI, 2AS, and 2IS. Es ima es di e depending on he co a iance es ima o , agg ega ion
unc ion, and whe he sha es a e compu ed be o e o a e agg ega ion. The bo om ow shows he median o all 6 epo ed es ima es.
6
a (X
k
akyk) = X
i∈K
X
j∈K
aiajco (yi, yj) = X
k
a2
k a (yk)+2X
1≤i
X
<j≤K
aiajco (yi, yj).
7The comedian is de ined as com(X, Y ) = med((X−med(X))(Y−med(Y))). I is no s ic ly a obus co a iance
es ima o as i does no p ese e he ela i e magni ude o a iances and co a iances. The implemen a ion a ailable
in R package obus base (Maechle e al.,2021) howe e uses he comedian o es ima e he co a iance, applying
app op ia e co ec ions.
8Mos ly exp essed h ough al eady nega i e co a iances becoming mo e nega i e, so no a co a iance educ ion
in absolu e e ms.
7
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 13

The ou come sugges s ha 32% o he agg ega e educ ion in pe capi a g ow h ola ili y
be ween τ1and τ2was accoun ed o by ag icul u e, 38% by se ices, and 28% by a educ ion in
he co a iances, o which, abb e ia ing sec o s by hei i s le e , a ound 7% a e accoun ed o
by AI and AS, and 13-14% by IS. The idiosync a ic educ ion in indus ial ola ili y only accoun s
o 1.9% o he agg ega e educ ion. The esul s hus con i m a mo e han p opo ional ole o
ag icul u e in he A ican Mode a ion bu also signi y a shi owa ds g ea e sec o al independence,
o a he , sec o al subs i u abili y.9
A sho coming o he esul s o Table 4, based on he le side o Eq. 3, is ha hey include
he e ec s o s uc u al change. To examine he con ibu ion o s uc u al change in isola ion,
I u he de elop he igh side o Eq. 3and decompose changes in agg ega e ola ili y in o
changes in sec o al ola ili ies and changes in sec o al p oduc ion sha es. This is mo i a ed by he
conside a ion ha he se ice sec o is subs an ially less ola ile han ag icul u e and indus y,
and he sha e o se ices in A ican GDP has been inc easing; hence, a quan i iable ac ion o he
A ican Mode a ion mus be a di ec consequence o s uc u al change. This ype o decomposi ion
is well-known in he s uc u al change li e a u e. McMillan e al. (2014) decompose changes in
agg ega e labo p oduc i i y as10
∆LP = ∆ X
k
θk lpk =X
k
θk, −1∆lpk +X
k
∆θklpk ,(4)
whe e θk, −1∆lpk deno es he sec o al labo p oduc i i y changes weigh ed by sec o sha es a
he beginning o he pe iod, and ∆θklpk deno es he changes in sec o al sha es weigh ed by inal
pe iod p oduc i i y le els. Applying Eq. 4 o he igh side o Eq. 3yields
∆ a (%∆Y)τ≈X
k∈KX
j∈K
¯
θkj,τ−1∆co (%∆yk,%∆yj)τ+X
k∈KX
j∈K
∆¯
θkj,τ co (%∆yk,%∆yj)τ,(5)
whe e τ= ( , . . . , +N−1)′, N ∈2, . . . , T deno es a ime-window o size No e which he
co a iance is compu ed, and ¯
θkjτ =1
N−1PN−1
i=1 θk, +i×PN−1
i=1 θj, +i∀k, j deno es he p oduc
o he a e age sec o al sha es. The i s weigh ed sum o co a iances in Eq. 5 hus cap u es changes
in agg ega e ola ili y esul ing om changes in ola ili y wi hin sec o s and he second changes
due o he shi ing o alue-added be ween sec o s a di e en le els o ola ili y. I es ima e Eq. 5
conside ing again a single di e ence be ween pe iods τ1and τ2, using bo h classical and comedian
es ima o s, and compu e sha es be o e and a e agg ega ing ac oss coun ies using he median.
Table 5 epo s he esul s. Columns ’Wi hin’ and ’Be ween’ gi e he median alue o he
espec i e componen s in Eq. 5, ans o med in o sha es be o e agg ega ion i ’T ans = Sha e’.
’Fi ’ indica es how closely Eq. 5is sa is ied, and columns ’Wi hin/Sum’ and ’Be ween/Sum’ p o ide
he pe cen age sha es o he wo componen s in hei sum i.e. ela i e o he o e all i , as in Table
4. I ’T ans = Sha e’, hese columns a e also compu ed be o e agg ega ion. Table 5shows ha in
he median coun y, s uc u al change explains be ween 3% and 5.6% o he agg ega e educ ion
in pe -capi a g ow h ola ili y in τ1→τ2. This esul is obus ac oss me hodological choices.
A conce n may be ha he 3-sec o se up is oo b oad o quan i y he e ec s o s uc u al
change on agg ega e ola ili y. Thus, I also epea he exe cise wi h a mo e de ailed da ase used
in he s uc u al change li e a u e: he Economic T ans o ma ion Da abase (K use e al.,2023)
p o ides a disagg ega ion in o 12 sec o s o 21 A ican coun ies o e he pe iod 1990-2018. I can
hus also be spli be ween τ1and τ2. The bo om hal o Table 5 epo s he esul s, indica ing
ha e en wi h a ine sec o al disagg ega ion, he con ibu ion o pu e s uc u al change o A ican
mode a ion is smalle han 5%.
9Implied by an obse ed median inc ease in he nega i e co a iance be ween AI and AS, and a shi om a small
posi i e IS co a iance in τ1 o a small nega i e co a iance in τ2.
10Equa ion 4is de i ed as: ∆LP =Pk∆(θk lpk ) = Pk(θk lpk −θk, −1lpk, −1) = Pk(θk lpk −θk, −1lpk, −1+
θk, −1lpk −θk, −1lpk ) = Pk(θk, −1∆lpk + ∆θklpk ).
8
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 14
Table 5: S uc u al Change and A ican Mode a ion: `a la McMillan e al. (2014)
Co Es T ans ∆ a (%∆Y)τWi hin Be ween Fi Wi hin/Sum Be ween/Sum
Wo ld Bank Da a (3-Sec o s, 41 Coun ies)
Classical None -5.823 -2.390 -0.141 43.5% 94.4% 5.58%
Classical Sha e -5.823 0.958 0.019 102% 97.0% 2.98%
Comedian None -1.669 -2.506 -0.147 159% 94.5% 5.53%
Comedian Sha e -1.669 0.757 0.045 88.7% 96.9% 3.07%
Economic T ans o ma ion Da abase (12-Sec o s, 21 Coun ies)
Classical None -6.304 -5.002 -0.002 79.4% 100% 0.03%
Classical Sha e -6.304 0.993 0.001 105% 98.1% 1.85%
Comedian None -2.775 -3.413 -0.111 127% 96.8% 3.16%
Comedian Sha e -2.775 0.969 0.037 113% 96.9% 3.07%
No es: The decomposi ion is compu ed a he coun y le el o 41 A ican coun ies acco ding o Eq. 5, compa ing
1990-2004 o he 2005-2019 pe iod. The wo componen s a e u ned in o sha es i ’T ans = Sha e’, and agg ega ed
ac oss coun ies using he median. Fu he desc ip ions o he columns a e p o ided in he main ex abo e. 13 coun ies
wi h less han 10 obse a ions o any sec o al g ow h a e in ei he pe iod we e excluded: Alge ia, Angola, he Cen al
A ican Republic, Djibou i, Equa o ial Guinea, E i ea, Kenya, Libe ia, Libya, Madagasca , Somalia, Sou h Sudan, and
S˜ao Tom´e & P ´ıncipe. The ETD o K use e al. (2023) eco ds 12 sec o s o 21 A ican coun ies: BFA, BWA, CMR,
EGY, ETH, GHA, KEN, LSO, MAR, MOZ, MUS, MWI, NAM, NGA, RWA, SEN, TUN, TZA, UGA, ZAF, ZMB.
A ela ed exe cise, es ablished by S ock & Wa son (2002) ixes he sec o al sha es bu main ains
he sec o al g ow h a es o gene a e a pseudo-ou come GDP g ow h se ies e lec ing he absence
o s uc u al change. Following hei me hod, I gene a e wo pseudo-ou come se ies wi h sec o al
sha es ixed a hei a e age in pe iods τ1and τ2, espec i ely. I hen compu e a ious measu es
o ola ili y σ( a iance, IQR, and MAD) ac oss he wo pe iods and se ies, yielding wo ac ual
(app oxima ely) and wo coun e ac ual ola ili y es ima es. Following S ock & Wa son (2002), I
compu e ψ= ([στ2
τ2−στ1
τ2]+[στ2
τ1−στ1
τ1])/2, whe e στ1
τ2is he ola ili y o GDP in pe iod τ2, compu ed
using he sec o al sha es o pe iod τ1. The s a is ic ψ hus es ima es he change in ola ili y due
o a change in sha es by a e aging bo h possible ways o conduc ing he coun e ac ual exe cise:
compa ing ac ual ola ili y in τ2 o ola ili y in τ2i he sec o al sha es a e hose o τ1, and
compa ing ola ili y in τ1wi h he sha es o τ2 o ac ual ola ili y o τ1. Fu he , I compu e
∆σ=στ2
τ2−στ1
τ1as he ac ual (app oxima e) change in GDP ola ili y. Table 6 epo s he esul s.
Table 6: S uc u al Change and A ican Mode a ion: `a la S ock & Wa son (2002)
S a is ic AggFun στ1
τ1στ2
τ1στ2
τ2στ1
τ2∆σ ψ 100ψ
∆σ
Wo ld Bank Da a (3-Sec o s, 41 Coun ies)
Va iance T immedMean (10%) 21.64 26.28 12.00 12.99 -6.67 2.39 -1.76
Va iance Median 17.83 19.40 6.94 7.44 -3.20 0.75 2.20
IQR T immedMean (10%) 4.62 4.73 3.29 3.45 -1.23 -0.04 9.19
IQR Median 3.90 4.21 3.26 3.21 -0.54 -0.01 5.11
MAD T immedMean (10%) 2.28 2.48 1.70 1.78 -0.54 0.03 -6.64
MAD Median 2.05 2.24 1.77 1.69 -0.30 -0.02 2.69
Economic T ans o ma ion Da abase (12-Sec o s, 21 Coun ies)
Va iance T immedMean (10%) 15.22 19.29 6.17 4.93 -9.10 1.58 0.97
Va iance Median 8.36 10.52 5.06 4.35 -4.25 0.70 6.49
IQR T immedMean (10%) 4.17 4.40 2.74 2.46 -1.27 0.17 3.98
IQR Median 3.72 3.51 2.29 2.54 -1.01 0.03 3.51
MAD T immedMean (10%) 2.23 2.21 1.42 1.13 -0.72 0.10 5.32
MAD Median 2.01 2.04 1.35 1.12 -0.46 0.05 8.76
No es: Decomposi ion o GDP ola ili y by ixing sec o al sha es and gene a ing coun e ac ual se ies as in
S ock & Wa son (2002). στ1
τ2is he ola ili y o GDP in pe iod τ2=2005-2019, compu ed using he sec o al
sha es o pe iod τ1=1990-2004. ψ= ([στ2
τ2−στ1
τ2]+[στ2
τ1−στ1
τ1])/2 es ima es he a e age ola ili y change due
o s uc u al change ac oss he wo pe iods. ∆σ=στ2
τ2−στ1
τ1es ima es he ac ual (app oxima e) change in
GDP ola ili y. Resul s a e agg ega ed ac oss coun ies using ei he he median o a immed mean, emo ing
10% o he obse a ion om bo h sides.
9
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 15
O e all, he esul s o Table 6a e e y simila o hose o Table 5. The coun e ac ual ola ili y
es ima es a e e y close o he ac ual ones, con i ming ha mac oeconomic mode a ion in A ica
was la gely a wi hin-sec o phenomenon. The pe cen age sha e o he mode a ion a ibu able
o s uc u al change by his me hod, epo ed in he inal column o Table 6, anges be ween
-6.6 and 9.2 %. The analysis p esen ed in Tables 4 h ough 6 hus es ablishes ha up o 30%
o he agg ega e A ican Mode a ion o eal pe -capi a g ow h a es is due o changes in he
co a iance owa ds g ea e sec o al independence/subs i u ion, and ∼5% can be a ibu ed o pu e
s uc u al change, wi h less ola ile sec o s like se ices becoming economically mo e impo an .
The emaining 65-70% is due o o he ac o s ha mos ly a ec ed ag icul u e and se ices. As
no ed by S ock & Wa son (2002), he inc ease in se ices employmen could, h ough mo e s able
incomes and demand, also ha e s abilizing e ec s on o he sec o s, which is no cap u ed by hese
decomposi ions. The decomposi ions migh hus unde s a e he ue gene al equilib ium e ec s o
s uc u al change on ou pu s abili y.
In he g ea mode a ion li e a u e, a equen ly men ioned d i e in ad anced economies (Mc-
Connell & Pe ez-Qui os,2000;Blancha d & Simon,2001;Ho an,2006) is g ea e p oduc ion
e iciency h ough in en o y managemen inno a ions. Gi en he small ole o he indus y sec o in
A ica’s mode a ion, he esul s sugges ha in en o y managemen is unlikely o be an impo an
d i e .
Be o e examining o he ac o s, I alida e hese esul s a he coun y le el and unco e some
he e ogenei y by compu ing he MAD sec o con ibu ion o g ow h, MAD(∆y /Y −1), and sec o al
g ow h ola ili y, MAD(%∆y ), o each coun y. Compu ing MAD(∆y /Y −1) o e he whole
1990-2019 pe iod yields a la ge g oup o 21 coun ies has se ices as he g ea es con ibu o o
agg ega e ola ili y, ollowed by indus y (17) and ag icul u e (13) (see Table C7). This me ic hus
b oadly aligns wi h he pa e n o s uc u al change. Conside ing he sec o al g ow h ola ili y,
howe e , leads o a la ge ealloca ion o coun ies o indus y and ag icul u e, wi h 22 coun ies
ha ing he la ges ola ili y in ag icul u e and 28 in indus y, and only 1 coun y (Gabon) in he
se ices ca ego y.11 Table C8 summa izes bo h me ics o τ1and τ2, and Figu e C16 isualizes
he mo emen in MAD(∆y /Y −1) o all coun ies, indica ing a nea ly ubiqui ous and la ge s abi-
liza ion o ag icul u e, as well as a sizeable s abiliza ion o se ices in mos coun ies. In indus y,
he de elopmen s a e e y he e ogeneous, wi h some coun ies like Ghana expe iencing g ea e
ola ili y and o he s like Sou h A ica expe iencing signi ican s abiliza ion. I is also in e es ing
o compa e di e en egions in A ica. Figu e C17 and Table C10 p o ide a egional summa y o
sec o al ola ili y and show ha all egions apa om sou he n A ica expe ienced a sizeable s a-
biliza ion in ag icul u e. O e all, Eas e n A ica expe ienced he la ges s abiliza ion in agg ega e
ou pu , ollowed by Middle and Wes e n A ica.
Disagg ega ed analysis hence con i ms he esul s o agg ega e analysis, indica ing ha s a-
biliza ion o ag icul u e and se ices was a sha ed expe ience o mos A ican coun ies since
1990. The e is mode a e egional he e ogenei y, wi h he mo e de eloped egions o No he n
and Sou he n A ica being a ec ed less. Appendix B epo s a simila decomposi ion om he
expendi u e side o GDP, indica ing a decline in he ola ili y o all expendi u e componen s,
especially consump ion, in es men , and expo s.
4 Ex e nal, Financial, and Policy Fac o s
Since ∼70% o he A ican Mode a ion canno be explained by s uc u al change o changes in
sec o al co a iances, he emainde o he pape examines o he con ibu ing ac o s, including
he ex e nal economic en i onmen aced by A ican economies, changes in he inancial sec o ,
mac oeconomic policies, and o he changes in economic o ins i u ional s uc u e (Sec ion 5).
Se e al pape s in es iga e he g ea mode a ion in he US and o he ad anced economies (AE)
along simila lines. Ho an (2006) s udies he educ ion o ou pu ola ili y in AE, ocussing on he
compe ing explana ions o be e mone a y policy, mo e e icien in en o y in es men o i ms,
and lowe exposu e o global shocks (oil p ice shocks). He inds ha , due o he di e en onse s
11See he bo om hal o Table C7.
10
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 16
o ou pu mode a ion in AE, only he o me wo p o ide c edible explana ions o he g ea
mode a ion. McConnell & Pe ez-Qui os (2000) and Blancha d & Simon (2001) also ind e idence
ha mo e coun e cyclical in en o y managemen has con ibu ed o he s abiliza ion o business
cycles in he US. Ahmed e al. (2004) a ibu es o mone a y policy a ole in b inging down US
in la ion ola ili y. Schmid -Hebbel (2009) discusses causes o he g ea mode a ion in eme ging
ma ke s and de eloping economies (EMDE), men ioning s onge policies and be e ins i u ions
(especially p ope y igh s, go e nance and accoun abili y, and cen al bank independence) as
d i e s. He documen s ha he adop ion o in la ion- a ge ing mone a y policy (IT) in EME
was associa ed wi h educed domes ic in la ion and exchange a e pass- h ough. Many de eloping
coun ies also adop ed mo e sus ainable iscal policies. In Sou h A ica, Bu ge (2008) p o ides
e idence ha be e mone a y policy and a mo e e icien inancial sec o b ough down ola ili y
in he 90s, bu in en o ies did no . Du Plessis & Ko z´e (2010) also no e ha a less ola ile
in e na ional en i onmen ollowing Sou h A ican libe aliza ion in he la e 80s and he g ea e
poli ical s abili y du ing he pos -apa heid la e 90s enhanced mac oeconomic s abili y in Sou h
A ica.
In he bulk o A ican economies, li le is known abou in en o y managemen p ac ices,
bu I ha e a gued agains i based on he small con ibu ion o he indus ial sec o o A ican
mode a ion. I will also a gue agains IT as a d i e o mode a ion in A ica. The e ha e, howe e ,
been no able changes in he ex e nal en i onmen aced by A ican economies in his ime ame,
e lec ed in be e e ms o ade (ToT), lowe ex e nal deb bu dens, highe in lows o FDI and
emi ances, as well as lowe ola ili y o me chandise ade, FDI and emi ance in lows. The e
has also been a g adual p ocess o inancial deepening, e lec ed in b oad money, c edi o he
p i a e sec o , na ional sa ings, and ese e asse s. Finally, he e ha e been changes owa d a
mo e s able exchange a e policy and an inc eased adop ion o iscal ules.
4.1 Ex e nal En i onmen
Reduced ola ili y may pa ly esul om a mo e a o able ex e nal en i onmen aced by A ican
economies, pe mi ing bo h s onge g ow h and mo e long- e m economic planning and in es -
men s. I is no able om Figu e 1 ha g ow h a es peaked sho ly a e 2010, which, hanks o
he Hea ily Indeb ed Poo Coun ies (HIPC) Ini ia i e launched in 1996, is also he pe iod when
A ica aced he lowes le els o public and ex e nal deb (see Figu e C18). A ican economies also
expe ienced mo e a o able ToT and highe FDI and emi ance in lows a e 2010.
Table C11 shows co ela ions o 10-yea olling a e ages o hese indica o s wi h olling medians
and MADs o pe -capi a g ow h and in la ion in coun y-s anda dized i s -di e ences. ToT and
FDI a e signi ican ly posi i ely co ela ed wi h g ow h, whe eas public and ex e nal deb a e
s ongly nega i ely ela ed o g ow h. In addi ion, highe ToT and emi ances a e associa ed
wi h lowe g ow h ola ili y and in la ion le els, whe eas g ea e deb s ocks co ela e wi h highe
ola ili y and in la ion. This indica es ha mo e a o able linkages wi h he wo ld could ha e
con ibu ed o he inc eased esilience in A ican eal sec o s.
A less ola ile ex e nal en i onmen may also ha e di ec ly con ibu ed o less ola ile domes ic
ac i i y. Figu e C19 shows ha exchange a e, ToT, and me chandise ade ola ili y ha e d opped
subs an ially o e he sample pe iod, and also FDI and emi ance lows became less ola ile.
Cu en accoun ola ili y also ell a e 2010. Table C12 shows co esponding wi hin-coun y
co ela ions o 10-yea olling ola ili y measu es, indica ing ha highe exchange a e, ToT, FDI,
and emi ance ola ili y a e associa ed wi h lowe g ow h, highe in la ion, and g ea e mac oeco-
nomic ola ili y. Especially exchange a e ola ili y is s ongly co ela ed wi h in la ion.
4.2 Financial Deepening
Ano he sou ce o inc eased esilience in A ica may be domes ic inancial deepening and inc eased
le els o in e na ional ese es o coun e ex e nal shocks. Fo example, Eas e ly e al. (2001)
analyze ola ili y wi h an emphasis on he inancial sec o and cons a e ha c edi cons ain s a e
an impo an sou ce o ola ili y in de eloping coun ies. Figu e C20 indica es ha A ica has
indeed made some p og ess in his di ec ion o e he pas 30 yea s. G oss Na ional Sa ings ha e
inc eased om a ound 14% o GDP in 1990 o a ound 18% in 2019, o al ese es ha e isen o
11
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 17
a ound 100% o ex e nal deb o 5.5 mon hs o impo s o goods and se ices, domes ic c edi o
he p i a e sec o has isen om 17% o 25% o GDP, b oad money om 30% o 40% o GDP, and
banks liquid ese es o asse s a io has isen om <20% o >=25%, a leas when weigh ed by
GDP o popula ion. Table C13 shows he co esponding co ela ions in coun y-s anda dized i s
di e ences, indica ing ha highe na ional sa ings, ese es, domes ic c edi o he p i a e sec o ,
and b oad money co ela e posi i ely wi h economic g ow h and mac oeconomic s abili y.
4.3 Mac oeconomic Policies
Imp o ed domes ic mac oeconomic and inancial policies may also ha e con ibu ed o he A ican
Mode a ion. I only conside he mos impo an s abiliza ion policies: managing in la ion and he
exchange a e, mac op uden ial s ingency, and iscal ules.
In la ion Ta ge ing
A ica s ill has e y ew in la ion a ge e s. Acco ding o he IMF’s Annual Repo on Exchange
A angemen s and Exchange Res ic ions (AREAER) da abase, only 4 coun ies cu en ly a ge
in la ion: Sou h A ica om 2000, Ghana om 2007, Uganda om 2011, and Seychelles om 2019.
Figu e C21 shows he in la ion a es o hese coun ies, indica ing ha he IT egimes we e adop ed
when in la ion had al eady s abilized o le els well below 20%. Thus, he adop ion o IT did no
play a la ge ole in he A ican Mode a ion.
Exchange Ra e A angemen s
To examine he e olu ion o exchange a e egimes, I ake da a om Ilze zki e al. (2019), a ailable
o 53 A ican coun ies. Figu e C22 shows ha he sha e o c awling bands and ee- alling/dual
ma ke s has declined in A ica since 1992, in a o o c awling peg a angemen s. F ee loa s a e
also a e, and since 2005, only Sou h A ica has main ained a loa ing egime.
Table C14 epo s 15-yea olling panel ixed-e ec s eg essions o g ow h and in la ion ola ili y
on he exchange egime dummies, using he ha d peg as a base ca ego y. Resul s imply ha ela i e
o he ha d peg, c awling pegs a e associa ed wi h g ea e g ow h s abili y, whe eas mo e libe al
egimes co ela e wi h less s able g ow h pe o mance. The coe icien s on model (3) wi h coun y
and ime ixed e ec s imply ha a c awling peg is associa ed wi h a 0.86 pe cen age poin (pp.)
dec ease in he MAD o eal pe -capi a g ow h is-a- is he ha d peg a angemen . Fo in la ion,
he ha d peg appea s o be he mos s able egime, bu he coe icien on he c awling peg is
insigni ican , indica ing ha he in la ion cos o swi ching o a c awling peg is mode a e. As
elucida ed by Bleaney e al. (2016), he choice o exchange a e a angemen is endogenous o
mac oeconomic condi ions and policy p io i ies, and educed in la ion has made pegging mo e
a ac i e in ecen yea s. Ne e heless, he shi in exchange a e egimes in A ica since 1990
owa ds c awling pegs and decline in eely- alling and o he ola ile egimes, e idenced by a s ong
decline o exchange a e ola ili y (Figu e C19), has likely con ibu ed o A ica’s mode a ion.
Mac op uden ial Regula ion
Mac op uden ial policy ecei ed inc eased a en ion a e he 2008/09 global inancial c isis. The
IMF adop ed a new Ins i u ional View in 2012, ecognizing he use ulness o mac op uden ial
measu es o mac oeconomic s abili y, pa icula ly in economies wi h less de eloped inancial
ma ke s (A o a e al.,2013;IMF,2017,2022). Figu e C23 shows indices o o al, in low, and
ou low con ols ac oss 18 A ican coun ies, compu ed by a e aging dummies o es ic ions in 10
inancial ma ke s, om Fe n´andez e al. (2016) (Augus 2021 upda e). Mac op uden ial measu es
ha e eased in he 1995-1997 pe iod,12 bu ha e emained qui e s able a ound 0.5 o in low measu es
and 0.625 o ou low measu es a e wa d. I is, he e o e, unlikely ha agg ega e mac oeconomic
mode a ion in A ica is much a ec ed by changes in mac op uden ial policy.
Some con inen -le el de elopmen s eme ge when conside ing es ic ions in he 10 di e en
ma ke s sepa a ely. Figu e C24 shows 10-yea MAs o he o e all s ingency in 18 A ican economies,
12Mainly due o libe aliza ions in E hiopia, Nige ia, Uganda, and Ghana.
12
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 18

indica ing ha bond and gua an ee ma ke s, as well as FDI, ha e become mo e es ic ed in ecen
yea s, whe eas equi y, eal es a e, and comme cial c edi ma ke s ha e become less es ic ed.
Table C15 shows 10-yea olling panel-FE eg essions o he MADs o GDP pe capi a g ow h
and in la ion on agg ega e mac op uden ial s ingency indica o s, indica ing ha mac op uden ial
s ingency is nega i ely associa ed wi h bo h ou pu and in la ion ola ili y. D awing on he
speci ica ion wi h coun y and ime ixed e ec s, an inc ease in o e all mac op uden ial s ingency
by 0.1 is associa ed wi h a 0.54 pp. educ ion in he MAD o pe -capi a g ow h and a 1.22 pp.
educ ion in he MAD o in la ion. Ou low measu es ha e a s onge associa ion wi h ou pu
s abili y, whe eas in low measu es s ongly associa e wi h educed in la ion ola ili y.
Fiscal Rules
A ou h and impo an se o s abiliza ion policies a e iscal ules. Global da a on iscal ules
adop ed since 1985 is a ailable h ough he IMF Fiscal Rules Da ase (Da oodi e al.,2022b,a).
Table 7compac ly summa izes he his o y o iscal ules in A ica.13
Table 7: A Ch onology o Fiscal Rules in A ica
En i y Fi s Rule Expendi u e (ER) Re enue (RR) Budge Balance (BBR) Deb (DR)
Kenya 1997 1997 1997 (2019)
Cape Ve de 1998 1998 1998
WAEMUa2000 2000 (2015) 2000 (2015) 2000 (2015)
Namibia 2001 2010 2001
CEMACb2002 2002 (2008, 2017) 2002
Bo swana 2003 2003 (2006, 2016) 2003 2005
Nige ia 2007 2007
Mau i ius 2008 2008 (2010)
Libe ia 2009 2009
EACc2013 2013 2013
Tanzania 2015 2015 2015
Uganda 2016 2016 2016
Rwanda 2019 2019
Da a Sou ce: Da oodi e al. (2022b). Rule e isions in pa en heses.
aComp ising Benin, Bu kina Faso, Cˆo e D’I oi e, Guinea-Bissau, Mali, Nige , Senegal and Togo
bComp ising Came oon, Cen al A ican Republic, Chad, Republic o Congo, Equa o ial Guinea and Gabon
cComp ising Tanzania, Kenya, Rwanda, Uganda, Bu undi and Sou h Sudan
Mos iscal ules in A ica can be ega ded as weak. Apa om Mau i ius and Bo swana, no
coun y has ins iga ed a o mal en o cemen p ocedu e o na ional ules, and no coun y has an
ex a-go e nmen al body o moni o compliance wi h na ional ules.
To e alua e he ela ionship o iscal ules wi h mac oeconomic s abili y, Table C16 p esen s
10-yea olling eg essions conside ing i s a dummy indica ing he adop ion o any iscal ule,
hen he o al numbe o ules, and inally a se o dummies o he di e en ypes o ules. Adding
bo h coun y and ime- ixed e ec s le s he wi hin R2d op o ze o, indica ing insu icien ime
a ia ion in he iscal ules o con ol o global e en s. The models wi h coun y- ixed e ec s,
howe e show a meaning ul and signi ican nega i e associa ion o iscal ules wi h bo h g ow h
and in la ion ola ili y. When disagg ega ing he se o ules, only he coe icien on he Budge
Balance Rule (BBR) is nega i e and signi ican in he g ow h ola ili y eg ession. The coe icien
size implies ha a BBR is associa ed wi h a ound 1 pp. lowe MAD o g ow h. Fo in la ion, bo h
Re enue Rules and BBRs ha e la ge nega i e coe icien s. Deb Rules (DRs) a e also nega i ely
ela ed o g ow h/in la ion ola ili y, wi h insigni ican coe icien s o 0.23/0.54.
Table C17 u he shows ha he exis ence and numbe o ules implemen ed co ela e posi i ely
wi h he cu en accoun (CAB) and go e nmen budge balance (GBB), and nega i ely wi h he
le el o g oss go e nmen deb (GGD). When disagg ega ing ules, BBRs a e associa ed wi h an
app ox. 4.2 pp. imp o emen in he CAB (in % o GDP), and a 7 pp. imp o emen in he GBB.
DRs appea o be s ongly associa ed wi h GGD, a e ec sizes up o 70-80 pp. lowe deb o GDP.
These coe icien s a e no o be in e p e ed as causal since iscal ules a e o en a commi men
de ice o go e nmen s ha al eady engage in sound mac oeconomic p ac ices. They ne e heless
13Figu e C25 also shows an agg ega e imeline o iscal ules adop ion in A ica by ype and issuing au ho i y.
13
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 19
sugges ha ins iga ing a ule is an e ec i e de ice o hese go e nmen s.
Ha ing conside ed ou di e en ypes o mac oeconomic policies in A ica o e he pas 30
yea s, i appea s ha only he shi owa ds c awling pegs om c awling bands and eely alling
a angemen s and he adop ion o iscal ules in an inc easing numbe o coun ies could ha e
con ibu ed o he la ge mac oeconomic mode a ion in g ow h and in la ion. In la ion- a ge ing
mone a y policy has only been aken up by ou coun ies a a poin when hei in la ion le els
we e al eady low and s able, and mac op uden ial policies, while po en ially e ec i e in cu bing
mac oeconomic ola ili y, show no agg ega e end o e mos o he pe iod unde conside a ion.
This assessmen is, o cou se, incomple e. Fo example, i is possible ha cen al banks ha e
become mo e e ec i e in a ge ing mone a y agg ega es wi hou shi ing o in la ion a ge ing o
ha many coun ies ha e un inancial sec o , ade, o ag icul u al policies ha con ibu ed o
mac oeconomic s abili y. Abo e all, he issue o policy endogenei y o mac oeconomic condi ions
p ecludes d awing oo wide- anging conclusions abou policy e icacy.
5 S uc u al Fac o s
An assessmen o mac oeconomic ola ili y and mode a ion would be incomple e wi hou e e ence
o s uc u al cha ac e is ics o an economy, such as poli ical and economic ins i u ions, di e si ica-
ion in p oduc ion and ade, economic openness, he incidence o con lic s and disas e s, geog aphy,
human capi al, e c. Signi ican li e a u es in economics ha e e alua ed he e ec s o hese ac o s in
di e en con ex s. Fo example, Acemoglu e al. (2003) analyze he e ec s o long- e m ins i u ional
de elopmen on mac oeconomic s abili y and ind ha coun ies ha inhe i ed mo e ’ex ac i e’
ins i u ions om hei colonial pas a e mo e likely o expe ience high ola ili y and economic
c ises. They a gue ha poo ins i u ions cause ola ile and dis o iona y mac oeconomic policies,
which ac as a p oxima e cause o ola ili y. Rod ik (1999) ela es he lack o pe sis en g ow h
in de eloping coun ies o social con lic s uelled by inequali y, e hnic ac ionaliza ion, and weak
ins i u ions. Malik & Temple (2009) examine he s uc u al de e minan s o ou pu ola ili y in
de eloping coun ies wi h Bayesian me hods. They ind a signi ican ole o ma ke access: emo e
coun ies a e mo e likely o ha e undi e si ied expo s, high le els o expo concen a ion, high
ToT ola ili y, and high ou pu ola ili y. Au e (2003) inds posi i e e ec s o na u al disas e s on
consump ion ola ili y in he Ca ibbean egion. Abdullahi & Sua di (2009) examine he e ec s o
inancial and ade libe aliza ion on ou pu and consump ion g ow h ola ili y in A ica and show
ha ade libe aliza ion inc eases ola ili y, whe eas inancial libe aliza ion dec eases ola ili y
h ough g ea e e icacy o consump ion smoo hing. They also ind ha inancial dep h and
ins i u ional quali y in e ac nega i ely wi h ade and inancial openness.
A signi ican li e a u e has also e alua ed he link be ween economic and ade di e si ica ion
and mac oeconomic ola ili y (Papageo giou & Spa a o a,2012;Papageo giou e al.,2015;Moo e
& Walkes,2010;Ko en & Ten ey o,2007;Romeu & da Cos a Ne o,2011;Fa shba ,2012;Jansen e
al.,2009), eaching a consensus ha mo e di e si ied economies show lowe ola ili y in a iables
such as GDP, consump ion, in es men , and expo s, and a e mo e esilien o ex e nal shocks.
A key channel is ha di e si ica ion in ol es LICs shi ing esou ces om sec o s whe e p ices
a e highly ola ile and co ela ed, such as mining and ag icul u e, o less ola ile and co ela ed
sec o s, such as manu ac u ing and se ices, esul ing in g ea e s abili y.
The e ec s o capi al lows and ans e s ha e also been hea ily s udied. Singh e al. (2011)
p o ide a ca e ul mac oeconomic s udy o emi ances in Sub-Saha an A ica (SSA), and ind
ha emi ances a y coun e -cyclically wi h GDP pe capi a, consis en wi h he hypo hesis ha
emi ances can help mi iga e economic shocks.
5.1 C oss-Sec ional Analysis
In he ollowing, I p esen an a emp o ank hese ac o s by ele ance o p edic ing ola ili y in
a c oss-sec ion o A ican economies du ing he 1990-2019 pe iod. Fo his, I selec ed 98 p edic o s
join ly a ailable o 49 A ican economies (excluding Djibou i, Libe ia, Somalia, Sou h Sudan, and
Zimbabwe), wi h a o al o 2.5% missing alues. These include he as majo i y o cha ac e is ics
s udied in he li e a u e e e enced abo e and also he ex e nal en i onmen and inancial sec o
14
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 20
indica o s s udied in Sec ions 4.1 and 4.2. I g oup hese 98 indica o s in o 19 opics, lis ed in Table
8, and, wi h s a is ical de ails, in Table C18. I hen use a Random Fo es s (RF) machine lea ning
model ollowing B eiman (2001) o p edic he ola ili y o pe -capi a g ow h and in la ion and
de e mine he impo ance o di e en p edic o s, bo h indi idually and a he opic le el.14
Table 8: Indica o Topics o C oss-Sec ional P edic ion
# Topic Indica o s
1 Ins i u ions 9
2 Business En i onmen 4
3 P oduc ion Sha es 2
4 Clima e & Ag icul u e 8
5 T ade In ensi y and Composi ion 7
6 T ade Di e si ica ion 4
7 Exchange Ra e and Te ms o T ade 5
8 Financial & Aid Flows 5
9 Financial Sec o 6
10 Deb & Rese es 4
11 Popula ion 6
12 Heal h 5
13 Educa ion 5
14 Na u al Disas e s & Con lic 6
15 Geog aphy & Accessibili y 7
16 Na u al Resou ces 2
17 Po e y & Inequali y 3
18 Religion & E hnici y 4
19 O he s 6
SUM 98
No es: 98 indica o s, a ailable o a c oss-sec ion o 49 A ican coun ies
(excluding Djibou i, Libe ia, Somalia, Sou h Sudan, and Zimbabwe), a e
classi ied in o 19 opics. See Table C18 o de ails.
To ank p edic o s indi idually, I i a eg ession o es o 100,000 highly de-co ela ed ees,
g own o ull size, wi h only 3 ou o 98 p edic o s andomly chosen a each spli . I de e mine
each p edic o ’s impo ance by andomly pe mu ing ha p edic o ’s obse a ions and measu ing
he inc ease in he Ou -o -Bag (OOB) Mean Squa ed P edic ion E o (MSE) caused by he
pe mu a ion in pe cen age e ms. Figu e C28 shows he op 30 p edic o s o he MAD o GDP pe
capi a g ow h o e he 1990-2019 pe iod. Su p isingly, despi e he high-dimensional da ase , he
model only explains 28% o he OOB a iance in he ou come a iable. The e a e 10 p edic o s
whose pe mu a ion inc eases he MSE by mo e han 2%; among hese, he e a e 3 ins i u ions, 2
business en i onmen , and 2 emi ance a iables. The o he op 10 a iables a e na u al esou ce
en s as a ac ion o GDP, he sha e o indus y in GDP, and na u al disas e dea hs. Among he
a iables ha dec ease p edic i e accu acy by mo e han 1% a e also oil en s, ade wi h LMICs
as a sha e o GDP, he MAD o FDI, o al ese es, he ce eal yield, human igh s and le el o
democ acy, he MAD o ToT g ow h, and he ade sha e o GDP.
Ranking opics (Table 8) is challenging, as opics a e mul i-dimensional and co ela ed. A i s
app oach is o use he model unde lying Figu e C28, pe mu e all p edic o s wi hin a opic, and
measu e he dec ease in p edic i e powe . A p oblem wi h his me hod is ha i does no a ain
he p edic i e pe o mance o a model i wi hou hose p edic o s. Thus, ano he app oach is
i ing di e en models, excluding opics and compa ing hei pe o mance o he baseline model.
This me hod can, howe e , also be c i icized i di e en opics a e co ela ed, as p edic o s in o he
opics will cap u e some a ia ion o p edic o s in he excluded opic. One possibili y o limi his
is o p ojec all o he p edic o s on he p edic o s o he excluded g oup and use he esiduals o i
14Ini ially, he RF model is used o p edic he 2.5% missing alues in he p edic o da ase by an i e a i e
algo i hm called ’MissFo es ’ de eloped by S ekho en & B¨uhlmann (2012). Mos p edic o s ha e no missing alues
(see Table C18), and no p edic o has mo e han 8 missing alues.
15
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 21
a new model.15 In he ace o ambigui y, I implemen all 3 me hods and compu e he a e age ank
based on he inc ease in MSE om pe mu a ion/exclusion/pa ialling ou he opical p edic o s.
Table C19 epo s he esul s. O e all, ins i u ions eme ge as he mos impo an opic, ollowed by
inancial lows, ade in ensi y and composi ion, he inancial sec o , business condi ions, na u al
esou ce in ensi y, na u al disas e s, and con lic .
The exe cise is epea ed, in Figu e C29 and Table C20, wi h he MAD o CPI in la ion. Ex-
change a e pass- h ough plays a dominan ole in many A ican economies, ollowed by indica o s
o agili y and con lic , and ins i u ions. Table C20 shows ha excluding exchange a e a iables
wo sens he model i by 17.6%, whe eas excluding mos o he opics inc eases he i by 1-3%.
Apa om he exchange a e, con lic / agili y and ins i u ions, business condi ions, popula ion
dynamics, ade in ensi y, ade di e si ica ion, and he inancial sec o a e impo an p edic o s
o in la ion ola ili y.
5.2 Time-Va ia ion in S uc u al Fac o s
The compa ison o changes in hese ac o s wi h he documen ed changes in ola ili y o e he 1990-
2019 pe iod is o g ea impo ance in he scope o his pape bu challenging as many indica o s
a e ei he (nea ly) ime-in a ian o lack his o ical da a o ace hem back o 1990. Pa icula ly,
su ey-based a iables measu ing he quali y o he business en i onmen and inancial access
lack his o ical co e age. Figu e C27 shows some ins i u ional and business a iables o e he
ime pe iod, indica ing no posi i e change in he Wo ldwide Go e nance Indica o s bu signi ican
imp o emen s in business condi ions and economic ins i u ions in he ecen yea s since measu es
became a ailable. Res ic ing he analysis o a iables wi h he necessa y his o y hus p o ides an
incomple e pe spec i e o changes wi hin A ican economies in he pas 30 yea s.
O he 98 a iables conside ed in he c oss-sec ion, 70 ha e some ime a ia ion o be conside ed
o analysis o changes.16 No included a e mainly geog aphy, eligion, and e hnici y a iables,
s a ic ag icul u al cha ac e is ics, and some ins i u ions and business indica o s wi h low ime
co e age. The analysis is hen epea ed on a c oss-sec ion o i s -di e ences o 49 A ican
economies, ob ained by sub ac ing he median o he 70 indica o s o e he 1990-2004 pe iod
om he 2005-2019 median and ela ing his o he di e ence in he MADs o PCGDP g ow h and
CPI in la ion. Figu e C30 and Table C21 show he esul s o PCGDP. I u ns ou ha p edic ing
changes in mac oeconomic ola ili y o e ime is e y challenging. The RF model in Figu e C30
explains 0% o he a iance in he change o he MAD o GDP pe capi a g ow h be ween 1990
and 2019 OOB ( he in-sample R2is 98%, indica ing o e i ing). Wi h some hype pa ame e
uning, he OOB R2can be inc eased o 4%, bu his is s ill poo . I is ne e heless no ewo hy
ha 2 inancial sec o a iables a e among he op 5 p edic o s ha inc ease he MSE by close o
1%. The o he 3 a iables a e GDP pe pe son employed, li e expec ancy, and popula ion, which
p oxy o changes in he labo o ce and in human capi al. Table C21 con i ms he impo ance
o he inancial sec o as well as social cha ac e is ics such as popula ion dynamics, heal h, and
educa ion, alongside ins i u ions and ’O he s’ which includes GDP pe pe son employed, g oss
na ional sa ings, and he Human De elopmen Index.
O e all, he esul is a nega i e one. This could be due o ela ing changes in he medians o
changes in he MAD o ola ili y, which h ows away a lo o po en ially use ul a ia ion, bu , as
shown in Appendix D, employing less obus measu es such as he s anda d de ia ion o g ow h
and ime-a e ages o p edic o s, does no p oduce models wi h highe p edic i e powe . Thus, he
esul s s ongly sugges ha he bulk o he A ican Mode a ion in g ow h ola ili y is no due
o changes in ha d s uc u al ac o s like ins i u ions, ade in ensi y, and di e si ica ion, con lic
in ensi y, po e y, and inequali y, o na u al esou ce en s, which make up he bulk o he p edic o
space, and hus he a iables andomly sampled a each spli o build a p edic i e model. These
ac o s con inue o be impo an in explaining di e en le els o baseline ola ili y be ween A ican
coun ies (as shown abo e), bu hey do no explain he A ican Mode a ion. This inding is
con i med by he model selec ing inancial sec o and human de elopmen a iables as he mos
15The p ojec ion is done using linea eg ession, e.g. Z(Z′Z)−1Z′Xwhe e Zis a se o opical p edic o s and
X he se o emaining p edic o s. I he se o opical p edic o s Zwe e la ge, his p ojec ion could also be made
using an RF model, bu wi h <10 p edic o s in Z he RF is no a sensible modeling choice.
16The i s column in Table C18 in he Appendix shows which a iables a e included in he panel.
16
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 22
Figu e A1: Es ima ed Coun y Spec al Densi ies and Median Spec al Densi y
10−5
10−4
10−3
10−2
10−1
2 3 4 5 7 10 20 30
1ωj (Pe iod in Yea s)
Is(ωj) (Scaled Spec al Densi y)
Scaled Spec al Densi ies o Ln(GDP pe Capi a) in A ica
Da a Sou ce: IMF Wo ld Economic Ou look, Oc obe 2021
I is e iden ha he spec a o di e en coun ies a e qui e he e ogeneous, wi h abou 3 o de s
o magni ude lying be ween he leas and mos - ola ile coun ies a each equency, bu an o e all
dec ease in spec al powe wi h highe equencies is common o all coun ies. The median es ima e
in Figu e A1 shows ha on a e age ola ili y a low equencies wi h pe iods o 20-yea s+ is a ound
2 o de s o magni ude la ge han yea - o-yea changes in ou pu (2-yea pe iod).
To de e mine whe he ola ili y a ce ain equencies is ha m ul o g ow h in A ican economies,
I compu e he c oss-sec ional co ela ion o he spec al densi y wi h median GDP pe capi a
g ow h in he 1990-2019 pe iod, o each undamen al equency ωj. Figu e A2 epo s hese
co ela ions in he op hal , and he bo om hal shows co esponding eg ession coe icien s, which
also ake in o accoun he di e ing magni udes o ola ili y a di e en equencies ha Figu e A1
made e iden .
Figu e A2: Co ela ion o Spec al Densi y and Median Pe -Capi a G ow h, A ica 1990-2019
2
2.1
2.3
2.5
2.7
3
3.3
3.8
4.3
5
6
7.5
10
15
30
co (Is(ωj), med(%∆Y))
−0.2
−0.1
0.0
0.1
0.2
0.3
2
2.1
2.3
2.5
2.7
3
3.3
3.8
4.3
5
6
7.5
10
15
30
co (Is(ωj), med(%∆Y)) a (Is(ωj))
1ωj (Pe iod in Yea s)
−300
−200
−100
0
100
23
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 29

Figu e A2 exhibi s an as onishingly clea pa e n, wi h a s ong nega i e co ela ion o abou
-0.2 be ween economic g ow h and ola ili y a high equencies o 0.5 (pe iod 2 yea s), which hen
g adually ends o ze o a equencies o 0.25 (pe iod 4 yea s), and u ns posi i e up o abou 0.3
o lowe equency a ia ion wi h 10 o 30-yea pe iods. Thus A ican da a indeed show ha sho -
e m ola ili y wi h pe iods o up o 4 yea s is associa ed wi h lowe g ow h, whe eas ola ili y a
longe pe iods is an indica ion o heal hy g ow h.
This sugges s ha a high-pass il e like compu ing he g ow h a e migh do easonably well o
ex ac luc ua ions ha m ul o g ow h. I use he eg ession coe icien s in he bo om hal o Figu e
A2 o c ea e an op imal disc e e high-pass il e (ωj) in he spi i o Tsui (1988), ha cap u es
ola ili y ha m ul o g ow h. The il e simply consis s o he absolu e alues o all nega i e
eg ession coe icien s on he equency bands, se ing posi i e coe icien s o ze o. Mul iplying he
spec al densi y es ima e o each coun y wi h his il e and summing up he weigh ed spec al
o dina es gi es he powe o he il e ed spec um, which p o ides a summa y s a is ic o he
ha m ul ola ili y in each coun y. Fo mally, I de ine a ha m ul ola ili y index (HVI) as
HVI = X
j
(ωj)×Is(ωj) whe e (7)
(ωj) = −βωj×1[βωj<0] and (8)
βωj=co (Is(ωj), med(%∆Y))
a (Is(ωj)) .(9)
Be o e compa ing he HVI o some s a is ic compu ed on he g ow h a e, I wish o de e mine
o wha ex en compu ing a g ow h a e i sel esembles he ans o ma ion induced by applying
(ωj) o he da a. Figu e A3 shows ha compu ing he g ow h a e indeed wo ks like a high-pass
il e ha , ela i e o he na u al log baseline, accen ua es ola ili y a pe iods lowe han 4.2 yea s
and dampens ola ili y a highe pe iods.
Figu e A3: Spec al Densi ies o G ow h Ra e and Na u al Log o GDP pe Capi a
10−4.5
10−4
10−3.5
10−3
2 3 4 5 7 10 20 30
1ωj (Pe iod in Yea s)
Is(ωj) (Scaled Spec al Densi y)
Func ion
Ln(Y)
G ow h(Y)
Median Spec al Densi y o FUN(GDP pe Capi a) in A ica
Da a Sou ce: IMF Wo ld Economic Ou look, Oc obe 2021
Di iding he g ow h spec um by he log spec um yields he disc e e il e ha , i mul iplied
wi h he log spec um, yields he same e ec as compu ing a g ow h a e (i.e. di e encing he
log-le el se ies) in he ime domain. I call his de i ed i s -di e ence il e ∆(ωj). To compa e
∆(ωj) o he op imal empi ical il e (ωj) based on eg essions agains median pe capi a g ow h,
I scale bo h il e s so ha he weigh s/coe icien s on all equencies ωjsum o 1. Figu e A4 shows
he ou come.
24
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 30
Figu e A4: Fi s -Di e ence Fil e and Reg ession-Based Fil e
2 2.1 2.3 2.5 2.7 3 3.3 3.8 4.3 5 6 7.5 10 15 30
1ωj (Pe iod in Yea s)
No malized Weigh s/Coe icien s
0.00 0.05 0.10 0.15 0.20
2 2.1 2.3 2.5 2.7 3 3.3 3.8 4.3 5 6 7.5 10 15 30
0.00 0.05 0.10 0.15 0.20
∆(ωj) (ωj)
Figu e A4 indica es ha he i s -di e ence il e ∆(ωj) b oadly esembles he op imal empi ical
il e (ωj) o ex ac ing ola ili y ha m ul o g ow h. Compa ed o he la e , i s -di e encing
p o ides a smoo he ans o ma ion o he da a, ha pu s less weigh on high- equency ola ili y,
bu he e o e keeps some o he low- equency ola ili y as well. Since inding an op imal il e (ωj)
o ex ac ha m ul economic ola ili y is likely always going o be a complex empi ical ask, and
he esul ing il e is p one o be highly dependen on he da a and me hodology used o es ima e
i , a simple me hodology such as compu ing i s -di e ences and hen applying some s a is ic
o summa ise he ola ili y in he di e enced se ies is p e e able o ensu e he anspa ency and
ep oducibili y o esea ch. Below I conside he 3 summa y s a is ics used in his pape : he
s anda d de ia ion (SD), in e qua ile ange (IQR), and median absolu e de ia ion (MAD) o he
g ow h a e o GDP pe capi a, and compa e hem o he HVI index (Eq. 7) and median pe
capi a g ow h, compu ed o each A ican coun y using da a om 1990 h ough 2019. The da a
a e co ela ed, and a eg ession line is i , using a obus MM es ima o ollowing Yohai (1987) and
Kolle & S ahel (2011), wi h a high b eakdown poin o 0.5, ensu ing ha ou lie s don’ in luence
he es ima es. Figu e A5 shows cha s including hese obus i s, a obus co ela ion coe icien
de i ed om he i , and empi ical ola ili y dis ibu ions es ima ed by a his og am and a gaussian
ke nel densi y.
Figu e A5: Vola ili y Measu es and Median GDP pe Capi a o 51 A ican Economies, 1990-2019
Raw Measu es Na u al Log o Measu es
x
Densi y
SD
0.05 0.200 4 8
0.05 0.15 0.25
0.05 0.15 0.25
0.91
***
x
Densi y
IQR
0.92
***
0.96
***
x
Densi y
MAD
0.02 0.06 0.10 0.14
0 2 4 6 8 10
0.98
***
0.44
**
0.46
**
x
Densi y
HVI
0.05 0.20
−0.15
−0.24
.
0.02 0.08 0.14
−0.22
−0.20
0.00 0.04
0.00 0.04
x
Densi y
Median
x
Densi y
SD
−4.0 −2.5−4 0 2
−4.0 −3.0 −2.0 −1.0
−4.0 −3.0 −2.0
0.84
***
x
Densi y
IQR
0.81
***
0.98
***
x
Densi y
MAD
−5.0 −4.0 −3.0 −2.0
−4 −2 0 2
0.89
***
0.67
***
0.66
***
x
Densi y
HVI
−4.0 −2.5 −1.0
0.17
0.18
−5.0 −3.5 −2.0
0.12
0.076
−6 −5 −4 −3
−6 −4
x
Densi y
Median
The le side o Figu e A5 shows ha he HVI is posi i ely co ela ed wi h all 3 ola ili y
measu es de i ed om he g ow h a e, pa icula ly wi h he SD. All ola ili y measu es a e also
nega i ely co ela ed wi h he median g ow h a e. Since a ew coun ies such as Lybia, Guinea-
Bissau, E i ea, and Rwanda ha e e y high le els o ola ili y (due o con lic s du ing his pe iod),
he empi ical ola ili y dis ibu ions a e igh -skewed. As indica ed on he le side, he nega i e
25
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 31
co ela ion o he IQR and MAD o g ow h wi h median g ow h is s onge compa ed o he HVI
and he SD, which may be he e ec o ou lie s ha ing a s onge e ec on he SD and HVI.20 The
igh side o Figu e A5 he e o e also shows a e sion o he cha whe e he na u al log was applied
o all measu es. This gi es nice sca e plo s and densi y es ima es bu also le s he ela ionship
be ween ola ili y and median g ow h u n posi i e (albei insigni ican ), o all measu es apa
om he HVI whe e he co ela ion is ze o. This change in he sign o co ela ions is explicable as
some o he coun ies a ec ed by con lic in 1990-2019, such as Rwanda and Guinea-Bissau, also
expe ienced high a e age g ow h h oughou his pe iod, and may exe a s onge in luence on
he MM es ima es a e aking he log.
To conclude, he discussions in his sec ion highligh ed ha when dealing wi h a di icul - o-
measu e phenomenon such as economic ola ili y, h ee hings a e impo an : p ecise measu emen
o (ha m ul) ola ili y, obus ness agains ou lie s, and a simple, ep oducible, and da a-independen
me hodology. This pape endo sed obus s a is ics such as he IQR and he MAD, compu ed on
he g ow h a e o he se ies, o measu e economic ola ili y. The analysis conduc ed in his sec ion
shows ha compu ing he g ow h a e p o ides a decen app oxima ion o an op imal empi ical
il e , applied o he spec al densi y o ex ac ola ili y ha m ul o economic de elopmen in
A ica and ha compu ing he IQR o MAD o he g ow h a e p o ides an accep able and obus
summa y measu e o his ola ili y, compa able o he powe o he op imally il e ed spec um
( he HVI). The IQR and MAD o he g ow h a e hus su icien ly mee he join aims o p ecision,
obus ness, and simplici y. A he coun y le el, he MAD is p e e ed o he IQR as i is mo e
obus .
Re e ences
Gelb, A. (1979). On he de ini ion and measu emen o ins abili y and he cos s o bu e ing expo
luc ua ions. The Re iew o Economic S udies,46(1), 149–162.
Kolle , M., & S ahel, W. A. (2011). Sha pening wald- ype in e ence in obus eg ession o small
samples. Compu a ional S a is ics & Da a Analysis,55(8), 2504–2515.
Shumway, R. H., S o e , D. S., & S o e , D. S. (2000). Time se ies analysis and i s applica ions
(Vol. 3). Sp inge .
Tsui, K. Y. (1988). The measu emen o expo ins abili y: a me hodological no e. Economics
Le e s,27(1), 61–65.
Yohai, V. J. (1987). High b eakdown-poin and high e iciency obus es ima es o eg ession.
The Annals o s a is ics, 642–656.
20The Fas Fou ie T ans o m unde lying he smoo h spec al es ima es used o p oduce he HVI is no obus
agains ou lie s.
26
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 32
B. A B ie Look a Expendi u e on GDP
Figu e B1 p o ides a de ailed b eakdown o expendi u e sha es in GDP, a e aged ac oss coun ies.
CINV deno es changes in in en o ies, SD a e s a is ical de ia ions, and expo s (X) and impo s
(M) a e p o ided alongside ne expo s (NX). CINV is e y small in he median A ican coun y.
Figu e B1: GDP Sha es: Expendi u e Side
Unweigh ed
Weigh ed by GDP
1992
1996
2000
2004
2008
2012
2016
2020
1992
1996
2000
2004
2008
2012
2016
2020
−10%
0%
10%
20%
30%
40%
50%
60%
70%
Yea
Mean Ac oss Coun ies
Expendi u e
C
I
CINV
G
X
M
SD
NX
Expendi u e Sha es in GDP in A ica
Da a Sou ce: Wo ld De elopmen Indica o s, Accessed No embe 2021
Figu e B2 shows smoo hed con ibu ions o majo expendi u e componen s o GDP pe capi a
g ow h, analogous o Figu e C11 on he p oduc ion side. I is e iden ha consump ion g ow h
declined in impo ance un il a ound 2013 and inc eased a bi again he ea e .
Figu e B2: Con ibu ions o GDP G ow h: Expendi u e Side
Unweigh ed
Weigh ed by Popula ion
1990
1995
2000
2005
2010
2015
2020
1990
1995
2000
2005
2010
2015
2020
−30%
−20%
−10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Yea
10−Yea MA o Median Coun y
Expendi u e
C
I
G
NX
SD
Expendi u e Sha es in A e age GDP pe Capi a G ow h in A ica
Da a Sou ce: Wo ld De elopmen Indica o s, Accessed No embe 2021
O e all he sha es a e ela i ely s able. In es men has inc eased sligh ly, climbing om ∼20%
in 2005 o ∼25% in 2012. Expo s and impo s also bo h inc eased g adually un il 2012 and hen
began o all, wi h a g ea e decline in he expo sha e, yielding a highe agg ega e ade de ici .
27
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 33
The le panel o Figu e B3 shows he agg ega e decline in ola ili y, which, on he expendi u e
side, is accoun ed o by declines in he ola ili y o all componen s be o e 2010, wi h he ola ili y o
ade and in es men emaining high he ea e . Pa icula ly in es men ola ili y (which includes
CINV in his disagg ega ion), declined s ongly. The igh panel o Figu e B3 shows ha om
a equency domain pe spec i e, in es men is he mos ola ile componen a all equencies
(exemp ing ne expo s). Consump ion is he leas ola ile componen and app oaches he ola ili y
o GDP a lowe equencies.
Figu e B3: Expendi u e Vola ili y Ac oss Time and F equency
5
10
15
20
2000 2005 2010 2015
Yea
Median Ac oss Coun ies
S a is ic
SD
MAD
Sec o
GDP
C
I
G
X
M
10−Yea Rolling Vola ili y o EXPPC G ow h
Da a Sou ce: Wo ld De elopmen Indica o s, Accessed No embe 2021
10−3
10−2
10−1
100
2
3
5
10
20
30
Pe iod in Yea s
Median Spec al Densi y
Sec o
GDP
C
I
G
X
M
Spec al Densi ies o ln(EXPPC)
No es: The LHS shows 10-yea olling SDs and MADs o he g ow h a e o GDP pe capi a a cons an 2015 p ices and
i s expendi u e componen s (GDP = C + I + G + X - M). Fo he RHS see he no e o Figu e 3and Appendix A.
Table B1 p o ides a co a iance ma ix analogous o Table C6. This shows la ge nega i e
co a iances o impo s wi h abso p ion and expo s, indica ing he endogenei y o ne expo s
and he di icul ies o accoun agg ega e changes in ola ili y om he expendi u e side. Linking
p oduc ion and expendi u e side da a is also di icul wi hou de ailed b eakdowns, bu he la ge
declines in ag icul u e and se ice sec o ola ili y a e likely e lec ed on he expendi u e side in he
decline in consump ion ola ili y, bu also in declining ola ili y o he me chandise ade balance.
Table B1: Expendi u e Vola ili y and Con ibu ion o Agg ega e Vola ili y, 1990-2019
Da a Sec o : C I G X M C I G X M
GDP Sha e (¯
θk) 0.701 0.227 0.152 0.336 -0.416 0.698 0.220 0.149 0.338 -0.406
Co .:Classical Robus (SDE)
Expendi u e C 52.22 33.69
G ow h I -0.93 528.25 1.87 272.76
(∆VA/VA −1) G 0.57 11.42 229.63 3.79 26.07 114.99
X -7.82 14.84 -5.95 269.80 -14.96 5.14 -7.61 212.40
M -26.78 -92.13 -16.58 -93.44 242.37 -20.57 -120.07 -29.83 -63.39 166.73
Expendi u e C 22.95 18.23
Con ibu ion I 0.27 14.43 0.47 11.57
(∆VA/GDP −1) G 0.12 0.56 3.41 -0.05 0.47 2.15
X -1.09 0.79 -0.18 14.21 -2.02 0.56 -0.08 12.88
M -5.36 -6.73 -0.91 -6.76 22.06 -5.08 -5.54 -1.51 -6.95 16.84
No es: Since sec o al g ow h a es can be e y ola ile, I employ bo h a classical (Pea son) and obus co a iance es ima o wi h a high b eakdown
poin (0.5) based on S ahel (1981) and Donoho (1982). The choice o me hods was in o med by Ma onna e al. (2018) and a ailable implemen a ions
in a ious R packages. The S ahel-Donoho obus co a iance es ima o is implemen ed by he package co (Todo o & Filzmose ,2009). Co a iance
e ms a e agg ega ed ac oss coun ies using he median, whe eas sec o al sha es a e agg ega ed wi h he mean. A e age sha es o each coun y a e
compu ed using all bu he i s obse a ion ollowing Eq. 3. The sha es epo ed abo e ”Robus ” a e compu ed by aking he median sha e o each
coun y, and agg ega ing ac oss coun ies using he mean.
Table B2 shows a decomposi ion o he educ ion in GDP ola ili y be ween τ1= 1990-2004
and τ2= 2005-2019, based on he LHS o Eq. 3, analogous o Table B2 in he pape . Due
28
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 34

o he di icul y wi h expo s accoun ing, I only epo esul s whe e sha es a e compu ed a
he coun y-le el and agg ega ed ac oss coun ies using he median. The esul s imply ha he
expendi u e-side sha es in he mode a ion a e oughly consis en wi h he hei sha e in agg ega e
ola ili y, epo ed in Table B1, wi h consump ion, in es men s and expo s ha ing a highe han
p opo ional sha e, consis en wi h Figu e B3. The esul s a e qui e noisy h ough, e en when
agg ega ed ac oss coun ies using he median; o example he sign o he co a iance con ibu ion
om he expendi u e side is no obus o he choice o co a iance es ima o .
Table B2: Sec o al Con ibu ion o Mode a ion in GDP Vola ili y
Co Es AggFun Fi ∆ a (%∆Y)τC I G X M Pco jk
Pea son Median 100% -6.65 48% 16% 3.8% 7.3% 11% 16%
Comedian Median 55% -1.14 73% 32% 5.1% 27% 12% -35%
No es: The ’Fi ’ column signi ies how closely Eq. 3is sa is ied. Columns C-M gi e he sec o al con ibu ion o he
agg ega e ola ili y educ ion in pe cen age e ms, and Pco jk gi es he combined con ibu ion o all co a iance
e ms. Sha es a e compu ed a he coun y-le el, and agg ega ed using he median.
C. Addi ional Tables and Figu es
29
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 35
Figu e C1: Log10 GDP pe Capi a o 54 A ican Economies in Cons an USD, 1990-2019
The Gambia
Togo
Tunisia
Uganda
Zambia
Zimbabwe
Sie a Leone
Somalia
Sou h A ica
Sou h Sudan
Sudan
Tanzania
Nige ia
Republic o Congo
Rwanda
São Tomé and P íncipe
Senegal
Seychelles
Mau i ania
Mau i ius
Mo occo
Mozambique
Namibia
Nige
Leso ho
Libe ia
Libya
Madagasca
Malawi
Mali
E hiopia
Gabon
Ghana
Guinea
Guinea−Bissau
Kenya
Democ a ic Republic o he Congo
Djibou i
Egyp
Equa o ial Guinea
E i ea
Eswa ini
Cabo Ve de
Came oon
Cen al A ican Republic
Chad
Como os
Cô e d'I oi e
Alge ia
Angola
Benin
Bo swana
Bu kina Faso
Bu undi
1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020
2.1
2.2
2.3
2.4
2.5
2.6
3.20
3.25
3.30
3.35
3.40
3.4
3.5
3.6
3.7
3.0
3.1
3.2
3.3
2.6
2.7
2.8
2.9
2.60
2.65
2.70
2.75
2.80
3.6
3.8
4.0
4.2
2.7
2.8
2.9
3.0
3.1
3.2
3.0
3.1
3.2
2.6
2.7
2.8
2.9
3.04
3.06
3.08
3.10
2.7
2.8
2.75
2.80
2.85
2.90
2.95
2.4
2.6
2.8
3.0
3.2
3.4
3.5
3.6
3.7
3.8
3.9
3.00
3.05
3.10
3.15
3.20
2.1
2.4
2.7
3.0
3.3
2.9
3.0
3.1
3.2
3.6
3.7
3.8
3.9
4.0
2.6
2.7
2.8
2.9
2.5
3.0
3.5
4.0
2.8
2.9
2.55
2.60
2.65
2.70
2.75
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.1
3.2
3.3
2.6
2.8
3.0
2.9
3.0
3.1
2.5
2.6
2.7
3.3
3.4
3.5
3.6
3.7
3.8
3.0
3.2
3.4
3.6
3.2
3.4
3.6
3.8
4.0
3.3
3.4
3.5
2.3
2.5
2.7
2.9
3.1
3.7
3.8
3.9
4.0
3.3
3.4
3.5
3.6
2.4
2.8
3.2
3.6
2.95
3.00
3.05
3.10
3.15
3.3
3.4
3.5
3.7
3.8
3.9
1.0
1.5
2.0
2.5
3.5
3.6
3.7
3.8
3.9
4.0
3.25
3.30
3.35
3.40
−2
−1
0
1
2
2.6
2.7
2.8
2.9
3.0
3.2
3.3
3.4
3.5
3.6
3.0
3.2
3.4
2.00
2.25
2.50
2.75
2.50
2.75
3.00
2.8
2.9
3.0
3.1
3.2
3.10
3.15
3.20
3.25
3.30
3.35
3.1
3.2
3.3
3.4
2.2
2.4
2.6
2.8
2.85
2.90
2.95
3.00
Yea
Log10 GDP Pe Capi a
Es ima e: IMF (cons an 2010 USD) Wo ld Bank (cons an 2015 USD)
Da a Sou ce: IMF WEO Oc obe 2021 and IFS, and Wo ld De elopmen Indica o s No embe 2021
30
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 36
Table C1: Agg ega e Vola ili y o 51 A ican Coun ies: 1990-2019
GDP Pe Capi a G ow h CPI In la ion
ISO3 Coun y Income Median MAD IQR Median MAD IQR
DZA Alge ia Uppe middle 1.124 1.197 2.359 4.642 1.740 4.925
AGO Angola Lowe middle 1.851 5.291 8.479 30.269 22.229 190.916
BEN Benin Low 1.422 1.246 2.320 2.140 1.766 3.978
BWA Bo swana Uppe middle 3.006 2.100 3.908 8.067 2.109 3.876
BFA Bu kina Faso Low 2.883 1.476 2.601 1.804 2.025 3.812
BDI Bu undi Low -0.274 1.547 3.068 8.294 4.474 8.363
CPV Cabo Ve de Lowe middle 4.092 1.609 4.714 2.944 2.050 4.551
CMR Came oon Lowe middle 1.271 0.774 1.481 2.022 0.987 1.988
CAF Cen al A . Rep. Low 0.843 1.753 3.991 2.782 1.993 3.787
TCD Chad Low -0.071 3.422 6.621 3.930 4.615 6.987
COM Como os Lowe middle 0.618 1.576 2.925 2.704 1.683 3.214
CIV Cˆo e d’I oi e Lowe middle -0.691 3.404 7.570 2.298 1.523 3.067
COD Dem. Rep. o. Congo Low 0.185 3.322 8.901 27.230 25.014 326.361
DJI Djibou i Lowe middle 1.520 2.586 4.819 2.629 1.366 2.887
EGY Egyp Lowe middle 2.133 1.258 2.389 9.727 3.186 5.367
GNQ Equa o ial Guinea Uppe middle 5.262 13.878 25.209 4.380 2.134 4.118
ERI E i ea Low 1.457 7.647 12.841 10.288 5.501 10.631
SWZ Eswa ini Lowe middle 1.506 1.146 2.197 7.469 1.818 3.302
ETH E hiopia Low 7.154 2.375 7.976 9.024 5.614 11.314
GAB Gabon Uppe middle 0.664 1.940 4.110 1.448 1.220 2.249
GHA Ghana Lowe middle 2.333 1.063 1.915 15.291 4.902 13.191
GIN Guinea Low 1.345 1.280 2.548 9.592 5.204 11.816
GNB Guinea-Bissau Low 1.493 1.402 2.710 3.277 3.886 13.204
KEN Kenya Lowe middle 1.292 1.464 3.397 7.324 2.230 6.072
LSO Leso ho Lowe middle 2.326 1.100 2.503 7.043 2.010 3.880
LBY Libya Uppe middle -0.577 6.512 14.369 3.122 3.704 7.868
MDG Madagasca Low 0.433 1.383 2.551 9.100 2.824 5.332
MWI Malawi Low 1.863 2.294 4.394 10.460 2.828 15.386
MLI Mali Low 1.493 1.412 3.018 1.563 2.617 5.526
MRT Mau i ania Lowe middle 1.682 2.670 5.467 4.715 1.547 2.772
MUS Mau i ius Uppe middle 3.702 0.666 1.326 5.164 1.936 3.644
MAR Mo occo Lowe middle 2.743 1.542 2.917 1.576 0.939 2.312
MOZ Mozambique Low 3.935 2.257 4.526 12.531 8.445 13.257
NAM Namibia Uppe middle 1.782 1.756 3.442 6.727 2.590 4.673
NER Nige Low -0.120 2.424 4.382 0.952 1.821 2.887
NGA Nige ia Lowe middle 1.521 2.463 4.909 11.837 3.253 5.849
COG Republic o Congo Lowe middle -1.317 4.648 7.723 2.790 1.860 3.586
RWA Rwanda Low 5.427 1.942 5.033 6.374 3.907 7.907
SEN Senegal Lowe middle 1.053 1.842 3.601 1.082 0.894 1.857
SYC Seychelles High 2.909 3.665 6.798 2.630 1.846 3.433
SLE Sie a Leone Low 1.415 2.604 5.344 13.312 7.259 16.186
ZAF Sou h A ica Uppe middle 0.911 1.079 2.289 5.980 1.368 3.713
SDN Sudan Lowe middle 2.325 1.822 4.635 20.161 13.356 38.351
STP S˜ao Tom´e & P ´ıncipe Lowe middle 0.658 1.103 2.032 13.830 6.478 23.908
TZA Tanzania Low 3.143 0.939 2.023 7.561 3.312 10.897
GMB The Gambia Low 0.585 2.546 5.186 5.306 1.743 2.736
TGO Togo Low 1.532 2.112 6.524 1.348 1.226 3.446
TUN Tunisia Lowe middle 2.490 1.505 2.825 4.092 1.104 2.029
UGA Uganda Low 3.340 1.094 2.025 5.970 2.446 7.148
ZMB Zambia Lowe middle 1.696 2.585 4.367 18.147 9.063 17.567
ZWE Zimbabwe Lowe middle -0.840 4.289 10.358 0.641 5.470 12.797
No es: Excluding Libe ia, Somalia, and Sou h Sudan. Da a Sou ce: IMF WEO Oc obe 2021.
31
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 37
Figu e C2: The G ea Mode a ion by Coun y: 50 A ican Coun ies
1.60.83
7.11.8
1.3 1.9
2.8 3.2
2.9 2.9
0.9
4.72.8
1.2 1.3
2.3
2.1
0.58 0.71
3.5
2.8
3.6
2.1 2.4
21
2.20.76
1.61.5
0.16 8.6
0.950.55 2.1 3.7
0.62 1.5
1.5 2.3
2.6
2.3 2.6
1.8
0.59
1.8 1.9
1.1 1.7
1.9 3.83.7
3.42.5
4.83.7
1.1 2.3
2
0.17 2.6
3.7 5.5
0.26 1.1
0.48 3
0.074 2.9
0.930.85
3.30.42
0.11 2.1
1.8 3.6
0.2 0.97
2.9
3.71.7
2.9 4.1
12.4
2.10.74
35
0.86 1.8
21.9 1.90.55
1.70.96
1.3 2.7
0.830.64
2.31.1
4.82.6
2.70.73
1.8 3
4.40.79
21.6
0.95 1.6
125.9
5.5 9.4
1.1 1.3
6.80.4 1.9 2
0.38 2
1.4 1.4
1.5 1.8
1.70.58
0.93 1.8
315
1.61.1
3.80.83
2.61.1
3.51.8
1.20.45
2.90.77
3.10.69
1.1 1.4
1.3 1.4 2.21.9
2.3 6.4
6.11.4
1.91.4
32.1
8.71.6
11
1.2 2.6
0.59 1.7
1.90.6
2.42.1
3.30.49 1.51.2
0.74 0.95
2.42
5.74.3
22013
3.61.2
9.27
2.31.5
9.76.5
4.41.3
1.9 2
1.6 3.5
4.22
2.4 3
3.11.3
36015
2.4 3.3
7.1 11
4.83.4
139.4
7.95.7
3.6 12
0.69 1.9 2512
5.6 12
151.5
8.46.6
8.65.2 3.62.7
128.6
239.2
1.5 1.7
4.94.1 6.63.5
2.81.4
176.4
9.95.3
2.50.46 1312
32.6
9.55.7
0.82 1.2
2.2 2.9
238.2
8.65.3
2518
3411
167
6.55.2
1.61.1 3.7 4.4
6.85.6
279
4.31.3
1203.6
2.11.5
1.4 2.5
2.61.3
5.23.8
3.21.1
1.60.89
2.82
63
1.3 1.8
1.40.61
33012
12
3.72.3
3.11.7
4.2 5.7
20.87
4.33.6
1.40.8 8.13.8
2.6 3.7
181.2
3.71.4
1.70.89 4.83.4
4.41.7
151.6
2.81
0.95 2
1.2 1.4
2.10.67
153.9
1.71.2 2.80.5 52.6
2.61.4
5.33.2
0.970.52
1.7 2.2
132.7
2.80.82
239.2
174.5
101.9
3.70.79
1.60.87 0.970.87
3.12
8.12
GDP/Capi a G ow h: Median
GDP/Capi a G ow h: MAD
CPI In la ion: Median
CPI In la ion: MAD
0.1 1 10 0.3 1 3 10 1 10 100 1 10 100
AGO
BDI
BEN
BFA
BWA
CAF
CIV
CMR
COD
COG
COM
CPV
DJI
DZA
EGY
ERI
ETH
GAB
GHA
GIN
GMB
GNB
GNQ
KEN
LBY
LSO
MAR
MDG
MLI
MOZ
MRT
MUS
MWI
NAM
NER
NGA
RWA
SDN
SEN
SLE
STP
SWZ
SYC
TCD
TGO
TUN
TZA
UGA
ZAF
ZMB
Value (Log10 Scale)
ISO3
1990−2004 2005−2019
Da a Sou ce: IMF Wo ld Economic Ou look, Oc obe 2021
32
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 38
Table C6: Sec o al Vola ili y and Con ibu ion o Agg ega e Vola ili y, 1990-2019
Da a Sec o : AGR IND SRV AGR IND SRV
Sec o Sha e (¯
θk): 0.232 0.276 0.492 0.226 0.276 0.494
Co a iance:Classical Robus (SDE)
Sec o AGR 126.95 69.01
G ow h IND -12.77 124.60 -5.67 64.73
(∆VA/VA −1) SRV -2.64 -9.86 52.93 -1.34 -1.78 25.90
Sec o AGR 6.40 2.14
Con ibu ion IND -0.50 5.79 -0.30 2.85
(∆VA/GDP −1) SRV -0.21 -0.66 8.44 -0.06 -0.38 5.39
No es: Since sec o al g ow h a es can be e y ola ile, I employ bo h a classical (Pea son) and obus
co a iance es ima o wi h a high b eakdown poin (0.5) based on S ahel (1981) and Donoho (1982). The
choice o me hods was in o med by Ma onna e al. (2018) and a ailable implemen a ions in a ious R
packages. The S ahel-Donoho obus co a iance es ima o is implemen ed by he package co (Todo o
& Filzmose ,2009). Co a iance e ms a e agg ega ed ac oss coun ies using he median, whe eas sec o al
sha es a e agg ega ed wi h he mean. A e age sha es o each coun y a e compu ed using all bu he i s
obse a ion ollowing Eq. 3. The sha es epo ed abo e ”Robus ” a e compu ed by aking he median
sha e o each coun y, and agg ega ing ac oss coun ies using he mean.
Figu e C12: Rolling Co a iances o Sec o al G ow h Ra es/Con ibu ion
−50
0
50
100
150
2000 2005 2010 2015
Yea
Median Ac oss Coun ies
Sec o
AGR.AGR
IND.AGR
SRV.AGR
IND.IND
IND.SRV
SRV.SRV
Rolling Co a iances o VA pe Capi a G ow h
Sou ce: Wo ld De elopmen Indica o s, Accessed No embe 2021
0
4
8
2000 2005 2010 2015
Yea
Median Ac oss Coun ies
Sec o
AGR.AGR
IND.AGR
SRV.AGR
IND.IND
IND.SRV
SRV.SRV
Rolling Co a iances o Con ibu ions o PCGDP G ow h
Sou ce: Wo ld De elopmen Indica o s, Accessed No embe 2021
39
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 45

Figu e C13: P oduc ion side GDP Sha es: ETD Da a
Unweigh ed
Weigh ed by GDP
1990
1995
2000
2005
2010
2015
1990
1995
2000
2005
2010
2015
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Sec o
AGR
MIN
MAN
PU
CON
WRT
TRA
COM
FIN
DWE
PUB
OTH
Figu e C14: Sec o Vola ili y and Con ibu ion o Agg ega e Vola ili y
ag min man pu con w a com in dwe pub o h
Sec o al PC G ow h G ow h IQR
0246810
ag min man pu con w a com in dwe pub o h
Sec o al Con ibu ion o PC G ow h G ow h IQR
0.0 0.2 0.4 0.6 0.8
40
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 46
Figu e C15: Rolling MADs o Sec o al G ow h Ra es/Con ibu ion
1
2
3
4
5
6
7
8
2000 2005 2010 2015
Yea
Median Ac oss Coun ies
Sec o
TOT
AGR
MIN
MAN
PU
CON
WRT
TRA
COM
FIN
DWE
PUB
OTH
Rolling MAD o VA pe Capi a G ow h
Da a Sou ce: Economic T ans o ma ion Da abase, Feb ua y 2021
0.1
0.2
0.3
0.4
0.5
0.6
2000 2005 2010 2015
Yea
Median Ac oss Coun ies
Sec o
AGR
MIN
MAN
PU
CON
WRT
TRA
COM
FIN
DWE
PUB
OTH
Rolling MAD o Con ibu ions o G ow h
Da a Sou ce: Economic T ans o ma ion Da abase, Feb ua y 2021
Table C7: Coun y Classi ica ion by La ges Sec o al Vola ili y
Me ic AGR (13) IND (17) SRV (21)
Sec o al
Vola ili y
Con ibu ion:
MAD(∆y /Y −1)
BDI, BFA, ETH, GNB,
LBR, MAR, MLI, NER,
SEN, SLE, TCD, UGA,
ZWE
AGO, BWA, COD, COG, DZA,
EGY, GAB, GIN, GNQ, LSO,
MRT, MUS, NGA, SSD, SWZ,
TUN, TZA
BEN, CAF, CIV, CMR,
COM, CPV, DJI, GHA,
GMB, KEN, MDG, MOZ,
MWI, NAM, RWA, SDN,
STP, SYC, TGO, ZAF, ZMB
Me ic AGR (22) IND (28) SRV (1)
Sec o G ow h
Vola ili y:
MAD(%∆y )
AGO, BFA, CAF, CMR,
COM, CPV, DJI, DZA,
GHA, GIN, GMB, GNB,
KEN, MAR, MUS, SEN,
SWZ, SYC, TUN, ZAF,
ZMB, ZWE
BDI, BEN, BWA, CIV, COD,
COG, EGY, ETH, GNQ, LBR,
LSO, MDG, MLI, MOZ, MRT,
MWI, NAM, NER, NGA, RWA,
SDN, SLE, SSD, STP, TCD,
TGO, TZA, UGA
GAB
Table C8: Agg ega e Sec o al G ow h S abiliza ion
Pe iod: 1990-2019 1990-2004 2005-2019
Sec o : AGR IND SRV AGR IND SRV AGR IND SRV
S a is ic: Median Ac oss Coun ies (and Pe iods)
MAD(∆y /Y −1) 1.06 1.17 1.57 1.63 1.21 1.74 0.71 1.02 1.38
MAD(%∆y ) 5.69 5.46 3.14 6.56 5.74 3.95 5.05 4.75 2.65
Sha e o Coun ies Abo e he 1990-2019 C oss-Coun y-Pe iod Median
MAD(∆y /Y −1) 0.57 0.49 0.51 0.61 0.47 0.51 0.33 0.47 0.43
MAD(%∆y ) 0.47 0.47 0.61 0.49 0.51 0.57 0.45 0.43 0.37
No e: The 1990-2019 s a is ics a e medians ac oss coun y-le el MADs o bo h he 1990-2004 and
2005-2019 pe iods. This mo e accu a ely e lec s he median ola ili y be ween hese wo pe iods, since
coun y-le el MADs calcula ed o e he en i e 1990-2019 pe iod a e much close o he 2005-2019 MADs.
41
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 47
Figu e C16: Sec o al Vola ili y Con ibu ion by Coun y
1.80.7 2.61.4 1.30.53 1.61.3
0.660.16 0.68 1.31.2 0.850.26 2.70.38 0.660.51
0.34 0.49 0.920.24
0.1 1.30.57 0.750.28 2.72.5
0.580.51 10.8 1.61.1 1.71.6 4.12.7
0.12 0.71 5.11.2 1.80.7 2.71.3
0.680.35 2.50.98 211.11.1 0.940.14 2.31.1
0.62 0.66 2.52.1 1.60.58 2.50.76 2.11.4 1.60.84 2.62
1.4
0.420.33 1.90.56 0.370.17 2.1 3.1
1.6 3.2
1.60.51 0.90.77 2.10.84
0.50.19 1.80.54 3.41.1
2.4 3.5
0.960.82 0.730.29 0.84 1.53.6 4.8
0.56 1.10.8 1.20.83 3.61.9 3.3 5.5
0.140.11 1.20.99
0.29 1.9 2.3
1.31.3
11.6 3.22.9
0.33 1.6
1.8 2.3
0.540.53 1.30.6 5.2
0.44
0.4 0.96
23
0.70.48
0.51 0.77
0.88 1.7
2.20.7 2.7 5.2
0.780.31 1.30.63 2.42
0.88 1.2 3.41.8
1.10.65 1.3 3.1
0.45 0.61 2113
0.47 0.72 20.92 1.50.87 0.920.56 1.81.1
11.6
0.830.79
0.62 1.2
1.20.26 2.61.4 22.5
2.8 3.3
1.4 1.8
1.81.2 1.21.1 1.71.5
0.86 2.71.5 2.20.93 4.90.98 3.52.4
0.620.45 2.72.3
2.3
1.5 1.7
0.960.93 1.70.75 3.12.6
0.35 0.77 1.51.4 2.623.31.8 2.7
1
0.54 2.1
22
0.8 11.61.1 1.90.97 2.31.3
1.4 2.2
0.990.35 20.92 2.41.5 1.40.57 1.3 2.3
1.81.72.1 2.6
0.95 1.2 20.85 5.4
1.60.83 1.90.93 3.42.3 2.123.12.7
1.20.76 10.72 1.21.1 1.10.73 22.4
1.6 5.2
AGR
IND
SRV
0.1 1 10 0.1 1 10 0.1 1 10
ZWE
ZMB
ZAF
UGA
TZA
TUN
TGO
TCD
SYC
SWZ
STP
SSD
SLE
SEN
SDN
RWA
NGA
NER
NAM
MWI
MUS
MRT
MOZ
MLI
MDG
MAR
LSO
LBR
KEN
GNQ
GNB
GMB
GIN
GHA
GAB
ETH
EGY
DZA
DJI
CPV
COM
COG
COD
CMR
CIV
CAF
BWA
BFA
BEN
BDI
AGO
Sec o al Vola ili y Con ibu ion: MAD(∆y Y −1)
ISO3
1990−2004 2005−2019
Da a Sou ce: Wo ld De elopmen Indica o s, Accessed No embe 2021
Table C9: Regions in A ica (51 Coun ies wi h Sec o al Da a)
Region Coun ies ISO3
Eas e n A ica BDI, COM, DJI, ETH, KEN, MDG, MOZ, MUS, MWI, RWA,
SSD, SYC, TZA, UGA, ZMB, ZWE
Middle A ica AGO, CAF, CMR, COD, COG, GAB, GNQ, SDN, STP, TCD
No he n A ica DZA, EGY, MAR, TUN
Sou he n A ica BWA, LSO, NAM, SWZ, ZAF
Wes e n A ica BEN, BFA, CIV, CPV, GHA, GIN, GMB, GNB, LBR, MLI,
MRT, NER, NGA, SEN, SLE, TGO
42
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 48
Figu e C17: Sec o al G ow h Risk by Region
AGR
IND
SRV
GDP
MAD_G c b
MAD_G ow h
Eas e n A ica
Middle A ica
No he n A ica
Sou he n A ica
Wes e n A ica
Eas e n A ica
Middle A ica
No he n A ica
Sou he n A ica
Wes e n A ica
Eas e n A ica
Middle A ica
No he n A ica
Sou he n A ica
Wes e n A ica
Eas e n A ica
Middle A ica
No he n A ica
Sou he n A ica
Wes e n A ica
0
1
2
3
0
3
6
9
12
Region
Value
Pe iod
1990−2004
2005−2019
Da a Sou ce: Wo ld De elopmen Indica o s, Accessed No embe 2021
Table C10: Sec o al G ow h S abiliza ion By Region
(1) MAD(∆y /Y −1) (2) MAD(%∆y )
Region Pe iod N AGR IND SRV AGR IND SRV
Eas e n 1990-2004 13 2.01 1.04 1.57 5.28 5.84 4.69
Eas e n 2005-2019 16 0.77 0.78 1.19 3.45 4.62 2.54
Middle 1990-2004 8 1.34 1.84 2.51 5.28 7.30 6.12
Middle 2005-2019 10 0.51 2.42 2.21 5.04 5.11 5.57
No he n 1990-2004 4 1.44 1.15 1.07 11.65 3.36 1.98
No he n 2005-2019 4 0.54 1.42 0.96 6.37 4.40 1.85
Sou he n 1990-2004 5 0.66 1.95 1.92 8.06 7.94 3.50
Sou he n 2005-2019 5 0.56 2.01 1.47 7.00 6.18 2.48
Wes e n 1990-2004 16 1.63 1.00 1.64 6.28 5.74 3.55
Wes e n 2005-2019 16 1.16 1.06 1.43 5.01 5.80 2.98
No e: S a is ics we e agg ega ed ac oss coun ies using he median.
43
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 49
Sec ion 4: Ex e nal, Financial, and Policy Fac o s
Figu e C18: Ex e nal En i onmen : Selec ed Indica o s
Gene al go e nmen g oss deb (% o GDP)
Ex e nal deb s ocks (% o GNI)
To al deb se ice (% o GNI)
Ne ba e e ms o ade index (2000 = 100)
Fo eign di ec in es men , ne in lows (% o GDP)
Pe sonal emi ances, ecei ed (% o GDP)
1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020
0.4
0.8
1.2
1.6
2.0
2.4
1
2
3
4
5
6
7
0.0
0.5
1.0
1.5
2.0
2.5
3.0
20
30
40
50
60
70
80
90
100
110
120
130
140
150
30
40
50
60
70
80
90
100
110
Yea
5−Yea Rolling A e age o C oss−Coun y Median
Weigh s
None
GDP
POP
Te ms o T ade, FDI, Remi ances and Deb in A ica, 1990−2019
Da a Sou ce: IMF and Wo ld Bank. Accessed h ough he a icamoni o API.
Table C11: Co ela ions wi h Ex e nal En i onmen Indica o s
Mean: ToT FDI REM GGDT EDT EDS
Median PC G ow h .065* .177* .053 -.271* -.208* -.045
MAD PC G ow h -.116* -.052 -.074* .091* .104* .039
Median In la ion -.057* -.039 -.083* .117* .187* .084*
MAD In la ion -.077* -.031 -.017 .098* .067* .035
No es: A 10-yea MA wi h da a om 1981 is used o smoo h he a iables shown
in Figu e C18 (in % o GDP/GNI e ms), and 10-yea olling medians and MADs o
pe -capi a g ow h and in la ion. These olling se ies a e hen s anda dized wi hin each
coun y, and i s -di e enced. Pai wise Pea son’s co ela ions a e compu ed on hese
i s di e ences ac oss all coun ies. A s a deno es signi icance a he 5% le el.
Figu e C19: Ex e nal En i onmen Vola ili y: Selec ed Indica o s
Fo eign Di ec In es men , Ne In lows (US$)
Pe sonal Remi ances, Recei ed (US$)
Cu en Accoun Balance (US$ Bn)
Exchange Ra e, Pe iod A e age (LCU pe US$)
Ne Ba e Te ms o T ade Index (2000 = 100)
Me chandise T ade Balance (US$)
1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020
40
50
60
70
80
70
80
90
100
110
120
130
140
150
160
6
7
8
9
10
11
12
20
25
30
35
40
45
8
10
12
14
16
18
20
22
60
80
100
120
140
160
180
Yea
Rolling IQR o he G ow h Ra e*
Weigh s
None
GDP
POP
Vola ili y o he G ow h Ra e o Selec ed Ex e nal Va iables in A ica, 1990−2019
Da a Sou ce: IMF and Wo ld Bank. Accessed h ough he a icamoni o API.
*No e: Plo s show a 5−yea MA o he c oss−coun y (weigh ed) median o a 10−yea olling IQR o he g ow h a e o he se ies.
44
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 50

Table C12: Co ela ions wi h Ex e nal En i onmen Vola ili y Indica o s
MAD: E PA ToT TB FDI REM CAB
Median PC G ow h -.173* -.202* -.116* -.034 -.115* .000
MAD PC G ow h .043 .263* .093* .206* .265* .153*
Median In la ion .911* .299* .058 .134* .243* .048
MAD In la ion .915* .295* .032 .132* .321* .060*
No es: 10-yea olling medians and MADs o he g ow h a es o he da a om 1981
a e compu ed o each coun y and ela ed h ough pai wise Pea son’s co ela ions
ac oss all coun ies. A s a deno es signi icance a he 5% le el.
Figu e C20: Rese es and Financial Dep h: Selec ed Indica o s
Domes ic c edi o p i a e sec o (% o GDP)
B oad money (% o GDP)
Bank liquid ese es o bank asse s a io (%)
G oss Na ional Sa ings (% o GDP)
To al ese es (% o o al ex e nal deb )
To al ese es in mon hs o impo s
1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020
2
3
4
5
6
7
8
9
10
19
20
21
22
23
24
25
26
0
50
100
150
200
250
300
350
400
30
35
40
45
50
55
14
16
18
20
22
24
26
15
20
25
30
35
40
45
50
Yea
5−Yea Rolling A e age o C oss−Coun y Mean
Weigh s
None
GDP
POP
To al Rese es and Financial Dep h in A ica, 1990−2019
Da a Sou ce: IMF and Wo ld Bank. Accessed h ough he a icamoni o API.
Table C13: Co ela ions wi h Financial Indica o s
Mean: GNS TR EDT TR MIM PSC BM BLR A
Median PC G ow h .073* .249* .093* .086* .072* -.017
MAD PC G ow h -.024 -.077* .060 -.106* -.078* .035
Median In la ion -.056* -.160* -.050 -.098* -.090* -.011
MAD In la ion -.004 -.035 .006 -.074* -.078* -.126*
No es: A 10-yea MA wi h da a om 1981 is used o smoo h he a iables shown in Figu e C20,
and 10-yea olling medians and MADs o pe -capi a g ow h and in la ion. These olling se ies
a e hen s anda dized wi hin each coun y, and i s -di e enced. Pai wise Pea son’s co ela ions
a e compu ed on hese i s di e ences ac oss all coun ies. A s a deno es signi icance a he 5%
le el.
45
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 51
Figu e C21: In la ion Ta ge ing in A ica
Sou h A ica
Uganda
Ghana
Seychelles
1990 2000 2010 2020 1990 2000 2010 2020
1990 2000 2010 2020 1990 2000 2010 2020
0
10
20
30
0
10
20
30
40
20
40
60
4
8
12
Yea
CPI In la ion
IT
FALSE
TRUE
In la ion Ta ge e s in A ica, 1990−2020
Da a Sou ce: IMF Wo ld Economic Ou look, Oc obe 2021
Figu e C22: Exchange Ra e Regimes in A ica, 1990-2019
0%
25%
50%
75%
100%
1990 1995 2000 2005 2010 2015
Da e
Sha e o 53 A ican Coun ies
A angemen
Peg
C awling Peg
C awling Band
Floa
F eely Falling
Dual Ma ke
Exchange Ra e Regimes in A ica, 1990−2019
Da a Sou ce: Ilze zki, Reinha and Rogo (2019)
No es: The igu e shows he ’coa se’ exchange a e egime classi ica ion om Ilze zki e al. (2019) wi h 6 ca ego ies. The
sha e o 53 A ican economies (excl. Sou h Sudan) wi h di e en egimes is compu ed o each yea om 1990-2019.
46
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 52
Table C14: Exchange Ra e 15-Yea Rolling Panel-Dummy-Reg essions, 1990-2019
Dependen Va iable: MAD Real GDP/Capi a G ow h (%) MAD In la ion (%)
Model: (1) (2) (3) (4) (5) (6)
Va iables
C awling Peg -0.5017∗∗∗ -1.030∗∗ -0.8594∗-0.2853 5.676 6.576
(0.1213) (0.4212) (0.4743) (0.4360) (10.57) (10.68)
C awling Band -0.7126∗∗ 0.2733 -0.0654 -0.5408 19.72∗∗ 18.62∗
(0.2486) (0.6678) (0.5721) (2.155) (8.871) (9.523)
Floa -1.227∗∗∗ 1.325 0.7436 -1.756 46.70∗46.38∗
(0.1489) (1.067) (0.7344) (1.161) (22.06) (21.93)
FF + DM 0.3907∗∗∗ 1.288∗∗∗ 0.4496 46.42∗∗ 117.5∗∗∗ 114.7∗∗∗
(0.1044) (0.3280) (0.2781) (17.21) (26.13) (27.07)
Fixed-e ec s
Coun y – 52 52 – 52 52
Yea – – 15 – – 15
Fi s a is ics
Obse a ions 751 751 751 759 759 759
R20.026 0.733 0.743 0.198 0.474 0.477
Wi hin R20.030 0.010 0.254 0.220
D iscoll & K aay (1998) (L=1) s anda d-e o s in pa en heses Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
A g. Coun y G oup Sizes: Peg: 22.8, C awling Peg: 17, C awling Band: 6.4, Floa : 1, FF: 2.9, DM: 0.9
No es: 15-yea MAs o he exchange egime dummies on da a om 1990-2019 ( e aining 15 obse a ions pe coun y)
a e eg essed on o 15-yea olling MADs o GDP pe capi a g ow h and CPI in la ion. Da a om WEO, Oc . 21.
Figu e C23: Mac op uden ial Measu es in A ica
0.50
0.55
0.60
0.65
0.70
0.75
1995 2000 2005 2010 2015 2020
Yea
A e age Ac oss 18 A ican Coun ies
Type
O e all
In low
Ou low
Mac op uden ial Policy Measu es in A ica, 1995−2019
Da a Sou ce: Fe nandez, Klein, Rebucci, Schindle and U ibe (2016, 2021)
Figu e C24: Disagg ega ed Mac op uden ial Measu es in A ica
0.45
0.50
0.55
0.60
0.65
2004 2008 2012 2016 2020
Yea
10−Yea MA o A g. o 18 A ican Coun ies
Type
Equi y
Bond
Money Ma ke
Collec i e In es men s
De i a i es
Comme cial C edi s
Financial C edi s
Gua an ees
Di ec In es men (FDI)
Real Es a e
Disagg ega ed O e all Mac op uden ial Res ic ions in A ica, 1995−2019
Da a Sou ce: Fe nandez, Klein, Rebucci, Schindle and U ibe (2016, 2021)
47
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 53
Table C15: Mac op oden ial Policy: 10-Yea Rolling Panel-Reg essions, 1995-2019
Dependen Va iables: MAD Real GDP/Capi a G ow h (%) MAD In la ion (%)
Model: (1) (2) (3) (4) (5) (6)
Va iables
O e all Measu es 0.1724 -5.095∗∗∗ -5.368∗∗∗ 2.497 -10.23∗-12.17∗∗∗
(0.1264) (0.7261) (0.7969) (1.966) (4.871) (3.644)
R20.004 0.512 0.602 0.007 0.344 0.388
Wi hin R20.153 0.196 0.004 0.007
In low Measu es 0.3058∗∗∗ -0.5994 -2.215∗∗∗ 9.942∗-7.188∗∗∗ -21.25∗∗∗
(0.0654) (0.7604) (0.5578) (5.142) (2.314) (2.018)
Ou low Measu es -0.0717 -4.096∗∗∗ -3.050∗∗∗ -5.008∗∗ -3.473 5.768
(0.1187) (1.034) (0.9308) (1.978) (3.277) (4.398)
R20.006 0.520 0.603 0.035 0.344 0.392
Wi hin R20.167 0.197 0.004 0.013
Fixed-e ec s
Coun y – 18 18 – 18 18
Yea – – 16 – – 16
Obse a ions 288 288 288 287 287 287
D iscoll & K aay (1998) (L=2) s anda d-e o s in pa en heses Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
No es: 10-yea olling MADs o GDP pe capi a g ow h and CPI In la ion om he WEO Oc . 21 a e eg essed on o 10-yea
MAs o o e all, in low and ou low measu es aken om he mac op uden ial da abase o Fe n´andez e al. (2016) (Augus
2021 upda e) and a ailable o 18 A ican economies: Alge ia, Angola, Bu kina Faso, Co e d’I oi e, Egyp , E hiopia, Ghana,
Kenya, Kingdom o Eswa ini, Mau i ius, Mo occo, Nige ia, Sou h A ica, Tanzania, Togo, Tunisia, Uganda, and Zambia.
Figu e C25: The Adop ion o Fiscal Rules in A ica
Any
Na ional
Sup ana ional
1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020
0
5
10
15
20
0
2
4
6
0
5
10
15
20
25
Yea
Numbe o A ican Coun ies Subjec o a Rule
Type
Expendi u e
Re enue
Budge Balance
Deb
Fiscal Rules in A ica, 1995−2021
Da a Sou ce: IMF Fiscal Rules Da ase , 2022
Figu e C26: Impo an Mac oeconomic and Fiscal Agg ega es, 1990-2019
Cu en Accoun Balance
Go e nmen Budge Balance
Go e nmen G oss Deb
1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020
25
50
75
100
125
150
−4.0
−3.5
−3.0
−2.5
−2.0
−1.5
−1.0
−0.5
−6
−5
−4
−3
−2
−1
0
Yea
% o GDP: 10−Yea MA o Coun y Medians
Weigh s
None
GDP
POP
Da a Sou ce: IMF Wo ld Economic Ou look, Oc obe 2021
48
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 54
Table C21: RF Ranking o Indica o Topics: P edic ing he MAD-Di e ence o PCGDP G ow h
Me hod: Pe mu a ion Exclusion Residual Fi Combined
Topic %∆MSE Rank %∆MSE Rank %∆MSE Rank A g. Rank
O he s 63.47 3 2.29 2 16.29 1 2.00
Financial Sec o 68.48 2 7.05 1 7.84 7 3.33
Popula ion 44.62 4 0.37 6 8.83 4 4.67
Heal h 34.72 6 0.37 5 1.21 11 7.33
Ins i u ions 23.60 14 0.47 3 8.65 6 7.67
Educa ion 24.29 13 0.30 7 13.37 3 7.67
T ade In ensi y and Composi ion 74.42 1 -1.25 15 5.06 8 8.00
Clima e & Ag icul u e 30.06 8 -0.28 12 8.75 5 8.33
Deb & Rese es 28.69 9 -1.38 16 13.44 2 9.00
T ade Di e si ica ion 33.82 7 -0.89 13 1.53 10 10.00
Na u al Disas e s & Con lic 39.17 5 -0.09 10 -3.17 17 10.67
Exchange Ra e and ToT 27.29 10 -0.05 9 -0.87 15 11.33
Financial & Aid Flows 24.47 12 -0.15 11 0.78 12 11.67
Po e y & Inequali y 16.19 16 0.42 4 -2.98 16 12.00
Na u al Resou ces 25.97 11 -1.52 17 3.30 9 12.33
Business En i onmen 11.88 17 0.26 8 0.07 13 12.67
P oduc ion Sha es 16.27 15 -0.92 14 -0.01 14 14.33
Figu e C31: RF P edic ing he MAD-Di e ence o CPI In la ion o 49 A ican Economies
MAD Di (Remi ances in % o GDP)
Domes ic C edi o P i a e Sec o (% o GDP)
U ban Popula ion (% o To al Popula ion)
MAD Te ms o T ade G ow h (%)
B oad Money (% o GDP)
Adul Li e acy (% o People Ages 15+)
Theil Index o Bila e al T ade (X+M)
Me chandise Impo s om HICs (% o GDP)
Manu ac u es Expo s (% o GDP)
Ln(GDP pe Pe son Employed)
Mean Yea s o Schooling
In e na ional Mig an S ock (% o Popula ion)
Na u al Disas e s: Ln(N. Dea hs)
Li e Expec ancy a Bi h, To al (Yea s)
Te ms o T ade G ow h (%)
Pe sonal Remi ances, Recei ed (% o GDP)
Re ised Combined Poli y Sco e
The P ope y Righ P o ec ion Index
Human De elopmen Index
Human Capi al Index
% Poo a $1.90 a Day (2011 PPP)
Ex e nal Deb S ocks (% o GNI)
Me chandise Expo s o HICs (% o GDP)
In an Mo ali y Ra e (pe 1000 Li e Bi hs)
Gene al Go e nmen G oss Deb (% o GDP)
Me ch. EX o LMICs Ou side Region (% o GDP)
Wo ldwide Go e nance Indica o s: PC1
Na u al Disas e s: Ln(N. Homeless)
Exchange Ra e G ow h (%)
MAD Nominal Exchange Ra e Dep ecia ion (%)
% Inc ease in Mean Squa ed E o om Pe mu ing he Va iable
012
Top 30 P edic o s om a RF Model wi h 70 Va iables, 100k T ees and 3 Va iables pe Spli . OOB R−Squa ed = 3.2%.
55
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 61

Table C22: RF Ranking o Indica o Topics: P edic ing he MAD-Di e ence o CPI In la ion
Me hod: Pe mu a ion Exclusion Residual Fi Combined
Topic %∆MSE Rank %∆MSE Rank %∆MSE Rank A g. Rank
Exchange Ra e and ToT 62.34 2 7.87 1 36.45 1 1.33
Popula ion 68.14 1 -0.01 7 2.10 3 3.67
Deb & Rese es 61.76 3 -1.12 15 2.89 2 6.67
Ins i u ions 22.22 13 0.55 2 1.35 6 7.00
Na u al Disas e s & Con lic 37.33 9 0.26 4 0.79 9 7.33
T ade In ensi y and Composi ion 39.68 7 0.29 3 -1.69 13 7.67
P oduc ion Sha es 38.81 8 0.05 6 0.41 10 8.00
Educa ion 45.36 4 -0.03 8 -1.80 15 9.00
Na u al Resou ces 4.70 17 0.18 5 1.43 5 9.00
Clima e & Ag icul u e 32.01 10 -0.84 14 1.25 7 10.33
Financial Sec o 42.28 5 -0.05 9 -5.75 17 10.33
Heal h 25.41 11 -0.37 10 -0.32 11 10.67
Po e y & Inequali y 19.66 14 -1.28 16 1.61 4 11.33
T ade Di e si ica ion 23.90 12 -0.56 12 -1.20 12 12.00
Business En i onmen 5.95 16 -0.67 13 1.23 8 12.33
Financial & Aid Flows 42.13 6 -1.29 17 -2.60 16 13.00
O he s 13.40 15 -0.42 11 -1.72 14 13.33
C oss-Sec ional P edic ion: Wi h Fi s 2 P incipal Componen s o Each Topic
Table C23: Pe cen Va iance Explained by Fi s 2 P incipal Componen s
% Va iance Explained
Topic N PC1 PC2 To al
Ins i u ions (excl. Colonial O igin) 7 66.86 16.29 83.16
Business En i onmen 4 76.24 13.38 89.62
P oduc ion Sha es 2 82.30 17.70 100.00
Clima e & Ag icul u e 8 34.76 21.51 56.27
T ade In ensi y and Composi ion 7 35.04 20.03 55.07
T ade Di e si ica ion 4 51.14 27.62 78.75
Exchange Ra e and ToT 5 42.81 34.21 77.02
Financial & Aid Flows 5 40.12 34.34 74.46
Financial Sec o 6 40.30 24.56 64.86
Deb & Rese es 4 38.84 27.70 66.54
Popula ion 6 39.68 24.03 63.71
Heal h 5 73.36 12.26 85.63
Educa ion 5 73.77 18.01 91.79
Na u al Disas e s & Con lic 6 52.65 18.79 71.44
Geog aphy & Accessibili y 7 37.78 28.31 66.09
Na u al Resou ces 2 93.18 6.82 100.00
Po e y & Inequali y 3 68.30 30.29 98.59
Religion & E hnici y 4 60.32 24.71 85.04
O he s 6 69.91 11.03 80.94
A e age 5.05 56.70 21.66 78.37
56
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 62
Table C24: RF Ranking o Indica o Topics: PC12 P edic ing MAD PCGDP G ow h, 1990-2019
Me hod: Pe mu a ion Exclusion Residual Fi Combined
Topic %∆MSE Rank %∆MSE Rank %∆MSE Rank A g. Rank
Financial Sec o 114.50 1 2.48 3 34.07 3 2.33
P oduc ion Sha es 61.84 4 4.47 1 29.96 5 3.33
Ins i u ions 35.02 6 2.84 2 31.12 4 4.00
Financial & Aid Flows 102.31 2 1.65 4 29.21 6 4.00
Na u al Resou ces 30.86 7 0.88 6 38.82 2 5.00
T ade In ensi y and Composi ion 68.14 3 -1.36 14 48.70 1 6.00
Popula ion 19.52 10 0.88 5 12.82 8 7.67
T ade Di e si ica ion 13.92 12 -0.42 8 1.57 14 11.33
Na u al Disas e s & Con lic 35.68 5 -1.10 13 0.78 16 11.33
Business En i onmen 23.65 9 -2.32 17 11.53 10 12.00
Exchange Ra e and ToT 12.02 13 -1.87 16 19.43 7 12.00
Deb & Rese es 17.17 11 -2.60 18 12.00 9 12.67
Clima e & Ag icul u e 11.98 14 -1.37 15 9.50 11 13.33
Geog aphy & Accessibili y 9.80 15 -0.38 7 -10.29 19 13.67
Po e y & Inequali y 3.57 19 -0.72 10 2.19 12 13.67
Educa ion 8.60 16 -0.45 9 -2.46 17 14.00
Religion & E hnici y 5.53 18 -0.91 11 1.63 13 14.00
Heal h 6.07 17 -1.02 12 0.94 15 14.67
O he s 25.18 8 -3.33 19 -3.09 18 15.00
Table C25: RF Ranking o Indica o Topics: PC12 P edic ing MAD CPI In la ion, 1990-2019
Me hod: Pe mu a ion Exclusion Residual Fi Combined
Topic %∆MSE Rank %∆MSE Rank %∆MSE Rank A g. Rank
Exchange Ra e and ToT 225.75 1 19.91 1 42.39 1 1.00
Ins i u ions 58.74 4 1.30 2 23.53 3 3.00
Na u al Disas e s & Con lic 61.13 2 0.61 3 22.97 4 3.00
Business En i onmen 43.39 6 -0.01 5 11.64 7 6.00
Popula ion 15.61 12 -0.01 4 26.71 2 6.00
Geog aphy & Accessibili y 19.32 9 -0.79 9 16.46 6 8.00
Na u al Resou ces 59.37 3 -4.29 18 17.97 5 8.67
O he s 20.09 8 -1.99 14 11.49 8 10.00
Financial Sec o 17.26 11 -0.73 8 0.26 14 11.00
T ade Di e si ica ion 10.41 14 -0.22 6 0.16 15 11.67
Po e y & Inequali y 44.91 5 -4.60 19 7.90 12 12.00
Heal h 11.58 13 -2.06 15 8.44 10 12.67
Clima e & Ag icul u e 5.27 19 -0.30 7 3.23 13 13.00
Educa ion 5.58 18 -0.86 10 8.21 11 13.00
Financial & Aid Flows 17.57 10 -1.54 12 -11.58 18 13.33
Religion & E hnici y 9.38 15 -3.13 17 8.96 9 13.67
Deb & Rese es 38.12 7 -2.63 16 -19.02 19 14.00
T ade In ensi y and Composi ion 9.08 17 -1.44 11 -0.28 16 14.67
P oduc ion Sha es 9.08 16 -1.97 13 -1.67 17 15.33
57
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 63
Panel P edic ion: Wi h Fi s 2 P incipal Componen s o Each Topic
Table C26: Pe cen Va iance Explained by Fi s 2 P incipal Componen s
% Va iance Explained
Topic N PC1 PC2 To al
Ins i u ions 3 47.87 40.35 88.22
Business En i onmen 2 61.01 38.99 100.00
P oduc ion Sha es 2 73.69 26.31 100.00
Clima e & Ag icul u e 5 35.72 21.57 57.29
T ade In ensi y and Composi ion 7 38.79 23.46 62.26
T ade Di e si ica ion 4 43.20 22.48 65.69
Exchange Ra e and ToT 5 40.17 28.43 68.61
Financial & Aid Flows 5 39.57 33.53 73.10
Financial Sec o 5 36.74 27.05 63.79
Deb & Rese es 4 46.07 28.98 75.05
Popula ion 6 44.68 22.30 66.98
Heal h 3 58.92 32.95 91.87
Educa ion 4 40.36 28.70 69.06
Na u al Disas e s & Con lic 5 31.21 20.61 51.82
Na u al Resou ces 2 80.75 19.25 100.00
Po e y & Inequali y 3 68.51 27.15 95.66
O he s 5 29.37 23.56 52.93
A e age 4.12 48.04 27.39 75.43
Table C27: RF Ranking o Indica o Topics: PC12 P edic ing MAD-Di e ence o PCGDP G ow h
Me hod: Pe mu a ion Exclusion Residual Fi Combined
Topic %∆MSE Rank %∆MSE Rank %∆MSE Rank A g. Rank
O he s 83.95 1 1.98 3 11.13 1 1.67
Exchange Ra e and ToT 45.31 4 4.34 1 3.39 6 3.67
Financial Sec o 35.92 7 2.24 2 4.56 3 4.00
Na u al Resou ces 63.17 3 0.55 7 5.68 2 4.00
Ins i u ions 44.60 5 1.21 5 4.00 4 4.67
Na u al Disas e s & Con lic 76.57 2 1.47 4 -5.28 17 7.67
Financial & Aid Flows 12.61 15 0.72 6 2.36 7 9.33
Heal h 25.13 9 -1.76 16 3.97 5 10.00
Deb & Rese es 26.06 8 -0.91 15 1.72 8 10.33
T ade In ensi y and Composi ion 36.89 6 -0.87 14 -2.50 12 10.67
Popula ion 19.11 10 -0.24 11 -1.43 11 10.67
Business En i onmen 13.99 13 -0.07 9 -3.23 13 11.67
Clima e & Ag icul u e 7.50 17 -0.13 10 0.89 9 12.00
P oduc ion Sha es 17.46 12 -0.70 13 -3.26 14 13.00
T ade Di e si ica ion 17.49 11 -0.67 12 -4.90 16 13.00
Po e y & Inequali y 11.56 16 0.09 8 -3.68 15 13.00
Educa ion 13.49 14 -1.91 17 0.43 10 13.67
58
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 64
Table C28: RF Ranking o Indica o Topics: PC12 P edic ing MAD-Di e ence o CPI In la ion
Me hod: Pe mu a ion Exclusion Residual Fi Combined
Topic %∆MSE Rank %∆MSE Rank %∆MSE Rank A g. Rank
Exchange Ra e and ToT 106.41 1 8.15 1 25.13 1 1.00
Deb & Rese es 43.52 5 0.15 6 5.97 2 4.33
P oduc ion Sha es 96.40 2 1.52 2 0.86 12 5.33
Po e y & Inequali y 6.23 13 0.25 4 2.58 6 7.67
Clima e & Ag icul u e 38.44 6 -0.15 8 0.97 11 8.33
Na u al Resou ces 8.82 12 -0.20 10 3.66 3 8.33
Heal h 72.33 3 -0.68 15 1.28 8 8.67
Educa ion 46.45 4 -1.51 17 2.98 5 8.67
Popula ion 36.27 7 1.11 3 -2.30 17 9.00
Na u al Disas e s & Con lic 11.64 11 0.02 7 1.22 9 9.00
Ins i u ions 13.15 10 -0.64 14 2.33 7 10.33
Financial Sec o 24.62 8 -0.48 12 0.42 13 11.00
Business En i onmen 5.09 15 -1.01 16 3.55 4 11.67
T ade Di e si ica ion 13.46 9 -0.64 13 0.13 14 12.00
Financial & Aid Flows 4.99 16 -0.40 11 0.98 10 12.33
T ade In ensi y and Composi ion 5.93 14 -0.19 9 -0.73 15 12.67
O he s 2.60 17 0.25 5 -0.91 16 12.67
59
CHAPTER 2.1. AFRICA’S GREAT MODERATION Page 65
Chap e 3
Regional and Global Economic
In eg a ion

A ica’s Regional and Global In eg a ion
Sebas ian K an z
Augus 16, 2024
Abs ac
This sho pape examines A ica’s egional and global in eg a ion h ough ade, global
and egional alue chains (GVCs and RVCs) a he agg ega e and sec o le els using de ailed
ade da a and he EMERGING MRIO ables. I inds ha he sha e o A ica’s ade wi h
i sel is inc easing and ha p ecious s ones and me als, pe ochemicals, mining, and p ocessed
oods a e d i ing RVCs, wi h high po en ial o u he in eg a ion. Mos RVC ade is wi hin
egional economic communi ies (RECs), pa icula ly inside SADC, implying oppo uni ies o
expand RVCs in o he RECs and es ablish c oss-REC RVCs. The con inen ’s ups eamness in
GVCs has dec eased in many sec o s, sugges ing a end owa ds g ea e local alue-addi ion.
1 G oss T ade Flows
A ica’s sha e o wo ld ade is widely acknowledged o be low. Acco ding o a ecen epo by
he UN Economic Commission o A ica (UNECA),1i is less han 3% o global ade, and mainly
d i en by me chandise ade. A ca e ul inspec ion o wo widely used da abases on me chandise
ade, CEPII’s BACI (Gaulie & Zignago,2010) (HS 1996 e sion) and he IMF’s Di ec ion o
T ade S a is ics (DOTS) (IMF Gene al S a is ics Di ision,1993) da abase, isualized in Figu e 1,
sugges s a highe A ican sha e o 5.5-6.5% in global me chandise ade. In e es ingly, he sha e
was high a 9% un il 1980, hen saw a apid decline o less han 4% in 1995 and a subsequen ise
o abo e 6% in 2012. The ade slowdown in he 80s and 90s is cong uen o he ex ended pe iod o
high in la ion, low commodi y p ices, deb dis ess, s uc u al adjus men , and poli ical ins abili y
commonly e e ed o as he ”los decade(s)” o A ica. The ade spu in he 2000s, on he
o he hand, is aligned wi h a g ow h spu suppo ed by highe commodi y p ices and economic
e o ms, commonly e e ed o as ”A ica Rising” and analyzed in Calde ´on & Bo eux (2016),
Rod ik (2018), and K an z (2023) among o he s. Thus, A ica’s sha e o wo ld ade co- a ies o
a la ge ex en wi h i s agg ega e mac oeconomic pe o mance.
Figu e 1: A ica’s Sha e o Global Me chandise T ade
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
10.0%
1960 1970 1980 1990 2000 2010 2020
Yea
A ica's Sha e in Wo ld T ade
Flow
Expo s (BACI)
Expo s (DOTS)
Impo s (DOTS)
No es: Figu e shows A ica’s sha e in global me chandise ade om di e en da abases. The DOTS is an agg ega e
da abase de i ed om o icial sou ces, whe eas BACI is a p oduc -le el da abase de i ed om COMTRADE.
1h ps://www.uneca.o g/s o ies/a ican-coun ies- ading-mo e-ou side- he-con inen - han-amongs -
hemsel es%2C-eca- epo
1
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 67
The e is also no able a ia ion in he ex en o A ican ade wi h i sel e sus wi h he es o he
Wo ld (ROW). Figu e 2shows he inne -A ican and A ica-ROW ade le els in cons an 2015 USD
acco ding o DOTS, and below i he a io o hese lows oge he wi h a lowess end. E iden ly,
he a io (in pe cen age e ms) o inne -A ican ade o A ica-ROW has isen conside ably since
1980, om 5% in 1980 o 20% o Expo s and 15% o impo s in 2020. The ade balance o
A ica wi h i sel has been posi i e since 2010 and nega i e wi h ROW.
Figu e 2: A ican T ade wi h I sel and ROW
Expo s
Impo s
T ade Balance
1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020
−$150 B
−$100 B
−$50 B
$0 B
$50 B
$100 B
$0 B
$100 B
$200 B
$300 B
$400 B
$500 B
$0 B
$100 B
$200 B
$300 B
$400 B
$500 B
$600 B
Yea
Value
Flow: Inne −A ican A ica−ROW
Expo s
Impo s
T ade Balance
1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020
−50%
0%
50%
100%
150%
200%
5.0%
7.5%
10.0%
12.5%
15.0%
17.5%
5%
10%
15%
20%
25%
Yea
Ra io
No es: Figu e shows inne -A ican and A ica-ROW ade lows acco ding o DOTS da a ( op panel), and he a io o
inne -A ican o A ica-ROW ade including a smoo h lowess end (bo om panel).
Figu e 3: Inne -A ican T ade Sha e in To al A ican T ade by Coun y: 2010-2022 A e ages
A ican Sha e in Coun y T ade
SWZ
GMB
ZWE
TGO
DJI
MLI
UGA
RWA
COD
NAM
SEN
KEN
LSO
TZA
NER
MWI
BEN
BDI
ZAF
CIV
GHA
MOZ
ZMB
GAB
ETH
COG
MUS
BWA
CAF
COM
SLE
BFA
EGY
CMR
TUN
SDN
NGA
GIN
GNB
MAR
MDG
MRT
SOM
STP
SYC
DZA
GNQ
AGO
LBR
CPV
LBY
ERI
TCD
SSD
0%
10%
20%
30%
40%
50%
60%
70%
80%
Flow
EX
IM
Coun y Sha e in To al Inne −A ican T ade
SWZ
GMB
ZWE
TGO
DJI
MLI
UGA
RWA
COD
NAM
SEN
KEN
LSO
TZA
NER
MWI
BEN
BDI
ZAF
CIV
GHA
MOZ
ZMB
GAB
ETH
COG
MUS
BWA
CAF
COM
SLE
BFA
EGY
CMR
TUN
SDN
NGA
GIN
GNB
MAR
MDG
MRT
SOM
STP
SYC
DZA
GNQ
AGO
LBR
CPV
LBY
ERI
TCD
SSD
0.01%
0.10%
1.00%
10.00%
30.00%
Flow
EX
IM
No es: Using DOTS da a, Figu e shows he A ican sha e in coun ies’ ade, and coun ies’ sha e in inne -A ican ade.
2
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 68
Figu e 3 u he disagg ega es inne -A ican ade sha es by coun y, exp essed as a sha e o
o al coun y expo s/impo s a he han a a io o he A ica-ROW ade as in Figu e 2. Figu e 3
unco e s conside able he e ogenei y in coun ies’ sha e o ade wi h A ican pa ne s ( op panel)
and coun ies’ o al sha e o inne -A ican ade (bo om panel). Wi h sha es o 30% on bo h
me ics, Sou h A ica is he mos signi ican egional ade , ollowed by Nige ia and Congo.
A u he le el o he e ogenei y in A ican ading is he sha e o inne -A ican ade wi hin
o be ween egional economic communi ies (REC), which ha e played a signi ican ole in he
con inen ’s economic de elopmen and egional in eg a ion agenda up o his poin . To acili a e
egional ade analysis, Table 1p o ides a mu ually exclusi e classi ica ion o coun ies in o ei he
hei mos impo an economic union o he union o closes geog aphic p oximi y.
Table 1: Classi ica ion o A ican Coun ies in o RECs
REC Desc ip ion Coun ies
AMU+EGY A ab Magh eb Union + Egyp DZA, EGY, LBY, MRT, MAR, TUN, ESH
CEMAC+STP Economic Communi y o Cen al A ican
S a es + S˜ao Tom´e and P ´ıncipe
CMR, CAF, TCD, COG, GNQ, GAB,
STP
EAC Eas A ican Communi y BDI, COD, KEN, RWA, SSD, TZA, UGA
ECOWAS Economic Communi y o Wes A ican
S a es
BEN, BFA, CPV, CIV, GMB, GHA, GIN,
GNB, LBR, MLI, NER, NGA, SEN, SLE,
TGO
IGAD-EAC In e go e nmen al Au ho i y on
De elopmen , excluding EAC Mmebe s
DJI, ERI, ETH, SOM, SDN
SADC-COD Sou he n A ican De elopmen
Communi y, excluding he DRC (now
EAC)
AGO, BWA, COM, SWZ, LSO, MDG,
MWI, MUS, MOZ, NAM, SYC, ZAF,
ZMB, ZWE
No es: Table p o ides mu ually exclusi e classi ica ion o coun ies in o RECs. The REC name e lec s de ia ions om o icial membe ship.
Figu e 4decomposes inne -A ican expo s be ween and wi hin RECs. The a io o wi hin- o
be ween-REC expo s shown on he RHS indica es ha he pe iod om 1960-1990 was cha ac e ized
by inc easing ade wi hin egional blocks - om 1.2 imes g ea e in 1960 o 2.75 imes g ea e
han be ween-REC ade in 1990. A e 1990, ade be ween RECs picked up again and appea ed
o s abilize a a a io o 2 in 2020. Thus, ade in RECs in A ica is cu en ly 2 imes g ea e han
ade be ween RECs.
Figu e 4: Inne -A ican T ade Be ween and Wi hin RECs
0
10
20
30
40
50
60
70
1960 1970 1980 1990 2000 2010 2020
Yea
Expo s in USD Billions
Inne −A ican Expo s: Wi hin RECs Be ween RECs
0.5
1.0
1.5
2.0
2.5
3.0
1960 1970 1980 1990 2000 2010 2020
Yea
Ra io o Expo s Wi hin / Be ween A ican RECs
No es: Figu e plo s inne -A ican ade wi hin and be ween RECs acc oding o DOTS da a (LHS) and hei a io (RHS).
A he REC le el, i is easible o isualize he ade lows. Figu e 5does his in 3 ways,
depending on whe he expo s o ROW and wi hin he REC a e included. The LHS indica es ha
he AMU+EGY, SADC-COD, and ECOWAS ha e he la ges expo s o ROW. SADC-COD has
he la ges inne -REC expo s, a a olume o 32 billion USD’15, ollowed, wi h some dis ance, by
ECOWAS wi h inne -REC expo s abo e 10.6 billion. The AMU+EGY and EAC ha e inne REC
expo s o 6.5 and 5.2 billion, espec i ely. Howe e , hese RECs ha e di e en economic sizes, so
hei o al ade olumes may no be indica i e o hei ex en o egional in eg a ion. A e aging
he GDP in cons an 2015 USD o he RECs o e he same pe iod (2010-22) yields a GDP o 736
3
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 69
billion o AMU+EGY, 673 billion o ECOWAS, 545 billion o SADC-COD, 225 billion o he
EAC, 168 billion o IGAD-EAC, and 82 billion o CEMAC+STP. The bo om panel o Figu e 5
shows ade lows in pe cen o he o igin REC GDP.
Figu e 5: (Inne -)A ican T ade (Expo s) by RECs: 2010-2022 A e ages in 2015 USD Billions
REC EX o Sel , O he s, and ROW REC EX o Sel and O he s REC EX o O he s
0
30
60
90
120
150
0
30
0
30
0
30
60
90
120
0
0
30
60
90
120
150
180
210
0
30
60
90
120
150
180
210
240
270
300
330
360
390
420
AMU+EGY
CEMAC+STP
EAC
ECOWAS
IGAD−EAC
SADC−COD
ROW
0
5
10
15
0
5
0
5
10
15
20
0
5
10
15
20
25
30
0
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
AMU+EGY
CEMAC+STP
EAC
ECOWAS
IGAD−EAC
SADC−COD
0
2
4
6
0
2
4
0
2
4
6
8
10
0
2
4
6
8
10
12
0
2
0
2
4
6
8
10
12
14
16
18
20
AMU+EGY
CEMAC+STP
EAC
ECOWAS
IGAD−EAC
SADC−COD
In Pe cen o O igin REC GDP (2010-22 A e age)
0
8
16
0
8
16
24
32
40
0
8
0
8
16
0
0
8
16
24
32
40
0
8
16
24
32
40
48
56
64
72
80
88
96
104
AMU+EGY
CEMAC+STP
EAC
ECOWAS
IGAD−EAC
SADC−COD
ROW
0
1
2
3
0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
0
1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
AMU+EGY
CEMAC+STP
EAC
ECOWAS
IGAD−EAC
SADC−COD
0
0.5
1
0
0.5
1
1.5
2
2.5
3
0
0.5
1
1.5
2
2.5
3
0
0.5
1
1.5
2
2.5
3
0
0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
AMU+EGY
CEMAC+STP
EAC
ECOWAS
IGAD−EAC
SADC−COD
No es: Figu e shows mig a ion low diag ams isualizing REC-le el expo s. The op panel p o ides h ee di e en
diag ams depending on whe he inne -REC expo s and expo s o ROW a e included. The bo om panel p o ides he
same diag ams bu wi h lows in pe cen o he expo ing REC’s GDP. Expo s da a is aken om DOTS, and GDP is
om he Wo ld De elopmen Indica o s. Bo h a e a e aged be ween 2010 and 2022 o smoo h empo al a ia ion.
Su p isingly, CEMAC+STP has he g ea es expo pene a ion a 41% o GDP, o which 34%
is des ined o ROW, 2.7% is in e nal ade, 1.5% o SADC-COD, and 1.2% o ECOWAS. I
is ollowed by SADC-COD, wi h a o al expo pene a ion o 33.2%, o which 26.2% o ROW.
SADC-COD main ains he la ges inne -REC ade sha e a 5.8% o GDP, and also expo s 0.7%
and 0.3% o GDP o he EAC and ECOWAS, espec i ely. The EAC emains he g ea es in e -
REC ade , a 6.9% o i s GDP expo ed o ROW, 2.3% egional ade, and a la ge sha e o 2% o
GDP is expo ed o SADC-COD, ollowed, wi h some dis ance, by 0.23% o GDP expo s o IGAD-
EAC. ECOWAS has an expo pene a ion o 17.2%, o which 14.% is o ROW, 1.6% egional,
1.1% o SADC-COD and 0.16% o CEMAC+STP. The AMU+EGY ades 20.3% o GDP, bu
18.9% wi h ROW a only 0.9% egional ade, 0.23% o ECOWAS, and 0.13% o IGAD-EAC.
IGAD-EAC has he lowes expo pene a ion a 6.6% o GDP, o which 5.7% is o ROW, 0.5%
egional, and 0.4% o he AMU+EGY. O e all, he absence o di ec eas -wes ade is s iking
in his pic u e. The EAC does no ade meaning ul quan i ies wi h ECOWAS o CEMAC+STP,
indica ing he exis ence o la ge ade ba ie s in cen al A ica. No h-Sou h ade is also sca ce,
likely e lec ing physical and cul u al ba ie s and he p oximi y o he AMU+EGY o Eu ope and
he Middle Eas . The expo s be ween AMU+EGY and SADC+COD, alued a 500-600 million
and below 0.1% o GDP om bo h sides, a e also insigni ican in ela i e magni ude.
4
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 70
Le Ebe a ec o o sec o -le el g oss expo s, hen VBE is he ma ix o sec o -le el expo s
(columns) decomposed by o igin o VA coun y-sec o ( ows) wi h elemen s beoi,uj. F om i ,
ollowing Hummels e al. (2001) and Baldwin & Lopez-Gonzalez (2015), we can de i e simple
indica o s o he impo ed expo s (I2E) and e-expo ed expo s (E2R) sha es.
I2Euj =1
Euj X
oi,o=u
beoi,uj ∀uj (5)
E2Roi =1
Eoi X
uj,u=o
beoi,uj ∀oi, (6)
The impo measu e is equal o he sha e o backwa d GVC pa icipa ion [I2E = (GX -
DVA)/GC]. The expo measu e is app oxima ely equal o he o wa d GVC pa icipa ion sha e
bu imp ecise because i includes double-coun ed componen s. These a e, howe e , ela i ely small
in A ica. Using hese measu es, I ob ain he sha es o A ican pa ne s in coun ies’ backwa d
and o wa d GVC pa icipa ion. Le AFR deno e he se o all A ican coun ies, hen
I2EAFR
uj =X
oi∈AFR,o=u
beoi,ujX
oi,o=u
beoi,uj ∀uj ∈AFR,(7)
E2RAFR
oi =X
uj∈AFR,u=o
beoi,ujX
uj,u=o
beoi,uj ∀oi ∈AFR.(8)
a e ela i e sha es acking he A ican sha e in coun ies’ o wa d and backwa d GVC pa icipa ion.
The LHS o Figu e 13 shows I2EAFR and E2RAFR compu ed a he sec o le el, i.e., he A ican
sha e in o al A ican GVC-expo s wi hin each sec o . The RHS applies hese sha es o he exac
measu es ollowing Bo in & Mancini (2019), mul iplying FVA, FDC, and DDC wi h I2EAFR and
NDAVAX and REF wi h E2RAFR. In mos sec o s, he RVC sha e is be ween 5% and 20%. Sec o s
WAP, MAN, PCM, and EGW ha e high o wa d pa icipa ion sha es (E2R), whe eas PSM, MPR,
FBE, and PCM ha e high backwa d pa icipa ion sha es (I2E). In absolu e alues, PSM alone
accoun s o 35% o A ican RVCs, a ound
$
7 billion, ollowed by PCM (12.4%), MIN (8.3%), and
FBE (7.5%). I is unlikely ha PSM and MIN RVCs a e sophis ica ed, bu hey highligh g ea
po en ial o p ocess aw ma e ials such as gold, diamonds, and o he o es and p ecious s ones in
A ican coun ies wi h mo e ad anced indus ial acili ies a he han expo ing hem di ec ly o ia
A ican neighbou s. PCM, comp ising mainly plas ics, sal , cemen , e ilize s, oils, and cosme ics,
soaps, and lub ican s (see Figu e 9), and FBE also show high po en ial o RVC deepening.
Figu e 13: A ican RVCs by Sec o : 2015-19 A e age
RVC Sha e in A ican GVC Expo s RVC Rela ed Componen s (SUM)
0%
5%
10%
15%
20%
25%
AFF
FBE
PCM
PSM
MIN
TEX
WAP
MPR
ELM
TEQ
MAN
EGW
SMH
TRA
PTE
CON
FIB
PAO
Sec o
A ican Sha e in A ican GVC Expo s
Te m
E2R
I2E
$0B
$1B
$2B
$3B
$4B
$5B
$6B
$7B
PSM
PCM
MIN
FBE
ELM
MPR
TEQ
TEX
TRA
AFF
SMH
FIB
WAP
PTE
MAN
EGW
PAO
CON
Sec o
GVC Rela ed Expo s Con en s
Te m
NDAVAX
REF
DDC
FVA
FDC
No es: Figu e shows A ican RVCs by sec o , compu ed ia egional backwa d (I2E) and o wa d (E2R) GVC sha es.
To in es iga e how coun ies engage di e en ly in RVCs, I plo hei o e all and sec o -le el
engagemen below. Figu e 14 p o ides he sha e o RVC expo s (bo h o wa d/E2R and backwa d/
I2E RVC in eg a ion) in coun y o al g oss expo s. Os ensibly, o mos coun ies, he RVC sha e
is below 5%, bu a hand ul o coun ies, pa icula ly in SADC, a e highly engaged a expo sha es
close o 30%. The o e all olume o RVC- ela ed lows is also concen a ed in SADC, wi h Sou h
11
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 77

A ica leading a
$
6 billion USD RVC- ela ed expo s, ollowed by Bo swana, Zambia, Zimbabwe,
and Namibia a be ween
$
1 and
$
2 billion. Nige ia ollows a
$
762 million.
Figu e 14: A ican RVCs by Coun y: 2015-19 A e age
$360M
$1160M
$614M
$1080M
$1640M
$1350M
$518M
$356M
$83.8M
$5990M
$246M
$80.2M
$262M
$15.8M
$73.2M
$559M
$218M
$337M
$69.2M
$13.4M
$80.9M
$227M
$31.4M
$118M
$611M
$372M
$101M
$148M
$8.54M
$762M
$86.1M
$459M
$92.8M
$47.1M
$7.16M
$113M
$19.4M
$311M
$26.3M
$73M
$2.09M
$282M
$1.47M
$34.5M
$6.18M
$84.8M
$12.4M
$83.9M
$496M
$445M
$70.1M
$115M
$7.76M
$49.5M
$0.0102M
0%
5%
10%
15%
20%
25%
30%
35%
LSO
ZWE
SWZ
NAM
BWA
ZMB
MLI
MOZ
MWI
ZAF
SEN
TGO
BFA
BDI
NER
CIV
MUS
TZA
SYC
CAF
RWA
UGA
SLE
BEN
GHA
KEN
COG
DJI
GMB
NGA
GIN
TUN
MDG
MRT
GNB
CMR
SOM
AGO
SSD
SDN
COM
DZA
STP
TCD
LBR
GNQ
CPV
ETH
MAR
EGY
GAB
COD
ERI
LBY
ESH
Sec o
RVC Expo s as Sha e o To al Expo s
Te m
E2R
I2E
No es: Figu e shows o al RVC sha e (backwa d (I2E) and o wa d (E2R)) in coun y g oss expo s, including i s alue.
Table 4p o ides a summa y o bila e al RVCs by RECs. E iden ly, he la ges sha e o RVC-
ela ed expo s is by SADC-COD coun ies, ollowed by ECOWAS, AMU+EGY, and he EAC. In
o al, 72% o RVC expo s a e wi hin RECs. O he emaining 28% be ween-REC RVC expo s,
he la ges lows a e be ween ECOWAS and SADC-COD, alued a a ound
$
1-1.4 billion, and
be ween he EAC and SADC-COD, alued a ound
$
450-520 million.
Table 4: A ican RVCs by REC: 2015-19 A e age (USD millions)
Expo e To al AMU+EGY CEMAC+STP EAC ECOWAS IGAD-EAC SADC-COD
AMU+EGY 1777.9 1038.4 64.7 84.7 238.2 103.8 248.1
CEMAC+STP 418.3 79.1 99.1 17.8 123.7 6.6 91.9
EAC 1173.7 95.9 16.0 527.4 49.1 31.6 453.5
ECOWAS 3380.3 278.2 101.1 51.4 1880.9 22.8 1045.9
IGAD-EAC 331.8 137.5 5.7 38.6 20.6 67.7 61.7
SADC-COD 13321.5 289.5 90.9 516.0 1376.0 57.1 10992.0
I also examine RVCs a he coun y-sec o and REC-sec o le els. Figu e 15 shows sec o al
sha es in coun y-le el RVC expo s, wi h he la ges RVC expo e o he le o he cha . The e
is signi ican he e ogenei y in coun ies’ RVC expo con en s. In many coun ies, PSM and MIN
(g een ba s) domina e RVC engagemen . In some smalle coun ies like Rwanda, Cape Ve de, and
Sao Tome, se ices (SRV) expo s domina e. Apa om hese, many coun ies expo AFF, FBE,
and PCM as pa o RVCs. Sou h A ica has he la ges and mos di e si ied RVC expo s.
Figu e 15: A ican RVCs by Coun y: 2015-19 A e age Sec o al Sha es
0%
20%
40%
60%
80%
100%
ZAF ($6110M)
BWA ($1620M)
ZMB ($1320M)
ZWE ($1170M)
NAM ($1070M)
NGA ($750M)
SWZ ($622M)
GHA ($592M)
CIV ($566M)
MLI ($519M)
EGY ($484M)
MAR ($481M)
TUN ($456M)
KEN ($388M)
LSO ($360M)
TZA ($351M)
MOZ ($340M)
AGO ($307M)
DZA ($277M)
BFA ($257M)
SEN ($238M)
MUS ($235M)
UGA ($215M)
DJI ($147M)
COD ($112M)
COG ($108M)
BEN ($107M)
CMR ($103M)
MDG ($92.2M)
GIN ($84.7M)
GNQ ($84.1M)
ETH ($82.5M)
RWA ($79.2M)
TGO ($78.3M)
MWI ($76.9M)
SDN ($73.9M)
GAB ($69.6M)
SYC ($68.4M)
NER ($66.5M)
LBY ($51.1M)
MRT ($45.7M)
TCD ($34.1M)
SLE ($30.7M)
SSD ($26.2M)
SOM ($19.2M)
BDI ($15.4M)
CAF ($13.6M)
CPV ($12.2M)
GMB ($8.89M)
GNB ($7.06M)
ERI ($6.91M)
LBR ($6M)
COM ($2.11M)
STP ($1.45M)
ESH ($0.01M)
Coun y (To al RVC Expo s)
RVC Rela ed Expo s Con en s
Sec o
AFF
FBE
PCM
PSM
MIN
TEX
WAP
MPR
ELM
TEQ
MAN
SRV
12
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 78
Table 5gi es a b eakdown o RVC expo s by REC and sec o . SADC-COD has he la ges
RVC expo s in all sec o s, and in all RECs apa om AMU+EGY and IGAD-EAC, ei he PSM
o MIN is he la ges aded sec o . Howe e , he e a e signi ican di e ences be ween RECs. FBE
RVCs play a signi ican ole in he EAC, whe e hey make up 17% o all RVC expo s. They play
a lesse ole in ECOWAS and SADC-COD a sha es a ound 7.7%. PCM, on he o he hand, is
e y impo an in he AMU+EGY (20%) and also in he EAC, SADC, and ECOWAS (12-13%).
Table 5: A ican RVCs by REC and Sec o : 2015-19 A e age (USD millions)
Sec o To al AMU+EGY CEMAC+STP EAC ECOWAS IGAD-EAC SADC-COD
AFF 749.8 (3.7%) 62.35 (3.5%) 2.65 (0.64%) 70.94 (6%) 108.6 (3.3%) 134.9 (41%) 370.4 (2.8%)
FBE 1596 (7.8%) 79.51 (4.4%) 7.5 (1.8%) 205.9 (17%) 221.7 (6.7%) 25.05 (7.6%) 1056 (7.9%)
PCM 2700 (13%) 357.7 (20%) 21.6 (5.2%) 153.8 (13%) 386.8 (12%) 17.27 (5.2%) 1763 (13%)
PSM 6986 (34%) 101.8 (5.7%) 48.81 (12%) 239 (20%) 1177 (35%) 43.96 (13%) 5375 (40%)
MIN 1774 (8.7%) 296 (16%) 184.6 (45%) 29.98 (2.5%) 787.9 (24%) 9 (2.7%) 466.4 (3.5%)
TEX 884.2 (4.3%) 160.6 (8.9%) 2.85 (0.69%) 47.19 (4%) 138.4 (4.2%) 6.42 (1.9%) 528.8 (3.9%)
WAP 364.2 (1.8%) 36.58 (2%) 32.11 (7.8%) 26.26 (2.2%) 28.5 (0.86%) 7.89 (2.4%) 232.9 (1.7%)
MPR 1133 (5.5%) 51.53 (2.9%) 5.39 (1.3%) 42.66 (3.6%) 41.96 (1.3%) 1.57 (0.48%) 989.7 (7.4%)
ELM 1202 (5.9%) 251.3 (14%) 3.78 (0.91%) 57.93 (4.9%) 25.44 (0.77%) 5.23 (1.6%) 858.5 (6.4%)
TEQ 1071 (5.2%) 77.07 (4.3%) 65.4 (16%) 29.23 (2.5%) 70.99 (2.1%) 4.01 (1.2%) 824.1 (6.2%)
MAN 97.22 (0.48%) 14.51 (0.81%) 0.25 (0.06%) 10.13 (0.85%) 1.62 (0.049%) 0.24 (0.074%) 70.47 (0.53%)
SRV 1889 (9.2%) 306 (17%) 39.12 (9.4%) 273.9 (23%) 333.9 (10%) 73.9 (22%) 862.2 (6.4%)
SUM 20446 (100%) 1795 (100%) 414.1 (100%) 1186.9 (100%) 3322.4 (100%) 329.5 (100%) 13398 (100%)
No es: Table shows RVC con en by REC and sec o , including sec o al sha es in o al REC RVC expo s. Compu ed using VBE sha es.
Ano he c i ical conside a ion is geog aphy, as deepening RVCs is acili a ed by coun ies being
geog aphically close. Towa ds his end, Figu e 16 summa izes coun ies’ agg ega e engagemen .
Panel (A) clea ly shows ha mos RVC engagemen is in SADC, ollowed by ECOWAS and he
EAC. No h A ica is no e y engaged in A ican RVCs. Panel (B) shows he la ges RVC sec o ,
which is MIN o PSM o mos coun ies. I hese wo a e excluded in Panel (C), No h A ican
coun ies mos ly engage in PCM RVCs, and many coun ies in eas e n, wes e n and cen al A ica
mainly engage in se ices RVCs. I se ices a e also excluded in Panel (D), IGAD-EAC ocuses on
ag icul u al RVCs, he EAC is spli be ween FBE and PCM, and SADC and ECOWAs a e mo e
di e se, exemp ing wo geog aphic clus e s: in SADC, FBE is he leading sec o in Zimbabwe,
Zambia, and Malawi, and in ECOWAS ex iles is leading in Benin, Bu kina Faso, and Mali.
To p o ide a de ailed spa ial analysis o impo an RVC sec o s, Figu e 17 shows he o al
alues (in million USD) o RVCs in 6 impo an sec o s. Ag icul u al RVCs in Panel (A) a e
qui e dispe sed, wi h Sou h A ica and Namibia he mos signi ican pa icipan s, bu also sizeable
pa icipa ion in he EAC, E hiopia, and Sudan, as well as Ghana, Mo occo, and Senegal. In FBE,
shown in Panel (B), Zimbabwe is he leading RVC expo e , wi h sizeable con ibu ions in all o
SADC, bu also in he EAC, especially be ween Uganda and Kenya, as documen ed in g ea e de ail
in K an z (2024). In Wes A ica, Ghana con ibu es signi ican ly o he FBE RVC. The PCM RVC
is geog aphically dispe sed, jus like he ag icul u al one. PSM RVCs a e hea ily concen a ed in
sou he n A ica, whe e Bo swana akes lead, bu also exis in wes e n A ica, pa icula ly Mali.
Mining (pe oleum) RVCs in Panel (E) a e domina ed by Nige ia, ollowed by Angola and Alge ia.
Se ices RVCs, shown in Panel (F), a e dispe sed, wi h key con ibu o s Egyp , Mo occo, and
Sou h A ica, Ghana, and Kenya. I end by examining A ica’s (changing) posi ion in GVCs.
2.2 GVC Posi ioning
Following An `as e al. (2012); An `as & Cho (2022), a common measu e o ups eamness Uoi ∈
uis ob ained by i e a ing o wa d he IO model in Eq. 1, mul iplying e ms by he numbe o
p oduc ion s ages needed o ob ain hem, and no malizing by g oss ou pu . In ma ix no a ion:
ux =d+ 2Ad + 3AAd + 4AAAd +··· = (I−A)−2d.(9)
The index is, by de ini ion, g ea e han 1, and An `as e al. (2012) s a e ha i can be in e p e ed
as he dolla amoun by which he ou pu o all coun y-sec o s combined inc eases ollowing a
one-dolla inc ease in he VA o sec o iin coun y o. In ui i ely, i measu es he dis ance o
he p oduc ion s age pe o med by sec o iin coun y o o he inally demanded p oduc (d).4
4An equi alen measu e o downs eamness (d) can be compu ed measu ing he dis ance o VA ins ead o FD
(An `as & Cho ,2022;Mille & Temu shoe ,2017;Mancini e al.,2024), bu , o he sake o b e i y, his is omi ed.
13
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 79
Figu e 16: A ican RVCs To al Engagemen and Main Sec o : 2015-19 A e age
(A) To al RVC Sha e in G oss Expo s (B) La ges RVC Expo ing Sec o
RVCs/GEXP (%)
0.65 o 2.09
2.09 o 3.47
3.47 o 5.38
5.38 o 6.95
6.95 o 8.33
8.33 o 11.05
11.05 o 16.42
16.42 o 24.21
24.21 o 29.61
29.61 o 34.40
Main RVC Sec o
AFF
FBE
PCM
PSM
MIN
TEX
WAP
MPR
ELM
TEQ
MAN
SRV
(C) (B) Excluding [MIN, PSM] (D) (B) Excluding [MIN, PSM, SRV]
Main RVC Sec o
AFF
FBE
PCM
PSM
MIN
TEX
WAP
MPR
ELM
TEQ
MAN
SRV
Main RVC Sec o
AFF
FBE
PCM
PSM
MIN
TEX
WAP
MPR
ELM
TEQ
MAN
SRV
No es: Figu e isualizes coun ies’ RVC sha e in coun y g oss expo s and he sec o wi h he la ges RVC expo s.
An `as e al. (2012) u he ind ha Uis posi i ely co ela ed wi h physical capi al in ensi y and
nega i ely co ela ed wi h skill in ensi y ac oss US indus ies, and nega i ely co ela ed wi h ule
o law, p i a e c edi o GDP, and educa ion ac oss a sample o OECD coun ies.
Figu e 18 shows agg ega e ups eamness by coun y, whe e sec o -le el Uoi es ima es we e
a e aged using g oss expo weigh s. Coun ies wi h a highly concen a ed expo mix in sec o s
such as mining o PSM ha ecei e a lo o downs eam p ocessing, such as Equa o ial Guinea,
Bo swana, Sou h Sudan, and Gabon, a e ela i ely ups eam. A he lowe end o he spec um,
Kenya, Egyp , and Sao Tome mainly expo a el se ices and FBE, which a e close o inal
demand. GVC posi ioning does no necessa ily imply any hing abou he s a e o de elopmen o
economic di e si ica ion, bu ypically, coun ies wi h se ice-led economies a e mo e downs eam.
China, o example, is ela i ely ups eam because i hea ily expo s elec ical machine y, which
is a GVC-in ensi e sec o wi h long GVCs. The op US expo s, on he o he hand, a e inancial
and business se ices, which place i much mo e downs eam.
The simples way o compu ing his index is as d=1′B, i.e., i is he column-sum o he Leon ie in e se ma ix
(Mille & Temu shoe ,2017;An `as & Cho ,2022). I can be in e p e ed as he o al inc ease in g oss ou pu in
he wo ld economy ha a uni inc ease in FD in he espec i e coun y-sec o would gene a e. A he wo ld le el,
uand da e iden ical and measu e he leng h o GVCs (Mancini e al.,2024).
14
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 80
Figu e 17: A ican RVCs in Key Sec o s: 2015-19 A e age
(A) Ag icul u e, Fo es y & Fishing (B) P ocessed Foods & Be e ages
AFF RVC ($M)
0.0 o 1.2
1.2 o 3.0
3.0 o 7.5
7.5 o 12.0
12.0 o 16.4
16.4 o 21.7
21.7 o 29.2
29.2 o 81.8
81.8 o 100.9
100.9 o 216.2
FBE RVC ($M)
0.0 o 4.7
4.7 o 12.5
12.5 o 23.9
23.9 o 37.2
37.2 o 52.2
52.2 o 79.4
79.4 o 97.4
97.4 o 121.7
121.7 o 299.0
299.0 o 337.9
(C) Pe ochemicals (D) P ecious S ones and Me als
PCM RVC ($M)
0 o 5
5 o 16
16 o 29
29 o 41
41 o 62
62 o 77
77 o 92
92 o 180
180 o 369
369 o 1,142
PSM RVC ($M)
0 o 13
13 o 51
51 o 104
104 o 148
148 o 253
253 o 468
468 o 675
675 o 1,154
1,154 o 1,258
1,258 o 1,428
(E) Mining (Pe oleum) (F) Se ices (incl. U ili ies)
MIN RVC ($M)
0.0 o 1.5
1.5 o 8.8
8.8 o 18.3
18.3 o 29.5
29.5 o 43.3
43.3 o 74.3
74.3 o 141.1
141.1 o 204.4
204.4 o 255.5
255.5 o 616.7
SRV RVC ($M)
0.4 o 6.0
6.0 o 16.6
16.6 o 28.4
28.4 o 48.3
48.3 o 63.7
63.7 o 81.6
81.6 o 103.3
103.3 o 118.7
118.7 o 140.2
140.2 o 552.1
No es: Figu e isualizes coun ies’ RVC expo s wi hin key RVC sec o s, based on EMERGING 2015-19 MRIO ables.
15
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 81
Figu e 18: Ups eamness Index o A ican Coun ies
0
1
2
3
GNQ
BWA
SSD
GAB
CHN
GIN
BFA
SWZ
ZMB
MRT
MLI
TCD
ERI
LBY
ZAF
GHA
AGO
NAM
ZWE
MOZ
LBR
DEU
SLE
BEN
COD
GNB
DZA
NGA
TZA
LSO
DJI
SEN
MDG
MAR
CMR
NER
USA
CIV
RWA
SYC
SOM
COG
CAF
TUN
GMB
TGO
MUS
CPV
UGA
BDI
MWI
ESH
ETH
COM
KEN
SDN
EGY
STP
Coun y
Ups eamness
No es: Figu e shows ups eamness index ollowing An `as e al. (2012), compu ed a he sec o le el and a e aged ac oss
sec o s using sec o al g oss expo s as weigh s. Based on EMERGING MRIO ables a e aged 2015-2019.
Figu e 19 p o ides an expo -weigh ed a e age (ac oss coun ies) o sec o al ups eamness,
including weigh ed 5 h and 95 h pe cen ile bounds. In all sec o s, a e age Wo ld ups eamness is
g ea e , indica ing ha A ican p oduc s a e pa o sho e GVCs. The PSM sec o is mos
ups eam, oge he wi h MPR, MIN, WAP, and PCM. A nea ly 3.5x he VA gene a ed in
downs eam p oduc ion s ages, A ican coun ies ha e signi ican oppo uni ies o inc ease local
alue addi ion by u he p ocessing p ecious s ones and o he mining p oduc s (e.g., pe oleum).
A he o he end o he spec um, FBE is he mos downs eam manu ac u ing sec o a an
ups eamness o a ound 1.8. Thus, o mo e local VA, A ican coun ies should p oduce and expo
mo e oods and be e ages and expand local PSM, MIN, and, o a lesse ex en , PCM p ocessing.
Figu e 19: Ups eamness Index by Sec o
0
1
2
3
4
PSM
MPR
MIN
WAP
PCM
EGW
ELM
TEQ
TEX
FIB
PTE
AFF
MAN
FBE
SMH
TRA
CON
PAO
Sec o
Ups eamness
Coun ies
A ica
Wo ld
No es: Figu e shows ups eamness index ollowing An `as e al. (2012), a e aged ac oss coun ies using sec o al g oss
expo s as weigh s, including weigh ed 5 h and 95 h pe cen iles. Based on EMERGING MRIO ables a e aged 2015-2019.
Has he e al eady been any p og ess in his di ec ion? Figu e 20 shows he di e ence be ween
he 2010 EMERGING es ima e and he 2015-19 median es ima e, compu ed a he coun y le el
and hen a e aged ac oss coun ies using 2015-19 median expo s as weigh s. I is ascina ing o see
om Figu e 20 ha all sec o s in A ica apa om PSM ha e mo ed downs eam on a e age. This
s ands in con as o a global end owa ds longe manu ac u ing GVCs. In he wo ld a e age,
all manu ac u ing sec o s apa om TEQ ha e mo ed ups eam, e lec ing his inc ease in he
16
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 82

leng h o chains. Fo A ica, he end hus sugges s g ea e local alue addi ion in many sec o s
bu may also imply a shi owa ds p ocessing ade in some sec o s.
Figu e 20: Ups eamness Index by Sec o : Di e ence
−1.0
−0.5
0.0
0.5
1.0
PSM
SMH
FBE
PAO
EGW
FIB
TRA
MAN
ELM
TEQ
TEX
PCM
AFF
MPR
CON
MIN
WAP
PTE
Sec o
Change in Ups eamness: 2015−19 − 2010
Coun ies
A ica
Wo ld
No es: Figu e shows an expo -weigh ed a e age o he coun y-le el di e ences be ween he 2015-2019 median
ups eamness and 2010 ups eamness, compu ed using EMERGING. Ba s gi e weigh ed 5 h and 95 h pe cen iles.
3 Conclusion
This sho pape examines A ica’s global and egional in eg a ion h ough ade, GVCs, and
RVCs, suppo ed by he EMERGING MRIO ables and he DOTS and BACI ade da abases. I
shows ha A ica’s sha e in global me chandise ade is a 5.5-6.5% and luc ua es wi h A ica’s
mac oeconomic pe o mance. Howe e , he sha e o he egion’s ade wi h i sel has inc eased
s eadily since 1980. Today, A ican inne -A ican ade is ∼5 imes smalle han A ica-ROW
ade, up om ∼20 imes smalle in 1980. Regional ade in ensi y di e s signi ican ly by coun y.
Pa icula ly coun ies in SADC a e hea ily engaged, wi h Sou h A ica alone accoun ing o 30%
o inne -A ican ade. T ade wi hin A ican RECs is 2x la ge han ade be ween RECs.
The la ges sec o s o inne -A ican ade a e mining (pe oleum), pe ochemicals (PCM),
p ecious s ones and me als (PSM), and p ocessed oods and be e ages (FBE). These sec o s also
d i e RVCs, wi h signi ican po en ial o deepe engagemen and mo e local alue addi ion. In
pa icula , coun ies wi h ad anced p ocessing acili ies could p ocess mo e PSM, mining, and
ag icul u al ou pu s om coun ies lacking hese acili ies. PCM RVCs could also become longe ,
wi h deepe p ocessing and mo e complex p oduc s p oduced ac oss bo de s. In FBE, an inc ease
in he sha e o ag icul u al ou pu being locally p ocessed and e o s o ma ke hese p oduc s
egionally could expand he size and each o RVCs, which a e e y localized a his poin .
All RECs could engage much mo e deeply in egional ading and alue addi ion. Es ablishing
compe i i e c oss-REC RVCs will likely equi e signi ican anspo in as uc u e in es men s.
Coun ies should hus ocus on exploi ing compa a i e ad an ages wi hin hei RECs. The sec o al
and g aphical analysis in his pape p o ides some sugges ions owa ds his end, such as enhanced
ag icul u al p ocessing in IGAD and he EAC (alongside PCM), mo e PSM p ocessing, elec ical
machine y and anspo equipmen in SADC and cen al A ica, deepe ex ile RVCs in wes e n
A ica (alongside mining, PCM, and PSM), longe PCM RVCs in he AMU and Egyp , e c.
A ica’s o e all posi ion in GVCs has shi ed sligh ly downs eam, coun e ing a global end
owa ds longe GVCs. In mos sec o s, his implies ha mo e local alue is added, a end ha
could be accele a ed by ha nessing p ocessing capabili ies in o he coun ies h ough RVCs.
17
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 83
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CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 84
Table 6: EMERGING Sec o s (2-Digi HS2002) Mapping o B oad Sec o s
HS02 EMERGING Sec o De ini ion BSC B oad Sec o De ini ion o Huo e al. (2022)
1 Li e Animals AFF Ag icul u e, Hun ing, Fo es y & Fishing
2 Mea and Edible Mea O al FBE Food P oduc ion, Be e ages & Tobacco
3 Fish, C us aceans, Molluscs, Aqua ic In e eb a es Ne AFF Ag icul u e, Hun ing, Fo es y & Fishing
4 Dai y P oduc s, Eggs, Honey, Edible Animal P oduc Ne FBE Food P oduc ion, Be e ages & Tobacco
5 P oduc s o Animal O igin, Nes AFF Ag icul u e, Hun ing, Fo es y & Fishing
6 Li e T ees, Plan s, Bulbs, Roo s, Cu Flowe s E c AFF Ag icul u e, Hun ing, Fo es y & Fishing
7 Edible Vege ables and Ce ain Roo s and Tube s AFF Ag icul u e, Hun ing, Fo es y & Fishing
8 Edible F ui , Nu s, Peel o Ci us F ui , Melons AFF Ag icul u e, Hun ing, Fo es y & Fishing
9 Co ee, Tea, Ma e and Spices FBE Food P oduc ion, Be e ages & Tobacco
10 Ce eals AFF Ag icul u e, Hun ing, Fo es y & Fishing
11 Milling P oduc s, Mal , S a ches, Inulin, Whea Glu e FBE Food P oduc ion, Be e ages & Tobacco
12 Oil Seed, Oleagic F ui s, G ain, Seed, F ui , E c, Ne AFF Ag icul u e, Hun ing, Fo es y & Fishing
13 Lac, Gums, Resins, Vege able Saps and Ex ac s Nes AFF Ag icul u e, Hun ing, Fo es y & Fishing
14 Vege able Plai ing Ma e ials, Vege able P oduc s Nes FBE Food P oduc ion, Be e ages & Tobacco
15 Animal, ege able Fa s and Oils, Clea age P oduc s, e FBE Food P oduc ion, Be e ages & Tobacco
16 Mea , Fish and Sea ood Food P epa a ions Nes FBE Food P oduc ion, Be e ages & Tobacco
17 Suga s and Suga Con ec ione y FBE Food P oduc ion, Be e ages & Tobacco
18 Cocoa and Cocoa P epa a ions FBE Food P oduc ion, Be e ages & Tobacco
19 Ce eal, Flou , S a ch, Milk P epa a ions and P oduc s FBE Food P oduc ion, Be e ages & Tobacco
20 Vege able, F ui , Nu , E c Food P epa a ions FBE Food P oduc ion, Be e ages & Tobacco
21 Miscellaneous Edible P epa a ions FBE Food P oduc ion, Be e ages & Tobacco
22 Be e ages, Spi i s and Vinega FBE Food P oduc ion, Be e ages & Tobacco
23 Residues, Was es o Food Indus y, Animal Fodde FBE Food P oduc ion, Be e ages & Tobacco
24 Tobacco and Manu ac u ed Tobacco Subs i u es FBE Food P oduc ion, Be e ages & Tobacco
25 Sal , Sulphu , Ea h, S one, Plas e , Lime and Cemen PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
26 O es, Slag and Ash PSM P ecious S ones & Base Me als Incl. Compounds
27 Mine al Fuels, Oils, Dis illa ion P oduc s, E c MIN Mining & Qua ying
28 Ino ganic Chemicals, P ecious Me al Compound, Iso ope PSM P ecious S ones & Base Me als Incl. Compounds
29 O ganic Chemicals PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
30 Pha maceu ical P oduc s PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
31 Fe ilize s PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
32 Tanning, Dyeing Ex ac s, Tannins, De i s,pigmen s e PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
33 Essen ial Oils, Pe umes, Cosme ics, Toile e ies PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
34 Soaps, Lub ican s, Waxes, Candles, Modelling Pas es PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
35 Albuminoids, Modi ied S a ches, Glues, Enzymes PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
36 Explosi es, Py o echnics, Ma ches, Py opho ics, E c PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
37 Pho og aphic o Cinema og aphic Goods PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
38 Miscellaneous Chemical P oduc s PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
39 Plas ics and A icles The eo PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
40 Rubbe and A icles The eo PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
41 Raw Hides and Skins (O he han Fu skins) and Lea he TEX Tex iles, Lea he & Wea ing Appa el
42 A icles o Lea he , Animal Gu , Ha ness, T a el Good TEX Tex iles, Lea he & Wea ing Appa el
43 Fu skins and A i icial Fu , Manu ac u es The eo TEX Tex iles, Lea he & Wea ing Appa el
44 Wood and A icles o Wood, Wood Cha coal WAP Wood, Pape & Publishing
45 Co k and A icles o Co k WAP Wood, Pape & Publishing
46 Manu ac u es o Plai ing Ma e ial, Baske wo k, E c. WAP Wood, Pape & Publishing
47 Pulp o Wood, Fib ous Cellulosic Ma e ial, Was e E c WAP Wood, Pape & Publishing
48 Pape & Pape boa d, A icles o Pulp, Pape and Boa d WAP Wood, Pape & Publishing
49 P in ed Books, Newspape s, Pic u es E c WAP Wood, Pape & Publishing
50 Silk TEX Tex iles, Lea he & Wea ing Appa el
51 Wool, Animal Hai , Ho sehai Ya n and Fab ic The eo TEX Tex iles, Lea he & Wea ing Appa el
52 Co on TEX Tex iles, Lea he & Wea ing Appa el
53 Vege able Tex ile Fib es Nes, Pape Ya n, Wo en Fab i TEX Tex iles, Lea he & Wea ing Appa el
54 Manmade Filamen s TEX Tex iles, Lea he & Wea ing Appa el
55 Manmade S aple Fib es TEX Tex iles, Lea he & Wea ing Appa el
56 Wadding, Fel , Nonwo ens, Ya ns, Twine, Co dage, E c TEX Tex iles, Lea he & Wea ing Appa el
57 Ca pe s and O he Tex ile Floo Co e ings TEX Tex iles, Lea he & Wea ing Appa el
58 Special Wo en o Tu ed Fab ic, Lace, Tapes y E c TEX Tex iles, Lea he & Wea ing Appa el
59 Imp egna ed, Coa ed o Lamina ed Tex ile Fab ic TEX Tex iles, Lea he & Wea ing Appa el
60 Kni ed o C oche ed Fab ic TEX Tex iles, Lea he & Wea ing Appa el
61 A icles o Appa el, Accesso ies, Kni o C oche TEX Tex iles, Lea he & Wea ing Appa el
62 A icles o Appa el, Accesso ies, no Kni o C oche TEX Tex iles, Lea he & Wea ing Appa el
63 O he Made Tex ile A icles, Se s, Wo n Clo hing E c TEX Tex iles, Lea he & Wea ing Appa el
64 Foo wea , Gai e s and he Like, Pa s The eo TEX Tex iles, Lea he & Wea ing Appa el
65 Headgea and Pa s The eo TEX Tex iles, Lea he & Wea ing Appa el
66 Umb ellas, Walking-S icks, Sea -S icks, Whips, E c TEX Tex iles, Lea he & Wea ing Appa el
67 Bi d Skin, Fea he s, A i icial Flowe s, Human Hai TEX Tex iles, Lea he & Wea ing Appa el
68 S one, Plas e , Cemen , Asbes os, Mica, E c A icles PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
69 Ce amic P oduc s Unda a PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
70 Glass and Glasswa e PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
71 Pea ls, P ecious S ones, Me als, Coins, E c PSM P ecious S ones & Base Me als Incl. Compounds
72 I on and S eel MPR Me al & Me al P oduc s
73 A icles o I on o S eel MPR Me al & Me al P oduc s
74 Coppe and A icles The eo PSM P ecious S ones & Base Me als Incl. Compounds
75 Nickel and A icles The eo PSM P ecious S ones & Base Me als Incl. Compounds
76 Aluminium and A icles The eo MPR Me al & Me al P oduc s
78 Lead and A icles The eo PSM P ecious S ones & Base Me als Incl. Compounds
79 Zinc and A icles The eo PSM P ecious S ones & Base Me als Incl. Compounds
80 Tin and A icles The eo MPR Me al & Me al P oduc s
81 O he Base Me als, Ce me s, A icles The eo PSM P ecious S ones & Base Me als Incl. Compounds
82 Tools, Implemen s, Cu le y, E c o Base Me al MPR Me al & Me al P oduc s
83 Miscellaneous A icles o Base Me al MPR Me al & Me al P oduc s
84 Nuclea Reac o s, Boile s, Machine y, E c ELM Elec ical & Machine y
85 Elec ical, Elec onic Equipmen ELM Elec ical & Machine y
86 Railway, T amway Locomo i es, Rolling S ock, Equipmen TEQ T anspo Equipmen
87 Vehicles O he han Railway, T amway TEQ T anspo Equipmen
88 Ai c a , Spacec a , and Pa s The eo TEQ T anspo Equipmen
89 Ships, Boa s and O he Floa ing S uc u es TEQ T anspo Equipmen
90 Op ical, Pho o, Technical, Medical, E c Appa a us ELM Elec ical & Machine y
91 Clocks and Wa ches and Pa s The eo ELM Elec ical & Machine y
92 Musical Ins umen s, Pa s and Accesso ies ELM Elec ical & Machine y
93 A ms and Ammuni ion, Pa s and Accesso ies The eo ELM Elec ical & Machine y
94 Fu ni u e, Ligh ing, Signs, P e ab ica ed Buildings MAN Manu ac u ing & Recycling
95 Toys, Games, Spo s Requisi es MAN Manu ac u ing & Recycling
96 Miscellaneous Manu ac u ed A icles MAN Manu ac u ing & Recycling
97 Wo ks o A , Collec o s Pieces and An iques MAN Manu ac u ing & Recycling
98 Commodi ies no Speci ied Acco ding o Kind MAN Manu ac u ing & Recycling
99 Elec ici y EGW Elec ici y, Gas & Wa e
100 Gas Manu ac u e, Dis ibu ion EGW Elec ici y, Gas & Wa e
101 Wa e Collec ion, Pu i ica ion, and Dis ibu ion EGW Elec ici y, Gas & Wa e
102 Coal MIN Mining & Qua ying
103 Oil MIN Mining & Qua ying
104 Gas MIN Mining & Qua ying
105 Pe oleum, Coal P oduc s PCM Pe oleum, Chemicals & Non-Me allic Mine al P oduc s
106 Manu ac u ing Se ices on Physical Inpu s Owned by O he s SMH Sale, Main enance & Repai o Vehicles; Fuel; T ade; Ho els & Res au an s
107 Main enance and Repai Se ices N.i.e. SMH Sale, Main enance & Repai o Vehicles; Fuel; T ade; Ho els & Res au an s
108 Sea T anspo TRA T anspo
109 Ai T anspo TRA T anspo
110 O he Modes o T anspo TRA T anspo
111 Pos al and Cou ie Se ices PTE Pos & Telecommunica ions
112 Goods (T a el) TRA T anspo
113 Local T anspo Se ices TRA T anspo
114 Accommoda ion Se ices SMH Sale, Main enance & Repai o Vehicles; Fuel; T ade; Ho els & Res au an s
115 Food-Se ing Se ices SMH Sale, Main enance & Repai o Vehicles; Fuel; T ade; Ho els & Res au an s
116 Cons uc ion CON Cons uc ion
117 Di ec Insu ance FIB Financial In e media ion & Business Ac i i y
118 Pension and S anda dized Gua an eed Se ices FIB Financial In e media ion & Business Ac i i y
119 Financial Se ices FIB Financial In e media ion & Business Ac i i y
120 Real Es a e FIB Financial In e media ion & Business Ac i i y
121 Cha ges o he Use o In ellec ual P ope y N.i.e. FIB Financial In e media ion & Business Ac i i y
122 Telecommunica ions Se ices PTE Pos & Telecommunica ions
123 Compu e Se ices PTE Pos & Telecommunica ions
124 In o ma ion Se ices PTE Pos & Telecommunica ions
125 Resea ch and De elopmen Se ices FIB Financial In e media ion & Business Ac i i y
126 P o essional and Managemen Consul ing Se ices FIB Financial In e media ion & Business Ac i i y
127 Enginee ing FIB Financial In e media ion & Business Ac i i y
128 Was e T ea men and De-Pollu ion Ag icul u al and Mining Se ices PAO Public Adminis a ion; Educa ion; Heal h; Rec ea ion; O he Se ices
129 Ope a ing Leasing Se ices FIB Financial In e media ion & Business Ac i i y
130 O he Business Se ices N.i.e. FIB Financial In e media ion & Business Ac i i y
131 Audio isual and Rela ed Se ices PAO Public Adminis a ion; Educa ion; Heal h; Rec ea ion; O he Se ices
132 Heal h Se ices PAO Public Adminis a ion; Educa ion; Heal h; Rec ea ion; O he Se ices
133 Educa ion Se ices PAO Public Adminis a ion; Educa ion; Heal h; Rec ea ion; O he Se ices
134 Rec ea ion & O he Se ices PAO Public Adminis a ion; Educa ion; Heal h; Rec ea ion; O he Se ices
135 Go e nmen Goods and Se ices N.i.e. PAO Public Adminis a ion; Educa ion; Heal h; Rec ea ion; O he Se ices
19
CHAPTER 3.1. AFRICA’S REGIONAL AND GLOBAL INTEGRATION Page 85
Pa e ns o Global and Regional In eg a ion
in he Eas A ican Communi y
Sebas ian K an z
*
July 20, 2024
Abs ac
Using de ailed global ade and no el Mul i-Region Inpu -Ou pu (MRIO) da a, his pape
examines he Eas A ican Communi y’s (EAC) global and egional in eg a ion h ough ade,
global, and egional alue chains (GVCs and RVCs). Wi h su gical a en ion o de ail, he
i s pa o he pape dissec s key pa e ns and ends o EAC membe s’ pa icipa ion in
global and egional ade and p oduc ion ne wo ks a he agg ega e, bila e al, sec o al, and
bila e al-sec o al le els. The second pa hen p o ides causal educed- o m e idence o he
economic bene i s o EAC in eg a ion h ough ade, GVCs, and RVCs a he sec o le el.
Findings imply ha he egion is mode a ely in eg a ed in o GVCs and RCVs bu shows no
o e all end owa ds g ea e in eg a ion. Regional in eg a ion is ad ancing in ag icul u e and
ood p ocessing, and Kenya is becoming a mo e dominan egional supplie o manu ac u es.
In eg a ion h ough ade and GVCs posi i ely a ec s economic de elopmen in he egion,
pa icula ly deepe o wa d GVC linkages in manu ac u ing. Deepening egional ade and
o wa d linkages yields addi ional economic bene i s is-a- is global linkages.
Keywo ds: GVCs, RVCs, EAC, ade, egional in eg a ion, economic de elopmen
JEL Classi ica ion: F14; F15; O11
1 In oduc ion
Global Value Chains (GVCs), e e ing o he in e na ionaliza ion o p oduc ion ne wo ks, ha e
become a cen al opic in ade and de elopmen policy. Wi h he en y in o o ce o he A ican
Con inen al F ee T ade A ea (A CFTA) in May 2019 and some p og ess owa ds i s ull enac men ,
he po en ial o a la ge common ma ke in A ica o inc eased GVC- ela ed ade, bo h wi hin
A ica and be ween A ica and he wo ld, is o g ea in e es o economic esea che s and policymake s.
To gauge he po en ial implica ions and dis ibu ional side-e ec s o A CFTA o ade and GVCs,
i is ins uc i e o s udy smalle e o s o egional in eg a ion and c ea ion o common ma ke s in
A ica, as has been he case in Eas A ica wi h he Eas A ican Communi y (EAC).
(Re-) ounded in 2000 by Uganda, Kenya, and Tanzania as a body o acili a e egional coope a ion,
he EAC quickly became a ehicle o economic in eg a ion. A cus oms union became ope a ional
in Janua y 2005, wi h Kenya, he egion’s la ges expo e , con inuing o pay du ies on some goods
en e ing o he coun ies on a declining scale un il 2010 (EAC Cus oms Union P o ocol, A icle 11
and Aloo (2017)). Rwanda and Bu undi acceded in 2007, joining he cus oms union in 2009. The
cus oms union expanded o a common ma ke o goods, labo , and capi al e ec i e in 2010. In
2013, he P o ocol o he Es ablishmen o he EAC Mone a y Union was signed, aiming o a
mone a y union wi hin 10 yea s, subjec o mac o- iscal con e gence c i e ia. In 2016, he newly
ounded Republic o Sou h Sudan joined he EAC, and he Democ a ic Republic o Congo joined
in July 2022. Thus, he EAC, pa icula ly he yea s ollowing he cus oms union in 2005 and he
common ma ke in 2010, p o ides a small case s udy in ligh o A CFTA’s b oade aims.
The e is, by now, ex ensi e academic and policy li e a u e on he s a e, de e minan s, and
consequences o in eg a ion in o GVCs, including o coun ies a di e en income le els. As one
o he i s , Kumm i z & Quas (2016) examine pa e ns o GVC in eg a ion in low- and middle-
income coun ies (LMICs) using he OECD TiVA da abase. They ind ha LMICs ha e become
an in eg al pa o GVCs and a e d i ing hei expansion, wi h a ising sha e in bo h he o eign
*
Kiel Ins i u e o he Wo ld Economy
Add ess: Haus Wel -Club, Dues e nb ooke Weg 148, D-24105 Kiel
E-mail: sebas ian.k an[email p o ec ed]
1
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 86
p oduce. All coun ies ha e some s akes in FBE, wi h Uganda supplying he mos , ollowed by
Kenya. In manu ac u ing, Kenya has a dis inc lead, ollowed by Tanzania and Uganda. Wi h
ROW, all EAC coun ies a e la ge ag icul u al expo e s and impo e s o manu ac u ed p oduc s.
Kenya is he la ges EAC supplie o bo h ag icul u e and p ocessed oods o ROW, whe eas i
only plays a mino supplie ole in he EAC. Tanzania supplies la ge amoun s o gold, and Congo
la ge amoun s o mine als o ROW, which a e subsumed unde MPR and PCM in Table 2, making
Kenya also he la ges EAC expo e o manu ac u es. The da a hus expound di e ences in he
na u e o ade bo h wi hin he EAC and wi h ROW. Sha ed capaci ies exis o FBE, which has
also been he ocus o policymake s and egional s udies. Fo example Daly e al. (2017) show ha
Uganda expo s dia y and maize p oduce o Kenya o p ocessing, bu has also ecei ed FDI and
begun o upg ade i s own ood p ocessing sec o . The Ugandan Minis y o Finance and Planning
(MoFPED,2021), IGC Uganda (Fowle & Rauschendo e ,2019) and IFPRI (Van Campenhou e
al.,2020) ha e iden i ied ag o-indus ializa ion as an impo an pilla o g ow h o he coun y.
Figu e 5shows co esponding a ios o EAC5-ROW o inne -EAC5 ade, indica ing ha
egional ade in ag icul u e and, o a lesse ex en , FBE, assumes inc easing sha es o o e all
EAC ade in hese sec o s. Acco ding o BACI, in 2020, he inne -EAC5 ade in ag icul u al
p oduc s was 10 imes smalle han EAC5-ROW ade, down om almos 40 imes smalle in 2000.
Simila ly, FBE inne -EAC5 ade was 11 imes smalle in 2020, compa ed o 16 imes smalle in
2000. In con as , he a io in manu ac u ing shows an oscilla ing inc ease om 15 in 2000 o 20
in 2020. These de elopmen s a e also e lec ed in he MRIO da abases.
Figu e 5: G oss ROW-EAC5 T ade o Inne EAC5 T ade Ra io by B oad Sec o
Ag icul u e & Li es ock
Foods & Be e ages
Manu ac u ed Goods
2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
0
2
4
6
8
10
12
14
16
0
5
10
15
20
25
30
35
40
45
EAC5−ROW/Inne −EAC5 T ade, 5 Yea MA
Da abase: BACI EORA EMERGING
No es: Figu e shows he a io o EAC5 ⇔ROW o inne -EAC5 ade (expo s + impo s), smoo hed using a
backwa d-looking 5-yea MA. The EAC5 includes Tanzania, Kenya, Uganda, Rwanda and Bu undi. The b oad sec o s
shown a e AFF (le ), FBE (middle), and TEX-MAN ( igh ) in Table 2.
Figu e 6again p o ides a de ailed b eakdown by expo s/impo s and indi idual membe s.
In ag icul u e, expo and impo sha es bo h inc eased: in 2015-20, he EAC5 expo ed 12.6%
o ag icul u al expo s o i sel , up om 4.6% in 1995-2000, and impo ed 19.3%, up om 9.3%
in 1995-2000. The FBE expo sha es also ose om 7.8% o 13.7%, whe eas he impo sha e
emained cons an a ound 20%. In manu ac u ing, he opposi e is he case, wi h he EAC5 expo s
sha e declining om 32% o 18.6% and he impo sha e emaining oughly cons an a a ound 7%.
A he coun y le el, Uganda signi ican ly inc eased i s EAC5 sha e as an expo e and impo e
o bo h ag icul u al p oduce and FBE. This de elopmen is mi o ed, o a lesse ex en , by Kenya,
which addi ionally main ains a e y high EAC5 sha e in manu ac u ed expo s o a ound 40%,
down om nea ly 50% in 2000. This s ands in s a k con as o a e y small EAC5 impo sha e o
less han 1%. Tanzania inc eased i s expo sha e o he EAC5 in all 3 b oad sec o s while u he
dec easing i s al eady low impo sha es in oods and manu ac u es o a ound 5%. Rwanda and
Bu undi ha e high expo and impo EAC5 sha es in all sec o s apa om manu ac u ing expo s.
Rwanda s ongly dec eased i s EAC5 ag icul u e and oods expo sha es since 2007, app oaching
he le els o Kenya in 2020, whe eas Bu undi s ongly inc eased i s ag icul u al expo sha e om
almos 0% in 2005 o 60% in 2020, while dec easing i s impo sha e om 70% o 30%.
8
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 93

Figu e 6: EAC5 Sha e in Membe s G oss T ade by Sec o using BACI Da a
Ag icul u e & Li es ock
Foods & Be e ages
Manu ac u ed Goods
Expo s
Impo s
2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020
0%
10%
20%
30%
40%
50%
60%
0%
10%
20%
30%
40%
50%
60%
70%
EAC5 T ade Sha e, 5 Yea MA
Coun y: UGA TZA KEN RWA BDI EAC5
No es: Figu e shows he EAC5 sha e in membe s’ o al expo s and impo s, smoo hed using a backwa d-looking 5-yea
MA. The b oad sec o s shown a e AFF (le ), FBE (middle), and TEX-MAN ( igh ) in Table 2.
Conside ing hei di e en le els o de elopmen , his sugges s ha coun ies i s become
egional ag icul u al expo e s and la e supplie s o manu ac u ed goods. Howe e , i seems
like hese manu ac u es do no ca e e y well o o he membe s’ demands, as e idenced by he
declining EAC5 sha es in bo h expo s and impo s, and hus ail o become a d i e o egional
in eg a ion. The hegemonic posi ion o Kenya as a supplie o manu ac u es may also c owd ou
o he coun ies’ a emp s o inc ease hei egional supply. Thus, g oss ade da a sugges s ha
EAC egional in eg a ion h ough ade is asymme ic, has p og essed mainly ia ag icul u e and
FBE, and is s onge in expo s. Pa icula ly, he la ge economies o Tanzania and Kenya impo
much mo e om ROW. Among he EAC5, Tanzania is o e all leas in eg a ed in o egional ading.
3.2 In e media e Flows
An ad an age o MRIO da abases is ha hey eco d g oss ade in bo h in e media es and inal
goods. Due o i s g ea e accu acy, I only examine such lows using he EM da abase, a e aged
ac oss 2015-2019 o smoo h empo al a ia ion. Figu e 7p o ides an agg ega e in e media e lows
able. The columns indica e in e media e inpu s equi ed by each coun y o egion om each ow
coun y o egion. Con e sely, he ows indica e in e media e quan i ies supplied.
Among he EAC coun ies, he able shows a signi ican supplie ole o Kenya, supplying
102.72 = 524 million USD o Uganda, 102.22 = 168 million USD o Tanzania and 101.96 = 91
million USD o Rwanda. Uganda/Tanzania also supplies 258/228 million o Kenya and 90/70
million o Rwanda. Tanzania supplies 56 million o Uganda, Rwanda 54 million o Kenya,
and all o he inne -EAC in e media e ade is below 35 million.5EM es ima es in e media e
ade wi h ROW o be 27.3 imes g ea e han inne -EAC ade, composed o in e media e
inpu s om ROW summing o 15.6 imes EAC in e media es ade and EAC inpu s o ROW
summing o 11.8 imes EAC in e media es ade. The la ges supplie o in e media es is China,
supplying 831m o Uganda, 1937m o Tanzania, and 2704m o Kenya, ollowed by Sou h Asia
supplying 601/1066/1562, espec i ely, and he EU supplying 620/844/1462. Compa ed wi h
hese, he es o SSA is ela i ely insigni ican a 184/325/514. In e ms o demand o EAC
in e media es, he EU is he la ges impo e , impo ing 552/828/1402, ollowed by he Middle
5Exemp ing Sou h Sudan, subsumed in SSA because o da a quali y conce ns, which ecei es 385 million in
in e media es om Uganda and 223 million om Kenya.
9
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 94
Eas and No h A ica (769/352/503), Sou h Asia (101/683/618), he es o SSA (105/716/462)
and China (148/631/340). China, no ably, supplies 4.9 imes mo e in e media es han i demands
om hese h ee economies. The supply and demand o in e media es wi h he EU, NAC, and
SSA a e qui e balanced. O e all, Uganda, Tanzania, and Kenya combined demand 1.7 imes mo e
inpu s om ROW han hey supply. I should be no ed ha Congo, while no eally in eg a ed wi h
o he EAC membe s in e ms o in e media es, has la ge and su p isingly balanced in e media e
lows wi h ROW, demanding/supplying 2598/2636 wi h he EU and 1199/1375 wi h China.
Figu e 7: Agg ega ed EMERGING MRIO Table: 2015-2019 A e age
Log10 Millions o Cu en USD a Basic P ices
4.15
1.75
2.72
1.31
0.87
0.40
2.27
2.54
2.79
2.07
2.15
2.78
2.02
2.92
2.38
1.57
1.09
1.49
4.49
2.22
0.96
0.02
0.61
2.51
2.88
2.93
2.44
2.48
3.03
2.54
3.29
2.42
1.67
1.61
2.41
2.36
4.68
1.73
0.73
0.80
2.71
3.08
3.16
2.52
2.68
3.19
2.64
3.43
2.69
2.05
1.74
1.96
1.85
1.96
3.42
0.30
−0.35
1.62
1.84
2.20
1.77
1.77
1.83
1.59
1.76
1.35
1.07
0.63
1.38
1.41
1.52
0.80
3.18
−0.69
1.13
1.66
1.87
1.01
1.18
1.51
0.96
1.66
0.94
0.40
−0.12
0.50
0.88
0.80
−0.20
−0.97
4.42
2.07
2.46
3.41
2.66
2.96
2.31
2.75
3.08
2.74
2.59
2.10
2.02
2.85
2.66
1.24
1.00
2.09
5.99
4.12
4.76
3.90
4.22
4.10
4.03
4.62
3.83
3.70
3.29
2.89
2.55
2.70
2.15
1.61
2.50
4.07
6.22
5.20
4.76
4.73
4.59
4.39
4.89
4.49
4.22
3.57
2.74
2.92
3.15
2.20
1.43
3.42
4.70
5.08
7.23
5.63
5.59
4.88
5.09
5.47
5.04
4.95
4.24
1.92
2.66
2.54
2.08
0.96
2.72
4.14
4.48
5.68
6.64
4.80
4.21
4.43
4.96
4.49
4.37
3.46
2.14
2.30
2.69
1.85
0.59
3.06
4.19
4.73
5.63
4.83
7.18
4.79
4.98
5.43
5.19
5.44
4.15
2.00
2.83
2.79
1.86
1.18
2.34
4.11
4.71
4.67
4.38
4.50
6.41
4.62
4.88
4.29
4.15
3.72
2.09
2.55
2.19
1.96
0.95
2.80
4.02
4.73
5.12
4.51
4.98
4.55
6.39
5.42
5.22
4.31
4.39
2.17
2.80
2.53
2.10
0.76
3.14
4.59
5.02
5.49
5.08
5.33
4.56
5.44
7.37
5.58
5.05
5.00
1.72
2.45
2.27
1.33
−0.08
2.80
4.02
5.01
5.15
4.62
5.19
4.18
5.16
5.36
6.77
4.54
4.64
1.13
1.42
1.70
0.83
−0.28
2.69
3.57
3.93
5.08
4.23
5.52
4.08
4.36
5.04
4.63
6.62
3.45
1.04
1.57
1.82
1.05
−0.22
1.96
3.19
3.74
4.52
3.56
4.36
3.70
4.53
4.52
4.26
3.46
6.15
UGA
TZA
KEN
RWA
BDI
COD
SSA
MEA
EUU
ECA
NAC
SAS
ASE
CHN
ROA
LAC
OCE
UGA
TZA
KEN
RWA
BDI
COD
SSA
MEA
EUU
ECA
NAC
SAS
ASE
CHN
ROA
LAC
OCE
0
1
2
3
4
5
6
7
No es: Figu e shows g oss in e media e inpu lows in log10 USD millions. Rows indica e he sou ces, and columns he
des ina ions o in e media es. The diagonal sums he domes ic IO/ egional ICIO able.
Despi e i s high use o o eign inpu s, domes ic in e media e inpu s co esponding o he
diagonal en ies a e, on a e age, 4.1 imes g ea e han o eign inpu s in EAC coun ies and 6.3
imes g ea e han EAC inpu s o o he coun ies. Fo he egion as a whole, hese igu es a e 4.6
and 6.13, espec i ely. This is low compa ed o o he majo egions, which p oduce and ade a
lo mo e wi hin hemsel es. Fo example, in he EU and No h Ame ica, own inpu s a e a ound
10 imes g ea e han o eign inpu s. Fo China, i is 14 imes.
To p o ide some sec o -le el de ail, Appendix Table B1 eco ds he 50 la ges sec o -le el
in e media e lows (excl. Congo). Bo h wi h ROW and inside he EAC, he la ges in e media e
lows a e in manu ac u ing and, in pa icula , in pe ochemicals (PCM), FBE, and, o a lesse
ex en , ex iles (TEX). Kenya is a signi ican EAC supplie o manu ac u ing inpu s, pa icula ly
o PCM, FBE, and me al p oduc (MPR) indus ies. Kenya also supplies la ge anspo (TRA)
(including a el and ou ism) in e media es o EU TRA se ices and ag icul u al inpu s o EU
FBE indus ies. I also supplies la ge inpu s o FBE indus ies in Sou h Asia. These lows a e, on
a e age, 3-4 imes la ge han i s egional in e media e supplies. Uganda supplies PCM o ROW,
and FBE and ag icul u e o Kenyan FBE and TRA indus ies.
3.3 Agg ega e S uc u e o P oduc ion and T ade
Figu e 8compac ly summa izes he s uc u e o p oduc ion and ade in he EAC. VA is a ound
60% o ou pu in all EAC membe s, apa om Rwanda, whe e i is 73%. The o he componen s
10
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 95
o ou pu a e domes ic and impo ed in e media es, o which, as he second plo shows, be ween
17 and 24% a e impo ed by di e en EAC membe s. G oss ou pu is hen ei he consumed o
expo ed o ei he in e media e o inal use. The RHS o Figu e 8shows ha be ween 5 and 16%
o g oss ou pu is expo ed by EAC membe s.
Figu e 8: G oss Decomposi ion o EAC P oduc ion and T ade
Value Added
Pe cen o Inpu s Impo ed
Pe cen o Ou pu Expo ed
UGA TZA KEN RWA BDI COD UGA TZA KEN RWA BDI COD UGA TZA KEN RWA BDI COD
0%
5%
10%
15%
0%
5%
10%
15%
20%
25%
0%
20%
40%
60%
EAC Membe
Pe cen
Expo s
Impo s
UGA TZA KEN RWA BDI COD UGA TZA KEN RWA BDI COD
0%
10%
20%
30%
0%
5%
10%
15%
EAC Membe
EAC6 Sha e
Flow
In e media e
Final
No es: Based on EMERGING and compu ed using an 2015-2019 a e age MRIO able.
The bo om panel o Figu e 8decomposes expo s and impo s by ype o low. I shows
ha Uganda, Rwanda, and Bu undi ha e signi ican expo and impo sha es wi h he EAC o
bo h in e media e and inal p oduc s. Kenya and Tanzania, on he o he hand, expo signi ican
amoun s o he EAC bu only impo small sha es. Wi h he excep ion o Tanzanian and Ugandan
expo s, inne -EAC ade in in e media es is sligh ly la ge han ade in inal goods.
4 Value Chains
While g oss in e media e lows p o ide use ul in o ma ion abou di ec p oduc i e ela ionships,
hey do no e eal how much o he alue was added in he supplying coun y-indus y and
p e ious p oduc ion s ages pe o med by o he coun y-indus ies. The Leon ie decomposi ion
sol es his p oblem by ealloca ing he alue o in e media e inpu s o he o iginal p oduce s
(Quas & Kumm i z,2015). To guide he u he discussion o VA ade lows, I begin wi h some
o mal de i a ions and in oduce a consis en no a ion used h oughou his pape .
Le Abe a no malized ICIO able whe e each elemen aoi,uj gi es he uni s o o igin coun y
oand sec o i’s ( ow) ou pu equi ed o he p oduc ion o one uni o using coun y uand sec o
j’s (column) ou pu , x he ec o o ou pu s o each coun y-sec o , and da ec o o inal demand
(FD) such ha he ollowing p oduc i e ela ionship holds
x=Ax +d.(1)
Leon ie (1936)’s insigh was ha one could sol e his equa ion o x o ge he amoun o ou pu
11
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 96
each coun y-sec o should p oduce gi en a ce ain amoun o FD
x= (I−A)−1d=Bd,(2)
whe e he Leon ie In e se in deno ed B= (I−A)−1. This ma ix is also o en called he
o al equi emen ma ix since i gi es he o al p oduc i e inpu equi emen om each sec o o
p oduce one uni o inal ou pu 6. The di ec VA sha e o each coun y-sec o is gi en by
=1−A′1,(3)
whe e 1= (1,1,1, ..., 1)′is a column- ec o o 1’s. Le Vbe he ma ix wi h along he diagonal
and 0’s in he o -diagonal elemen s. Mul iplying Eq. 2wi h V hen gi es VA in each coun y-sec o
Vx =V(I−A)−1d=VBd.(4)
The e m VB =V(I−A)−1is known as he ma ix o VA mul iplie s o VA sha es, which can
be used o ob ain he amoun o VA gene a ed in each sec o (Vx) when p oducing o sa is y FD
(d). Mo e speci ically, he ma ix VB con ains he amoun o VA by each coun y-sec o ( ow) o
he p oduc ion o one uni o each coun y-sec o ’s (column’s) ou pu .
4.1 Backwa d GVC Pa icipa ion
The FVA sha e in domes ic p oduc ion and expo s, e med ’Ve ical Specializa ion’ (VS) by
Hummels e al. (2001), is he mos widely used measu e o backwa d GVC in eg a ion. Conside
VB wi h elemen s boi,uj, hen VS o a pa icula coun y-sec o may be exp essed as
VSuj =X
oi, o=u
boi,uj ∀uj. (5)
Figu e 9shows a ime se ies o VS acco ding o di e en da a sou ces. The calcula ed VS measu e
using EORA21 is iden ical o he WDR one. The ex ension o EORA h ough 2021, as men ioned,
in oduces a la ge s uc u al b eak in 2016, which, in some cases such as Tanzania whe e VS d ops
o ze o o Bu undi whe e VS ises o abo e 50% ( unca ed in Figu e 9) is highly un ealis ic.
EM is he mo e eliable da abase o hese coun ies and indica es ha o all EAC membe s,
be ween 8% and 30% o p oduc ion/expo s is o eign con en . Fu he mo e, EM sugges s ha he
smalle economies Bu undi, Rwanda, and Uganda ha e inc eased hei VS, especially in 2015-2019,
whe eas Kenya and Congo ha e seen a decline in VS. In Tanzania, VS appea s s agnan a ∼16%.
Figu e 9: EAC Backwa d GVC Pa icipa ion
RWA
BDI
COD
UGA
TZA
KEN
2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020
10%
20%
30%
10%
20%
30%
Backwa d GVC Pa icipa ion (VS)
Sou ce: EMERGING EORA WDR_EORA
No es: Hummels e al. (2001)’s index o Ve ical Specializa ion (VS) is he FVA sha e in g oss ou pu and expo s.
6Speci ically each elemen in boi,uj in Bgi es he ou pu equi ed om coun y-sec o oi o he p oduc ion o
one uni o he inal good in uj. Thus, he i s column o Bgi es all he p oduc i e inpu equi ed om all sec o s
o he p oduc ion o one uni o he inal good in sec o 1, and he i s ow o Bgi es all he inpu equi ed om
sec o 1 o p oduce one uni o he inal good in each sec o .
12
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 97
Apa om i s o e all size, he composi ion o VS is o in e es . Figu e 10 shows a b eakdown
o VS by sou ce coun y/ egion, a e aged, o EORA be ween 2010 and 2015 and o EM be ween
2015 and 2019. Cong uen o he EAC impo sha e shown in he bo om igh panel o Figu e
8, only Rwanda, Bu undi, and Uganda sou ce a signi ican ac ion o o eign inpu s om EAC
pa ne s. Acco ding o EM, Kenya supplies 11.5% o he o eign con en in Ugandan expo s, 9.8%
in Rwanda, and 8% in Bu undi. Uganda also supplies 9.3% o he o eign con en in Rwandan
expo s and 5.5% in Bu undi. In absolu e alues, Uganda supplies sligh ly mo e o Kenyan expo
p oduc ion (a ound 23 million USD acco ding o EM, s. 20.7 million o Rwanda). This is dwa ed
by he 115 million ha Kenya adds o Ugandan expo s.
In o al, he EU and China ha e he g ea es sha es in EAC VS. The EU supplies 33% o
he o eign con en o Congolese expo s, 23% in Bu undi, 21% in Rwanda, 17%, 16%, 15% in
Uganda, Kenya, and Tanzania, espec i ely. China supplies 27% o he o eign con en o Kenyan
and Tanzanian expo s (app ox. 300 million USD in bo h cases), 21% in Uganda, and 16% in
Congo. Thus, o e all, EAC expo s ha e modes amoun s o o eign con en , and mos o his VS,
pa icula ly o majo expo e s Congo, Kenya, and Tanzania, o igina es in he EU o China.
Figu e 10: EAC Backwa d GVC Pa icipa ion: Sou ces o Fo eign Con en
A e age EMERGING 2015-2019 Fo eign Con en Sha e in Pa en heses
UGA (14.9%)
TZA (16%)
KEN (9.6%)
RWA (15.5%)
BDI (19.9%)
COD (25.1%)
EORA EMERGING EORA EMERGING EORA EMERGING EORA EMERGING EORA EMERGING EORA EMERGING
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Da abase
Sha e o Fo eign Expo s Con en
Sou ce
UGA
TZA
KEN
RWA
BDI
COD
SSA
MEA
EUU
ECA
NAC
SAS
ASE
CHN
ROA
LAC
OCE
No es: Figu e shows a b eakdown o VS by sou ce coun y acco ding o EORA (2010-2015) and EM (2015-2019) a e ages.
Sec o s exhibi g ea he e ogenei y, bo h in e ms o o e all o eign con en and i s composi ion.
Table 3shows o e all VS con en sha es acco ding o EM. In gene al, manu ac u ing sec o s ha e
highe o eign con en , a pa e n emphasized in he WDR, which also no es ha a hand ul o
sec o s, including elec ical machine y (ELM) and anspo equipmen (TEQ), ha e d i en GVC
expansion since 1995. In he a e age EAC coun y, hese manu ac u ing sec o s ha e mo e han
20% o eign con en , bu he e is ma ked he e ogenei y ac oss coun ies. No ably, in Rwanda,
manu ac u ing sec o s ha e less han 15% o eign con en . The highes o eign con en sec o s
by coun y a e ELM in Tanzania (42%), wood and pape (WAP) in Kenya (40%), mining (MIN)
and ex iles (TEX) in Uganda (29%), sales and epai s (SMH) in Rwanda (25%), pe ochemicals
(PCM) in Bu undi (47%) and TEQ in Congo (36%). Since Bu undi and Congo ha e no IO able,
hese igu es need o be aken wi h cau ion. The inal columns o Table 3gi e FVA in o e all
sec o al expo s by EAC membe s, including alue addi ion by o he membe s, wi h and wi hou
Congo. These esemble a classical VS dis ibu ion cen e ing a ound ELM and TEQ a ∼35%.
Figu e 11 b eaks down he o igin o o al EAC5 VS and hus p o ides a sec o -le el pe spec i e
o EAC egional in eg a ion. The sec o s wi h he highes EAC5 sha e a e SMH a 14.5% and
FBE a 14%. O he sec o s wi h sizeable egional sha es a e PCM a 8.6%, TEX a 7.4%, AFF a
7.2%, elec ici y (EGW) a 6.3% and TEQ a 6.2%. This quan i a i ely highligh s he po en ial o
he FBE sec o o egional in eg a ion bu also indica es a ailu e o egional in eg a ion in many
co e manu ac u ing sec o s. Fo example, ELM, which has a VS o a ound 35% acco ding o Table
3, only has a 2.8% egional sha e. Mul iplying hese pe cen ages yields ha only 1% o he g oss
expo s (and ou pu ) in EAC ELM is egional FVA, compa ed o 1.5% o FBE. 7Figu e 11 hus
indica es g ea po en ial and challenges in de eloping egional manu ac u ing alue chains.
7Due o he lowe FVA sha e o 11% in he FBE sec o .
13
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 98

Table 3: EAC Backwa d GVC Pa icipa ion: Sec o al He e ogenei y
A e age EMERGING 2015-2019 Fo eign Con en Sha es (%)
sec o UGA TZA KEN RWA BDI COD Mean Median EAC6 EAC5
AFF 5.9 4.1 3.7 5.8 15.2 3.3 6.3 5.0 4.2 4.4
MIN 29.2 5.5 0.0 2.0 17.0 4.7 9.7 5.1 4.6 6.8
FBE 22.7 7.6 3.1 20.6 19.3 13.6 14.5 16.4 11.1 10.7
TEX 29.6 17.1 25.2 8.3 8.5 28.6 19.6 21.2 26.1 24.1
WAP 13.7 22.7 39.5 1.4 5.9 20.1 17.2 16.9 24.8 29.2
PCM 23.9 19.8 19.5 10.2 47.1 20.6 23.5 20.2 20.0 19.7
MPR 27.0 26.9 16.2 10.2 39.7 25.0 24.2 26.0 24.3 23.6
ELM 18.7 41.9 30.7 5.2 27.4 34.9 26.5 29.1 34.9 35.2
TEQ 22.7 17.1 19.7 0.0 32.8 36.4 21.4 21.2 34.6 23.9
MAN 23.3 21.8 27.3 0.8 0.4 24.5 16.3 22.6 25.3 25.6
EGW 28.2 3.1 26.2 0.0 2.1 11.9 3.1 16.9 27.1
CON 15.6 13.1 16.2 7.4 5.3 30.6 14.7 14.3 12.9 12.9
SMH 5.6 11.7 9.4 25.2 14.1 15.3 13.5 12.9 10.9 10.9
TRA 7.6 18.5 6.6 4.7 0.1 4.3 7.0 5.7 11.0 11.0
PTE 11.0 21.7 5.4 0.0 0.0 2.5 6.8 4.0 11.9 12.0
FIB 0.4 8.2 0.3 1.9 0.0 4.5 2.5 1.1 1.1 0.9
PAO 5.0 2.2 8.0 0.0 0.0 4.5 3.3 3.4 6.8 7.0
No es: Table epo s o al o eign con en sha es (VS) acco ding o he EM 2015-2019 a e age in
pe cen age e ms. These sha es a e epo ed o each EAC6 coun y and o he EAC6 and EAC5
as a whole, which also coun s VA by membe s among each o he as FVA, i.e., hese a e expo -weigh ed
a e ages o indi idual membe s VS. The ’Mean’ and ’Median’ gi e unweigh ed EAC6 a e ages.
Figu e 11: EAC5 Backwa d GVC Pa icipa ion: Sou ces o VS by Sec o
Based on A e age EMERGING 2015-2019 EAC Expo s (Excl. Congo)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
AFF MIN FBE TEX WAP PCM MPR ELM TEQ MAN EGW CON SMH TRA PTE FIB PAO
Sec o
Sha e o Fo eign Expo s Con en
Sou ce
UGA
TZA
KEN
RWA
BDI
COD
SSA
MEA
EUU
ECA
NAC
SAS
ASE
CHN
ROA
LAC
OCE
No es: Figu e shows a sec o -le el b eakdown o o al EAC5 VS by sou ce coun y, acco ding o EM (2015-2019) a e ages.
4.2 Fo wa d GVC Pa icipa ion
Apa om VS, which measu es backwa d GVC in eg a ion, Hummels e al. (2001), and mo e
o mally Daudin e al. (2011), in oduced he sha e o domes ic expo s ha en e o eign coun ies’
expo s, e med VS1, as a measu e o o wa d GVC In eg a ion. I is de ined as8
VS1oi =1
Eoi X
uj,u=o
beoi,uj ∀oi, (6)
whe e Eoi a e he g oss expo s o coun y-sec o oi used o no malize he sum along he ows o
VBE (excluding domes ic sec o s, Eis a diagonal g oss expo s ma ix) which cap u e he use o
VA om a domes ic sec o oi in he expo s o all o eign sec o s uj.Bo in & Mancini (2019) show
8Fo comple eness I no e ha VS can be de ined in an analogous way as VSuj =1
Euj Poi,o=u beoi,uj ∀uj,
howe e , since Poi boi,uj = 1 ∀uj, he expo s cancel ou and he equa ion educes o Eq. 5.
14
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 99
ha his measu e is biased because i con ains double-coun ed componen s. They p opose (DVA -
DAVAX)/E, which is he a io o DVA (excl. double-coun ed i ems) minus di ec ly abso bed DVA
in expo s (DAVAX) o g oss expo s as a e ined measu e o o wa d GVC pa icipa ion.
Accu a e compu a ion o o wa d GVC pa icipa ion equi es a ull coun y-le el ICIO da abase.
Due o compu a ional cons ain s, I educe he numbe o sec o s o 5: AFF, FIB, MIN, MAN
(combining 7 manu ac u ing sec o s), and SRV (all o he sec o s) while p ese ing he ull numbe
o coun ies and e i o ies (187 o EORA and 245 o EM). Figu e 12 shows he co ec ed
measu e o o wa d GVC pa icipa ion ollowing Bo in & Mancini (2019). E iden ly, a educ ion
o he sec o al dimension a enua es agg ega e VS1 indica o s a bi , bu he ends a e b oadly
p ese ed. All indica o s show ha commodi y expo e s such as Congo and Bu undi ha e g ea e
o wa d GVC in eg a ion. EM measu es sugges ha VS1 has inc eased sligh ly in Congo and
dec eased sligh ly in Kenya, Rwanda, and Tanzania since 2010, sugges ing a sligh shi away om
commodi ies in he la e h ee economies.
Figu e 12: EAC Fo wa d GVC Pa icipa ion
RWA
BDI
COD
UGA
TZA
KEN
2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020
10%
20%
30%
40%
50%
10%
20%
30%
40%
50%
Fo wa d GVC Pa icipa ion (VS1)
Sou ce: EMERGING EORA WDR_EORA
No es: Bo in & Mancini (2019)’s index o o wa d GVC in eg a ion (VS1) is he (non-double coun ed) DVA in expo s
ha is no di ec ly abso bed by he di ec impo e , di ided by g oss expo s: (DVA - DAVAX)/E.
Since e en wi h 5-sec o ICIO ables, bila e al GVC indica o s using Belo i e al. (2020)’s ICIO
STATA package a e ex emely ime-consuming, I compu e he simple VS1 measu e ollowing Eq. 6
(also called expo s o e-expo s (E2R) by Baldwin & Lopez-Gonzalez (2015)) o examine bila e al
ela ionships. Figu e 13 o e s a b eakdown o VS1 by GVC pa ne . The heade s indica e ha
E2R (Eq. 6) is indeed upwa d biased is-a- is he co ec ed measu e o Bo in & Mancini (2019)
(BM), bu his does no necessi a e bias in he GVC pa ne sha es. Acco ding o EM, 4.4% o
Kenya’s VS1 was e-expo ed by Uganda, and 3.2% o Ugandan VS1 is e-expo ed by Kenya.
O he EAC coun ies also e-expo a small sha e o hei VS1 h ough Kenya: Bu undi (1.25%),
Rwanda (1.5%), and Tanzania (1.5%). Bu undi and Rwanda expo 2.4% and 0.8% o hei VS1
h ough Uganda, espec i ely. Fo wa d GVC linkages in he EAC a e almos an o de o magni ude
smalle han backwa d linkages. The majo GVC pa ne o EAC coun ies is he EU, accoun ing
o 43% o Kenyan and Congolese VS1 and close o 30% o VS1 in he o he EAC membe s. The
ea ly li e a u e (e.g., Fos e -McG ego e al. (2015), Kumm i z (2016)) associa es inc eased VS1
wi h p oduc i e upg ading, which, acco ding o Figu e 13, is s ill in i s in ancy in EAC RVCs.
Table 4shows o al o wa d GVC pa icipa ion by sec o , simila o Table 3 o backwa d GVC
pa icipa ion, and highligh s conside able he e ogenei y ac oss EAC coun ies and sec o s. In he
EAC5 (excl. Congo), a ound 21% o g oss expo s in ag icul u e and manu ac u ed p oduc s a e
e-expo ed as pa o GVCs.
Since EAC o wa d GVC in eg a ion ocuses on Uganda and Kenya, I also examine his link a
he sec o le el. Based on EM 2015-19 a e ages, Kenya expo s 81 million USD h ough Uganda,
which amoun ed o 4.4% o Kenya’s VS1 and 0.76% o i s g oss expo s. 54% o hese 81 million
15
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 100
Figu e 13: EAC Fo wa d GVC Pa icipa ion: Re-Expo ing GVC Pa ne s
A e age EMERGING 2015-2019 Re-Expo ed Con en Sha es (VS1)
UGA
E2R: 18.3%
BM: 13.2%
TZA
E2R: 23.3%
BM: 16.3%
KEN
E2R: 17.4%
BM: 12.6%
RWA
E2R: 28.6%
BM: 20.1%
BDI
E2R: 22.4%
BM: 16.1%
COD
E2R: 33.7%
BM: 23.5%
EMERGING EORA EMERGING EORA EMERGING EORA EMERGING EORA EMERGING EORA EMERGING EORA
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Da abase
Sha e o Re−Expo ed Expo s Con en
Pa ne
UGA
TZA
KEN
RWA
BDI
COD
SSA
MEA
EUU
ECA
NAC
SAS
ASE
CHN
ROA
LAC
OCE
No es: Figu e shows a b eakdown o o wa d GVC in eg a ion by GVC pa ne acco ding o EM 2015-2019 a e ages. The
classical VS1 measu e o Daudin e al. (2011) (Eq. 6, also e med E2R) is used o de e mine each pa ne ’s sha e in o al
VS1. The heade s p o ide o e all VS1 using bo h E2R and he co ec ed measu e by Bo in & Mancini (2019).
Table 4: EAC Fo wa d GVC Pa icipa ion: Sec o al He e ogenei y
A e age EMERGING 2015-2019 Re-Expo ed Con en Sha e (%) (VS1 ollowing BM)
sec o UGA TZA KEN RWA BDI COD Mean Median EAC6 EAC5
AFF 16.1 17.0 27.8 13.5 21.0 21.8 19.5 19.0 21.3 21.2
MIN 28.1 14.3 15.7 3.4 6.8 31.1 16.6 15.0 30.7 14.7
FBE 11.3 19.3 11.4 13.2 19.2 15.6 15.0 14.4 13.3 12.9
MAN 22.0 23.4 11.9 42.8 21.6 23.7 24.2 22.7 22.5 21.0
SRV 7.8 10.2 10.0 7.6 8.3 12.1 9.3 9.2 9.6 9.5
No es: Table epo s o al o wa d GVC pa icipa ion (VS1) ollowing Bo in & Mancini (2019) using he
EM 2015-2019 a e age in pe cen age e ms. These sha es a e epo ed o each EAC6 coun y and o he
EAC6 and EAC5 as a whole, which includes e-expo ed VA by EAC membe s among each o he . They
a e hus expo -weigh ed a e ages. The ’Mean’ and ’Median’ columns gi e unweigh ed EAC6 a e ages.
a e manu ac u ed goods, 20% a e se ices, and 17% a e ag icul u al p oduc s. Uganda, on he
o he hand, expo s 30 million USD h ough Kenya, which amoun s o 3.2% o Ugandan VS1 and
0.58% o Ugandan g oss expo s. O hese 30 million, 45% a e ag icul u al p oduc s, 22% se ices,
16% FBE, and 18% o he manu ac u ing. The links be ween hese wo coun ies accoun o he
bulk o EAC o wa d GVC in eg a ion, summa ized compac ly by Table 5. O pa icula in e es
in his able is he EAC sha e in sec o al VS1, which is high a 20.7% o Kenyan manu ac u es,
indica ing ha abou 1/5 h o e-expo ed VA in Kenyan manu ac u ing is expo ed by i s EAC
pa ne s. O he no able igu es a e he 41%/29% EAC sha es in e-expo ed Rwandan/Kenyan
mining expo s, which a e, howe e , e y small in alue.
Table 5: EAC Fo wa d GVC In eg a ion a he Sec o Le el
A e age EMERGING 2015-2019 T adi ional VS1 Es ima es (Daudin e al.,2011)
VS1 (Re-Expo ed By EAC Pa ne s) To al + EAC Sha es EAC Sha e in Sec o al VS1
Coun y AFF FBE MAN MIN SRV SUM VS1 EXP AFF FBE MAN MIN SRV
UGA 18.23 6.05 13.52 0.00 11.98 49.78 5.60 0.95 6.36 9.05 6.37 6.79 4.12
TZA 11.06 9.20 12.40 1.15 17.64 51.45 2.80 0.61 3.44 5.89 3.03 8.91 1.91
KEN 19.58 9.24 65.88 0.98 28.33 124.01 6.83 1.17 3.22 6.50 20.74 28.73 3.86
RWA 3.63 3.08 3.39 0.00 3.59 13.69 2.86 0.80 13.98 12.99 1.21 41.04 2.27
BDI 0.37 1.27 0.38 0.01 0.33 2.37 4.37 0.97 5.49 7.49 2.98 3.01 2.24
COD 0.22 0.10 1.33 0.46 0.34 2.45 0.06 0.02 0.07 0.08 0.06 0.05 0.07
No es: VS1 is eco ded in million USD, sha es in pe cen age e ms. Column ’SUM’ gi es o al coun y VS1 h ough EAC
pa ne s, and columns ’VS1’ and ’EXP’ gi e he sha e o his in he coun y’s o al VS1 and g oss expo s, espec i ely.
To comple e he pic u e, Figu e 14 shows he sec o -le el sha es in o wa d GVC pa ne s o
he EAC5 (Excl. Congo). The EAC sha e is highes in mining a 13%, bu , Congo being excluded,
16
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 101
mining VS1 comp ises only 13 million USD, compa ed o 2.8/3 billion in AFF/FBE, 7.8 billion in
manu ac u ing, and 5.6 billion in se ices e-expo s. Among hese, he EAC has a sha e o 4%
in AFF and 6.7% in bo h FBE and MAN, indica ing ha manu ac u ing accoun s o he bulk o
GVC o wa d egional in eg a ion. The bigges o wa d GVC pa ne in all sec o s emains he
EU, a sha es be ween 47% o AFF and 16% o MIN and MAN.
Figu e 14: EAC Fo wa d GVC Pa icipa ion: GVC Pa ne s by Sec o
Based on A e age EMERGING 2015-2019 EAC Expo s (Excl. Congo)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
AFF MIN FBE MAN SRV
B oad Sec o (5−Sec o Agg ega ion)
Sha e o Re−Expo ed Con en (VS1)
Pa ne
UGA
TZA
KEN
RWA
BDI
COD
SSA
MEA
EUU
ECA
NAC
SAS
ASE
CHN
ROA
LAC
OCE
No es: Figu e shows a sec o -le el b eakdown o o al EAC5 VS1 (E2R) by GVC pa ne using EM (2015-2019) a e ages.
4.3 T ends in EAC Regional In eg a ion in Value Added Te ms
While o e all EAC GVC in eg a ion appea s ela i ely s able, exemp ing an inc ease in VS in
he smalle economies and a g adual decline in VS1, he e may be s onge ends in egional
in eg a ion ela i e o o e all ade and GVC in eg a ion - as e iden in g oss ade lows. In his
sec ion, I hus in oduce ou me ics o ack EAC egional in eg a ion h ough VA in supply
chains ela i e o he membe s’ o e all GVC pa icipa ion. The i s me ic is he sha e o FVA in
a membe ’s p oduc ion/expo s accoun ed o by i s EAC neighbou s. I is de ined as
VSEAC
uj =1
VSuj X
oi∈EAC, o=u
boi,uj ∀uj ∈EAC,(7)
whe e VSuj is de ined as in Eq. 5. VSEAC is hus a ela i e measu e acking he EAC sha e in VS,
as shown also in Figu e 10, such ha he o e all EAC VA sha e in domes ic p oduc ion/expo s
can be compu ed as VSEAC
uj ×VSuj ∀uj. I de ine an analogous measu e o VS1 as he p opo ion
o DVA in e-expo ed expo s expo ed by EAC pa ne s a es, also isible in Figu e 13
VS1EAC
oi =X
uj∈EAC,u=o
beoi,ujX
uj,u=o
beoi,uj ∀oi ∈EAC.(8)
These wo me ics e ec i ely ack he ole o he EAC in membe s’ GVC pa icipa ion. They,
howe e , do no accoun o he impo side, i.e., he EAC’s ole in p o iding goods and se ices o
membe s’ ela i e o ROW. I hus compu e wo addi ional me ics o cap u e his aspec o egional
in eg a ion. The i s is he sha e o EAC VA in membe s’ impo s, which I deno e by VAIEAC.
Conside eu he ec o o g oss expo s o EAC using coun y u∈EAC om each coun y-sec o .
I hen compu e he VA o igins o hese expo s o coun y uas
eVA
u=VBeu,(9)
whe e eVA
udeno es he ec o , wi h elemen s eVA
oi,u, o VA supplied by each coun y-sec o (oi) in
hese impo s o coun y u. F om eVA
u, he sha e o EAC VA is easily compu ed as
VAIEAC
u=X
oi∈EAC,o=u
eVA
oi,uX
oi,o=u
eVA
oi,u.(10)
17
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 102
Figu e 21: G ow h o (N)RCA Be ween 2006-2010 and 2015-2019 (Medians)
RWA
BDI
EAC5
UGA
TZA
KEN
−100
−50
0
50
100
−100
−50
0
50
100
−100
−50
0
50
100
PAO
FIB
PTE
TRA
SMH
CON
EGW
MAN
TEQ
ELM
MPR
PCM
WAP
TEX
FBE
MIN
AFF
PAO
FIB
PTE
TRA
SMH
CON
EGW
MAN
TEQ
ELM
MPR
PCM
WAP
TEX
FBE
MIN
AFF
G ow h Ra e o (N)RCA (Pe cen )
Sec o
Sou ce: EMERGING BACI Measu e: O e all Rela i e o EAC In Inne −EAC T ade
No es: Figu e shows he g ow h a e in pe cen age e ms be ween he 2006-10 and 2015-19 (N)RCA medians. EM
es ima es a e based on DVA in expo s, BACI on g oss expo s. Appendix Figu e B5 and Table B5 show he alues.
6 T ade, Value Chains, and Economic De elopmen
Ha ing ex ensi ely documen ed he pa e ns o EAC global and egional in eg a ion h ough
bo h adi ional ade (Sec ion 3) and alue chains (Sec ion 4) while highligh ing salien ends,
po en ials, imbalances, and policy p io i ies, a c i ical emaining policy ques ing ega ds he impac
o di e en o ms o in eg a ion on economic de elopmen in he egion. This sec ion a emp s o
p o ide causal educed- o m e idence on his ma e ollowing Kumm i z (2016).
6.1 Re iew o he Empi ical Li e a u e
The WDR Chap e 3 p esen s ex ensi e co ela ional e idence ha GVC pa icipa ion is associa ed
wi h gains in GDP pe capi a g ow h and labo p oduc i i y, po e y educ ion, skill ans e , and
employmen c ea ion, o en bene i ing gende equali y, bu also wi h challenges o axa ion and
highe inequali y (Wo ld Bank,2020;An `as & Cho ,2022). The epo highligh s ha long- e m
i m- o- i m links and specializa ion in GVC- ela ed asks p omo e e icien p oduc ion, echnology
di usion, and access o capi al. A c oss-coun y dynamic g ow h eg ession es ima ed wi h Sys em-
GMM yields an 11-14% imp o emen in pe -capi a GDP ollowing a 10% inc ease in o e all GVC
pa icipa ion, which is con as ed wi h a 2% gain om inc eased ade in p oduc s ully p oduced
in one coun y. De eloping coun ies expe ience he bigges g ow h spu upon ansi ioning om
commodi ies o limi ed manu ac u ing, ypically eaping 20% income gains wi hin 3 yea s.
These indings a e b oadly echoed in much mac oeconomic wo k on GVCs and economic
de elopmen . Among he i s , Kumm i z (2016) assesses he e ec o GVC pa icipa ion on labo
p oduc i i y and DVA using OECD ICIOs o 61 coun ies and 34 indus ies om 1995-2011. He
de elops a no el ins umen al a iable (IV) o GVC pa icipa ion - a VA ade esis ance index
combining hi d-coun y ade cos s wi h indus y-speci ic echnological a iables - and es ima es
ha a 1 pe cen inc ease in VS leads o 0.11% highe DVA in he a e age indus y, and a 1 pe cen
inc ease in VS1 leads o 0.60% highe DVA and 0.33% highe labo p oduc i i y. The e ec s
o o wa d in eg a ion (VS1) a e g ea e o high-income coun ies, whe eas low/middle-income
coun ies show s onge e u ns om backwa d in eg a ion (VS).
24
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 109

Al omon e e al. (2018), using an IV combining he g owing size o con aine ships since 1997
wi h he ex-an e a ailabili y o deep sea po s, also p esen causal e idence o a posi i e e ec o
GVC- ela ed ade (DVA in expo s) on g ow h, which is la ge han he e ec o adi ional ade.
Bo h a e h ee-s ep IV s a egies ollowing Rome e al. (1999) and Fey e (2009,2019).
Cons an inescu e al. (2019), used he WIOD wi h 40 coun ies and 13 sec o s o e 1995-2009,
and ind ha (backwa d) GVC pa icipa ion boos s labo p oduc i i y. An inc ease o 10% yields
an a e age p oduc i i y inc ease o 1.7%. Examining a sample o 24 eme ging economies, Jangam
& Ra h (2021) show ha bo h o wa d and backwa d pa icipa ion signi ican ly imp o e DVA
in expo s om 1995–2011. Al un e al. (2023) examine he ole o GVC pa icipa ion in high-
echnology expo s o 120 coun ies du ing 1995–2019 and ind ha GVC pa icipa ion co ela es
s ongly wi h high- ech expo s. Kumm i z e al. (2017) ind ha GVC pa icipa ion inc eases
VA, especially in ups eam s ages. Pahl & Timme (2020) s udy he e ec s o GVC pa icipa ion
on VA in 58 coun ies (o which 38 de eloping) be ween 1970 and 2008 and ind a obus posi i e
e ec on manu ac u ing p oduc i i y g ow h, especially o less p oduc i e coun ies whe e he
dis ance o he global on ie is la ge. Howe e , hey ind no posi i e e ec s on employmen and
some nega i e e ec s o middle-income coun ies. Thus, hey conclude ha GVC pa icipa ion is
a mixed blessing, inducing skill-biased echnological change, in line wi h Rod ik (2018). Kumm i z
(2016) no es ha GVCs do no necessa ily need o bene i de eloping coun ies as he e could be
ad e se e ms o ade e ec s o dec eases in p oduc i e endowmen s om hea y engagemen in
hem, which is also shown in some heo e ical models such as Baldwin & Robe -Nicoud (2014). An
a gumen by Kumm i z (2015) is also ha GVCs migh subs i u e o eign o domes ic supplie s.
Howe e , his empi ical esea ch sugges s ha FVA is a a he a complemen o DVA.
Be e elli e al. (2019) p o ide empi ical e idence on he ela ionship be ween domes ic alue
chains (DVCs) and GVCs. They ind ha ac oss coun ies a di e en s ages o de elopmen , highe
domes ic in eg a ion by 1 s anda d de ia ion aises GVC in eg a ion h ough backwa d linkages
(VS) by 0.4%. DVC in eg a ion explains up o 30% o o e all GVC pa icipa ion. They explain
hese esul s wi h ixed cos s o agmen a ion and swi ching supplie s: ”High agmen a ion cos s
allow, due o hei sunk na u e, DVCs o ac as s epping s ones o GVCs” (Be e elli e al.,2019).
Shen e al. (2021) cons uc a simple dynamic model o illus a e he mic o-mechanism o
indus ial upg ading along he GVC. Using he WIOD, hey ind ha mo e ups eam indus ies
co ela e wi h highe p o i abili y and VA, capi al in ensi y, and R&D in es men . Thei dynamic
model explains his h ough h ee e ec s: endogenous sunk cos s, dec easing in e media e inpu
p ice elas ici y, and sequen ial p icing e ec unce ain y. They show ha he empi ical pa e ns
e ealed in China a e consis en wi h he model’s p edic ions. Tian e al. (2022) also s udy he
ela ionship be ween GVC pa icipa ion and indus ial upg ading (p ocess, p oduc , and skill
upg ading) using he WIOD. They ind ha GVC in eg a ion inc eases indus ial upg ading
o de eloping and de eloped coun ies. De eloping coun ies bene i mo e om backwa d GVC
pa icipa ion h ough impo ing mo e sophis ica ed inpu s and lea ning h ough embodied knowledge,
whe eas de eloped coun ies upg ade mo e h ough o wa d GVC pa icipa ion. They in e p e
hei indings as e idence agains mo e c i ical oices and models ques ioning he bene i s o
de eloping coun y pa icipa ion in GVCs, such as Baldwin & Robe -Nicoud (2014)o Dalle
e al. (2013). The mac oeconomic s udy o Lwesya (2022) on GVCs and economic upg ading in
he EAC, discussed in he in oduc ion, also inds a signi ican posi i e e ec o lagged FVA on
DVA in EAC5 expo s, wi h coe icien s implying an elas ici y o 0.49.
Many mo e mic oeconomic s udies also ind posi i e e ec s o GVC pa icipa ion on indus ial
de elopmen . Pie ma ini & Rub´ıno ´a (2014), o example, use indus y-le el R&D and pa en
da a o a sample o 29 coun ies du ing 2000-2008 and show ha knowledge spillo e s inc ease
wi h he in ensi y o supply chains linkages be ween coun ies and ha hese spillo e s a e la ge
han spillo e s om adi ional ade lows. Simila e idence is p esen ed by Benz e al. (2015),
who use i m-le el da a o show ha o sho ing leads o knowledge spillo e s and ha o wa d
spillo e s ( om p oduce s o use s i in e media e inpu s) a e s onge han backwa d spillo e s.
Mic oeconomic s udies in ol ing EAC membe s include Ba ien os e al. (2016)’s case s udy
o supe ma ke expansion wi hin sou he n and eas e n A ica, showing ha highe quali y and
25
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 110
sou cing equi emen s by global and egional supe ma ke chains induced imp o ed p ocesses
in Kenyan and Ugandan ho icul u e, allowing di e si ica ion and highe ui s and ege able
expo s (Wo ld Bank,2020). A s udy o Kenyan ho icul u e by K ishnan (2018) shows ha
incomes inc eased a e con ac a me s adop ed quali y s anda ds by hei in e na ional buye s,
and also ha oppo unis ic RVCs eme ged when supplie s ound hei p oduce ejec ed due o
lack o s anda ds compliance, which g adually led o mo e o ganized RVCs wi h own s anda ds
and p ocu emen s a egies. Dihel e al. (2018) s udy he e ec s o alue chain pa icipa ion on
A ican a me s ia a su ey o 3,935 a me s, 60 agg ega o s, and 56 buye s in he maize, cassa a,
and so ghum alue chains in Ghana, Kenya, and Zambia, and show ha con ac ed a me s saw
g ea e s uc u al ans o ma ion, highe ou pu , and be e access o seeds, e ilize s, pes icides,
echnology, and ex ension se ices han non-con ac ed a me s. These indings a e commensu a e
wi h Daly e al. (2016)’s s udy o Maize alue chains in Eas A ica, which iden i ies Kenyan
p ocesso s as he lead i ms demanding Ugandan supplie s o p o ide high-quali y maize, and
documen in es men s in o Ugandan p oduc ion acili ies by Sou h A ican and Ge man companies.
They also documen challenges in access o inance o a me s, insu icien comme cial scale, and
lack o communica ion o ma ke signals and s anda ds along he alue chain.
6.2 Empi ical S a egy
A na u al idea o assess he impac o GVC in eg a ion on economic de elopmen is o in es iga e
i highe GVC pa icipa ion is associa ed wi h highe domes ic VA (GDP). Many au ho s do his in
one o m o ano he , including Lwesya (2022) who use DVA in expo s. Following Kumm i z (2016)
and Rod iguez & Rod ik (2000), I a gue ha unning eg essions a he coun y le el is subjec
o omi ed a iable bias om many ac o s a ec ing GVC in eg a ion and economic de elopmen .
Thus, a sec o -le el eg ession amewo k wi h coun y-sec o , coun y-yea , and sec o -yea ixed
e ec s is ad an ageous o cap u e many con ounding ac o s such as in as uc u e, geog aphy,
ins i u ions, and economic policies, o mul ila e al esis ance. A ca ea is ha he coe icien s
only cap u e wi hin-indus y e ec s, and a e he e o e likely lowe bound es ima es o he o e all
economic e ec s o GVC in eg a ion. My baseline speci ica ion is
log(VAcs ) = βlog(GVCcs ) + αcs +βc +γs +ϵcs ,(14)
wi h GVCcs a GVC indica o (such as VS o VS1), and αcs,βc , and γs coun y-sec o , coun y-
yea , and sec o -yea ixed e ec s, espec i ely. The coe icien βcould s ill be biased by sec o -le el
con ounde s, measu emen e o in GVCcs , simul anei y, and e e se causali y.
To add ess hese issues, Kumm i z (2016) de elops an ins umen o GVC pa icipa ion
combining hi d-pa y ade cos s and indus y dis ance in he alue chain o induce exogenous
a ia ion in FVA in expo s, which o ms he basis o simple GVC indica o s. The i s s ep is o
p edic he elemen s o he VBE (VA expo s) ma ix using exogenous ade cos s and indus y
s uc u e and hen compu e VS and VS1 indica o s ollowing Equa ions 5and 6using his p edic ed
ma ix ˆ
VBE. These exogenous componen s ˆ
VSuj and ˆ
VS1oi can hen be used o ins umen VS
and VS1 in Eq. 14. Speci ically, o each GVC ins umen , a di e en ˆ
VBE ma ix is cons uc ed,
whose ( ime- a ying) elemen s beoiuj a e p edic ed using equa ions
log( ˆ
beVS
oiuj ) = βVS log(τou ×δoiuj) + αuj +βu +γj +ϵoiuj ∀u=o(15)
log( ˆ
beVS1
oiuj ) = βVS1 log(τou ×δoiuj) + αoi +βo +γi +ϵoiuj ∀o=u(16)
o cons uc ˆ
VSuj and ˆ
VS1oi, espec i ely.14 Thus, all a ia ion in he o eign sou ces o VA (oi )
in Eq. 15 and in he usage (uj ) in Eq. 16, is due o he exogenous ade cos e m: log(τou ×δoiuj).
The ade cos e m has wo componen s: τou is an expo -weigh ed es ima e o he bila e al
ade cos s o he supplie o VA (o) wi h all o he ading pa ne s (k=u) in pe iod . This is done
o p ese e he exogenei y o he ade cos measu e o ac o s a ec ing he speci ic bila e al ou
link, which may be co ela ed wi h GVC- ela ed ade along his link. Following Kumm i z (2016),
I use he Wo ld Bank ESCAP ade cos s da abase (A is e al.,2016) based on No y (2013), which
p o ides a holis ic, a i -equi alen measu e o o al ade cos s implied by an in e se g a i y model.
14E.g., ˆ
VSuj is ob ained by summing column uj o a ma ix ˆ
VBEVS
whe e domes ic elemen s a e 0 and he
non-domes ic (u=o) elemen s a e es ima ed using Eq. 15, which includes ixed e ec s o he using coun y-sec o
(uj) and ime (u ,j ) dimensions (as in he inal model). Simila ly, Eq. 16 es ima es ˆ
VBEVS1
o ob ain ˆ
VS1oi .
26
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 111
The second e m, δoiuj, is a ime-in a ian measu e o he dis ance be ween indus ies oi and
uj along he GVC. I is de ined as δij = 1/(uoi ×duj), whe e uoi =1
TP uoi is he a e age
ups eamness o coun y-sec o oi as de ined in Eq. 12 and duj =1
TP duj a co esponding
downs emness index, as desc ibed e.g. in An `as & Cho (2022) and oo no e 10.Kumm i z
(2016) no es ha he indi ec ade cos s (τou ) ha e a la ge e ec on VA o indus ies sepa a ed
by mo e s ages (δoiuj). The index δoiuj is in e ed since uoi and duj ha e a posi i e ela ionship
wi h he elemen s o VBE, o yield a ade cos index τou ×δoiuj nega i ely ela ed o beoiuj .1516
I also es ima e economic e u ns o g oss ade and ade in inal goods. To ins umen hese, I
omi he indus y dis ance componen and ins ead cons uc a sec o -le el ime- a ying 3 d-pa y
ade cos measu e τoiu , ob ained as expo s-weigh ed a e age o sec o iin coun y o’s expo s
o all des ina ion coun ies k=u. This is hen used o p edic bila e al sec o -le el ade in g oss
and inal goods using simila ze o-s age equa ions o 15 and 16, and he p edic ions a e summed
ac oss impo e s o yield app op ia e ins umen s o g oss and inal goods expo s, espec i ely.
A las , I also conside e u ns o egional in eg a ion in bo h g oss and VA e ms using an
al e na i e inal s age model o he o m
log(VAcs ) = β1log(GVCcs ) + β2SHEAC
cs ×log(GVCcs ) + αcs +βc +γs +ϵcs ,(17)
whe e SHEAC
cs is he EAC sha e in GVCcs . Following Sec ion 4.3, his is VSEAC and VS1EAC o
GVC indica o s and he EAC sha e in g oss/ inal expo s o adi ional ade. The coe icien β2
gi es he addi ional impac when SHEAC
cs is inc eased by one uni (100%), i.e., a 1% inc ease in
egional ade yields a β1+β2% inc ease in VA, whe eas a 1% inc ease in ex a- egional ade has
an impac o β1%. Since he egional sha e is a componen o GVCcs , ob aining an ins umen
o i om he ze o-s age p edic ions is s aigh o wa d. The RHS o he i s s ages hus mi o
Eq. 17, wi h SHEAC
cs and GVCcs eplaced by hei ze o-s age p edic ed measu es.
My de aul sample includes he ull numbe o sec o s (26 o EORA, 134 o EM) o 5
EAC coun ies: Uganda, Rwanda, Tanzania, Kenya, and Bu undi.17 Es ima ions a e un using
indica o s compu ed on EORA 2021, EORA 2015, and EM. Wi h each da abase, I un one
se o es ima ions using he ull se o sec o s and one using only manu ac u ing sec o s (all
sec o s mapping o b oad sec o s FBE, TEX, WAP, PCM, MPR, ELM, TEQ, MAN, in Table
2). Un o una ely, wi h EM, all es ima es a e s a is ically insigni ican and close o ze o. This
indica es ha he high esolu ion o 134 sec o s in hese ables is no sui able o e alua ing
e u ns o ade and GVC pa icipa ion in he EAC5. Agg ega ing o 17 b oad sec o s also yields
insigni ican esul s due o he sho ime dimension o 6 yea s. Thus, I do no epo EM esul s.
15Unlike Kumm i z (2016), I employ an indus y dis ance measu e (δoiuj ) a he coun y-sec o le el, whe eas
he uses a measu e (δij ) o pu e indus y dis ance ha is a e aged ac oss coun ies as well. While his common
echnology assump ion may be app op ia e o his sample o mos ly OECD economies in he OECD TIVA ICIO
ables, he ins umen cons uc ed using his o mula ion lacks some ele ance o he EAC5. This sugges s ha
indus ies in de eloping coun ies use di e en echnologies and ha e di e en GVC posi ions han he same
indus ies in ad anced economies. While using a bila e al-sec o -le el indus y dis ance measu e may pa ly
comp omise he exogenei y o he ins umen , his is unlikely because his dis ance is s ill ime-in a ian , and
he 2SLS eg essions include iple ixed e ec s. Thus, he iden i ying a ia ion s ill comes om ime- a ia ion
in he ade cos e m (τou ), and using a mo e accu a e measu e o indus y dis ance me ely helps inc ease he
ele ance o his e m. Empi ically, I ind ha compu ing wo ins umen s using bo h δoiuj and δou, and including
hem bo h in he i s s age o en yields a sizeable imp o emen in he i , indica ing ha he di e ence o local
indus y s uc u e o he wo ld a e age in e ac ed wi h ade cos s has some p edic i e powe o FVA in de eloping
coun ies. In all cases, howe e , he ins umen s a e e y weak.
16Ano he di e ence o Kumm i z (2016) is ha I smoo h bila e al ade cos s using a cen e ed 3-yea MA and
impu e missing alues a he end o he sample using he las MA obse a ion ca ied o wa d. This is sensible
because ade cos s based on an in e ed g a i y model a e endogenous o cu en ade lows and, he e o e, mo e
ola ile han pu e echnological o egula o y changes would wa an . The smoo hing s ep does no comp omise
he ins umen ’s ele ance, con i ming ha he ESCAP measu e is noisy. Appendix Figu e B6 shows he aw and
smoo hed bila e al ade cos s among EAC5 membe s. In e es ingly, he ESCAP es ima es sugges ha ading wi h
Kenya is signi ican ly less cos ly, and Kenya and Uganda also epo ade cos s below 100% on each o he . These
cos s a e endogenous o obse ed ade lows, and he s ong ade links be ween Kenya and Uganda ha e al eady
been highligh ed se e al imes. Howe e , his pe spec i e en e ains he possibili y ha high and asymme ic ading
cos s may be ano he eason o sluggish and asymme ic EAC in eg a ion in supply chain ade. In he amewo k
o An `as & De Go a i (2020), high ade cos s imply g ea e impo ance o egional GVC pa icipa ion. Since he
IV ade cos measu e (τou ) is a weigh ed a e age o o igin’s (o) ade cos s wi h hi d pa ies, and EAC membe s
ade much mo e wi h ROW han wi h each o he , an accu a e and imely ep esen a ion o egional ade cos s is
i ele an o he iden i ica ion.
17Sou h Sudan is omi ed because o da a quali y conce ns, Congo because o lacking RVC in eg a ion wi h he
EAC, and di e en ading pa e ns. The inclusion o Congo does no signi ican ly al e he esul s.
27
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 112
6.3 Resul s: G oss T ade and T ade in Final Goods
Table 7 epo s esul s o g oss ade using he ull sample o sec o s, and Table 8shows iden ical
eg essions o he subse o manu ac u ing sec o s. In bo h ables, he ins umen s a e weak, and
wi h one excep ion, no signi ican ly di e en om OLS.18 The OLS esul s sugges an elas ici y
o VA o g oss ade o 0.13-0.25, in line wi h he 0.2 epo ed by he WDR. The esul s a e also
cong uen o Al omon e e al. (2018), who ind la ge e ec s a ound 0.3 using he WIOD and e y
simila OLS and IV coe icien s, wi h IV being sligh ly la ge han OLS. The e ec s o ade in
inal goods on VA a e sligh ly lowe a 0.1-0.2, and he e ec s o bo h g oss and inal goods ade
in manu ac u ing sec o s (Table 8) a e e en lowe a ≤0.1. This indica es ha in e media e ade,
i.e., GVC- ela ed ade, is mo e c i ical o economic de elopmen in he EAC, pa icula ly o
manu ac u ing sec o s whe e in e media es accoun o a la ge ac ion o o al ade.
Table 7: G oss T ade EAC5 Reg essions
Dependen Va iable: log(VA)
Expo s Measu e: G oss Final Goods
Da a: EORA21 EORA15 EORA21 EORA15
Model: OLS IV OLS IV OLS IV OLS IV
Va iables
log(E) 0.2454∗∗∗ 0.1072 0.1305∗∗∗ 0.1503∗0.1892∗∗∗ 0.1931∗∗ 0.1036∗∗∗ 0.1776∗∗
(0.0419) (0.0770) (0.0233) (0.0777) (0.0353) (0.0818) (0.0207) (0.0719)
Fixed-e ec s
# coun y-sec o 130 130 129 129 130 130 129 129
# coun y-yea 110 110 80 80 110 110 80 80
# sec o -yea 572 572 416 416 572 572 416 416
Fi s a is ics
Obse a ions 2,740 2,740 2,023 2,023 2,740 2,740 2,023 2,023
R20.9859 0.9856 0.9928 0.9928 0.9855 0.9855 0.9927 0.9927
Wi hin R20.0485 0.0331 0.0238 0.0232 0.0269 0.0269 0.0143 0.0070
Wu-Hausman, p- alue 0.0101 0.5572 0.9703 0.0819
Kleibe gen-Paap (1s s age), F 23.99 28.82 3.086 20.28
Wald (1s s age), p- alue <0.001 <0.001 0.0790 <0.001
D iscoll-K aay (L=2) s anda d-e o s in pa en heses. Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
No es: Table shows he elas ici y o DVA o g oss and inal goods expo s using EORA wi h ull 26 sec o esolu ion o 5 EAC coun ies: Uganda,
Tanzania, Kenya, Rwanda, and Bu undi. Es ima ions a e done using bo h he i s edi ion o EORA (EORA15: yea s 2000-2015) and he ex ended
e sion (EORA21: yea s 2000-2021). The IV speci ica ion uses a sec o -le el expo s weigh ed a e age o hi d-coun y ade cos s as an ins umen .
Table 8: G oss T ade EAC5 Reg essions: Manu ac u ing Sec o s
Dependen Va iable: log(VA)
Expo s Measu e: G oss Final Goods
Da a: EORA21 EORA15 EORA21 EORA15
Model: OLS IV OLS IV OLS IV OLS IV
Va iables
log(E) 0.1015∗∗∗ 0.9659 0.1175∗∗∗ 0.2198 -0.0566 -0.1048 0.1075∗∗∗ -0.0211
(0.0324) (0.6435) (0.0299) (0.1746) (0.0920) (0.5590) (0.0303) (0.0400)
Fixed-e ec s
# coun y-sec o 40 40 40 40 40 40 40 40
# coun y-yea 110 110 80 80 110 110 80 80
# sec o -yea 176 176 128 128 176 176 128 128
Fi s a is ics
Obse a ions 859 859 640 640 859 859 640 640
R20.9883 0.9817 0.9951 0.9951 0.9882 0.9882 0.9951 0.9950
Wi hin R20.0077 -0.5515 0.0216 0.0052 0.0022 0.0006 0.0216 -0.0093
Wu-Hausman, p- alue 0.1060 0.7317 0.9675 0.5074
Kleibe gen-Paap (1s s age), F 1.009 5.278 0.4104 19.31
Wald (1s s age), p- alue 0.3152 0.0218 0.5217 <0.001
D iscoll-K aay (L=2) s anda d-e o s in pa en heses. Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
No es: Table shows he elas ici y o DVA o g oss and inal goods expo s using EORA wi h a sample o 8 manu ac u ing sec o s (FBE, TEX, WAP,
PCM, MPR, ELM, TEQ, and MAN in Table 2), o 5 EAC coun ies: Uganda, Tanzania, Kenya, Rwanda, and Bu undi. Es ima ions a e done using bo h
he i s edi ion o EORA (EORA15: yea s 2000-2015) and he ex ended e sion (EORA21: yea s 2000-2021). The IV speci ica ion uses a sec o -le el
expo s weigh ed a e age o hi d-coun y ade cos s as an ins umen .
18Appendix Table B7 shows he ze o-s age eg essions, wi h sizeable coe icien s bu weak wi hin-R2.
28
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 113
6.4 Resul s: Backwa d and Fo wa d GVC Pa icipa ion
Appendix Table B7 epo s he ze o s age eg essions p edic ing he elemen s beoiuj . As expec ed,
he ade cos measu es co ela e nega i ely wi h he elemen s o VBE. I hen un bo h OLS and
2SLS ixed-e ec s eg essions acco ding o Eq. 14 using VS and VS1 measu es in log-le els and
ins umen ing hem wi h ˆ
VS and ˆ
VS1, also in log-le els. I es ima e 4 speci ica ions: (1) OLS, (2) IV
wi h a ime-in a ian (δij) indus y-dis ance ins umen as in Kumm i z (2016) (see Foo no e 15),
(2) IV wi h he bila e al (δoiuj) indus y-dis ance ins umen , and (4) IV wi h bo h ins umen s.
Table 9shows he esul s on he ull sample, and Table 10 o he manu ac u ing sample.
Appendix Tables B8 and B9 epo he co esponding i s s ages. The i s s ages a e gene ally
e y weak, wi h many coe icien s insigni ican o o he w ong sign. Since he ade cos measu e
τou ×δoiuj is nega i ely co ela ed wi h VBE, hese nega i e i s -s age coe icien s could indica e
some o e i ing a he ze o s ages (Table B7) which a e also qui e weak. In any case, his indica es
ha he IV/2SLS esul s in Tables 9and 10 need o be ea ed wi h cau ion, e en in cases whe e
i s -s age s a is ics a he bo om o hese ables (such as a sizeable Kleinbe gen & Paap F-
s a is ic) sugges ha hey a e su icien ly s ong. Also no able is ha coe icien s om he ull
EORA 200-2021 sample a e gene ally smalle and mo e o en insigni ican han hose o he WDR
(EORA 2000-2015) sample. This appea s o e lec a end change in he da a upda e om 2016
( he ixed e ec s abso b he s uc u al b eak). Thus, I conside he esul s on he WDR sample
mo e eliable, as i s IO ables we e c ea ed using a consis en me hodology.
Table 9: GVC Pa icipa ion EAC5 Reg essions
Dependen Va iable: log(VA)
Da a: EORA21 (2000-2021) WDR EORA15 (2000-2015)
Model: OLS IV-δij IV-δoiuj 2SLS OLS IV-δij IV-δoiuj 2SLS
Va iables
log(VS) -0.2193∗∗ 0.5786 0.6015 0.2378∗∗∗ -0.0664 0.2050∗∗∗ 0.2096∗∗∗ 0.1922∗∗∗
(0.0841) (0.3895) (0.4202) (0.0827) (0.0539) (0.0418) (0.0412) (0.0435)
Fi s a is ics
Obse a ions 2,740 2,740 2,740 2,740 2,023 2,023 2,023 2,023
R20.9858 0.9776 0.9771 0.9831 0.9927 0.9919 0.9918 0.9920
Wi hin R20.0416 -0.5091 -0.5413 -0.1391 0.0066 -0.1037 -0.1075 -0.0935
Wu-Hausman, p- alue <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Kleibe gen-Paap (1s s age), F 1.652 1.547 32.23 47.22 47.07 53.78
Wald (1s s age), p- alue 0.1988 0.2136 <0.001 <0.001 <0.001 <0.001
Va iables
log(E2R) 0.7351∗∗∗ 0.1842 -0.1586 -0.6351 0.7432∗∗∗ 0.5716∗∗∗ 0.5359∗∗ 0.7366∗∗∗
(0.0396) (1.129) (2.588) (2.591) (0.0607) (0.1831) (0.1907) (0.0597)
Fi s a is ics
Obse a ions 2,740 2,734 2,733 2,733 2,023 2,017 2,016 2,016
R20.9950 0.9894 0.9803 0.9608 0.9976 0.9973 0.9972 0.9976
Wi hin R20.6633 0.2889 -0.3138 -1.618 0.6683 0.6329 0.6164 0.6724
Wu-Hausman, p- alue 0.0678 0.0568 0.0016 0.0638 0.0460 0.8330
Kleibe gen-Paap (1s s age), F 0.4409 0.1569 0.2861 3.421 3.989 4.301
Wald (1s s age), p- alue 0.5067 0.6920 0.7512 0.0645 0.0459 0.0137
Fixed-e ec s
# coun y-sec o 130 130 130 130 129 129 129 129
# coun y-yea 110 110 110 110 80 80 80 80
# sec o -yea 572 572 572 572 416 416 416 416
D iscoll-K aay (L=2) s anda d-e o s in pa en heses. Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
No es: Table shows he elas ici y o DVA o backwa d (VS) and o wa d (E2R) GVC pa icipa ion using EORA wi h ull 26 sec o esolu ion o 5 EAC
coun ies: Uganda, Tanzania, Kenya, Rwanda, and Bu undi. Es ima ions a e done using bo h he i s edi ion o EORA (EORA15: yea s 2000-2015)
and he ex ended e sion (EORA21: yea s 2000-2021) ia OLS and IV/2SLS. The IV models use exogenous GVC pa icipa ion p edic ed by an expo s-
weigh ed a e age o hi d coun y ade cos s in e ac ed wi h bila e al (δij) o bila e al-sec o le el (δoiuj ) indus y dis ance along he alue chain as
ins umen s. Appendix Table B7 shows he ze o s age, and Table B8 he i s s age es ima ions, including he same se o iple ixed e ec s.
O e all, he esul s sugges ha GVC pa icipa ion posi i ely a ec s VA, and ha his e ec
is la ge o o wa d in eg a ion and manu ac u ing sec o s (E2R = VS1 is used he e o a oid
con usion). D awing om he IV esul s in he WDR sample, a 1% inc ease in he o eign con en
o expo s (VS) implies a 0.2% inc ease in VA in he ull sample, and a 0.45-0.5% inc ease in
manu ac u ing VA. On he o he hand, a 1% inc ease in he e-expo ed con en o expo s (E2R)
implies a 0.5-0.7% inc ease in VA, and a 0.8-1% inc ease in manu ac u ing VA.
29
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 114

Table 10: GVC Pa icipa ion EAC5 Reg essions: Manu ac u ing Sec o s
Dependen Va iable: log(VA)
Da a: EORA21 (2000-2021) WDR EORA15 (2000-2015)
Model: OLS IV-δij IV-δoiuj 2SLS OLS IV-δij IV-δoiuj 2SLS
Va iables
log(VS) 0.1344∗∗∗ 1.404 1.638 0.4800∗∗ 0.0590∗0.4394∗∗∗ 0.4562∗∗∗ 0.2089∗∗∗
(0.0227) (1.945) (2.768) (0.1835) (0.0291) (0.1178) (0.1247) (0.0459)
Fi s a is ics
Obse a ions 859 859 859 859 640 640 640 640
R20.9884 0.9685 0.9605 0.9870 0.9951 0.9940 0.9939 0.9949
Wi hin R20.0190 -1.674 -2.356 -0.1064 0.0054 -0.2179 -0.2381 -0.0293
Wu-Hausman, p- alue 0.0974 0.1160 0.0010 0.0025 0.0028 0.0249
Kleibe gen-Paap (1s s age), F 0.4762 0.3118 51.79 61.77 62.66 24.34
Wald (1s s age), p- alue 0.4901 0.5765 <0.001 <0.001 <0.001 <0.001
Va iables
log(E2R) 0.6724∗∗∗ 0.8149∗∗∗ 0.8204∗∗∗ 0.8366∗∗∗ 0.5275∗∗∗ 1.094∗∗∗ 1.214∗∗∗ 0.7602∗∗
(0.0775) (0.0377) (0.0390) (0.0443) (0.1523) (0.3019) (0.3362) (0.2842)
Fi s a is ics
Obse a ions 859 859 859 859 640 640 640 640
R20.9966 0.9963 0.9962 0.9961 0.9978 0.9946 0.9932 0.9972
Wi hin R20.7148 0.6827 0.6801 0.6721 0.5496 -0.0841 -0.3807 0.4427
Wu-Hausman, p- alue 0.0002 <0.001 <0.001 0.1988 0.1378 0.5741
Kleibe gen-Paap (1s s age), F 7.529 9.387 22.39 11.86 9.076 7.092
Wald (1s s age), p- alue 0.0062 0.0022 <0.001 0.0006 0.0027 0.0009
Fixed-e ec s
# coun y-sec o 40 40 40 40 40 40 40 40
# coun y-yea 110 110 110 110 80 80 80 80
# sec o -yea 176 176 176 176 128 128 128 128
D iscoll-K aay (L=2) s anda d-e o s in pa en heses. Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
No es: Table shows he elas ici y o DVA o backwa d (VS) and o wa d (E2R) GVC pa icipa ion using EORA wi h a sample o 8 manu ac u ing sec o s
(FBE, TEX, WAP, PCM, MPR, ELM, TEQ, and MAN in Table 2), o 5 EAC coun ies: Uganda, Tanzania, Kenya, Rwanda, and Bu undi. Es ima ions
a e done using bo h he i s edi ion o EORA (EORA15: yea s 2000-2015) and he ex ended e sion (EORA21: yea s 2000-2021) ia OLS and IV/2SLS.
The IV models use exogenous GVC pa icipa ion p edic ed by an expo s-weigh ed a e age o hi d coun y ade cos s in e ac ed wi h bila e al (δij)
o bila e al-sec o le el (δoiuj ) indus y dis ance along he alue chain as ins umen s. Appendix Table B7 shows he ze o s age, and Table B8 he i s
s age es ima ions, including he same se o iple ixed e ec s.
La ge p oduc i i y gains om o wa d in eg a ion a e also p e alen in he li e a u e. Kumm i z
(2016) inds obus bene i s o GVC backwa d and o wa d in eg a ion on VA in bo h de eloping
and de eloped coun ies, wi h a la ge bene i o o wa d in eg a ion (E2R) a elas ici ies o 0.58
o low/middle-income coun ies and 0.68 o high-income coun ies. VS elas ici ies a e smalle
a ound 0.09/0.21, espec i ely. He also es ima es labo p oduc i i y elas ici ies o E2R o 0.29 o
low/middle-income coun ies and 0.49 o high-income coun ies. In a simila exe cise, Kumm i z
(2015) inds ha high-income coun ies bene i ela i ely mo e om o wa d linkages, whe eas
middle-income coun ies also bene i om backwa d linkages (VS). The esul s p esen ed he e
b oadly align wi h hese indings, sugges ing ha bo h backwa d and o wa d in eg a ion ha e
sizeable e u ns in low-income coun ies. In manu ac u ing sec o s, he es ima es o hese EAC
coun ies a e e en g ea e han hose o Kumm i z (2016), wi h VA elas ici ies om o wa d
in eg a ion close o 1, en a i ely indica ing ha low-income A ican economies (no co e ed by
he OECD TIVA ICIO’s) can bene i subs an ially om inc easing hei supply o high-quali y
manu ac u ing in e media es. I no e ha hese es ima es, while la ge, a e s ill smalle han he
1.1-1.4 elas ici ies o o e all GVC pa icipa ion (VS + VS1) epo ed by he WDR.
6.5 Resul s: Regional In eg a ion
Tables 11 and 12 show egional in eg a ion es ima ions o g oss and GVC- ela ed ade using he
ull sample o sec o s. Appendix Tables B10 and B11 p o ide equi alen esul s o manu ac u ing
sec o s. In he manu ac u ing sample, he in e ac ion e m is s a is ically insigni ican .
In his mo e complex speci ica ion, he ins umen s a e e en weake , hus, o GVC- ela ed
ade, I only epo 2SLS speci ica ions employing bo h se s o ins umen s. Wi h g oss ade
(Table 11), he Wu-Hausmann es ails o ejec he exogenei y o he eg esso in all bu he i s
IV speci ica ion. Wi h GVC- ela ed ade (Table 12) his is also he case o o wa d in eg a ion.
Fo backwa d in eg a ion, he IV speci ica ions ha e a sizeable nega i e wi hin-R2and a huge
nega i e in e ac ion e ec . This signi ies ha he ins umen s a e useless in his mo e complex
case. The e o e, I only in e p e he OLS es ima es.
30
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 115
Table 11 epo s posi i e in e ac ion e ms wi h signi ican coe icien s be ween 0.066 and 0.079,
sugges ing ha an inc ease in egional ade in bo h g oss e ms and in inal goods yields a 6-8
pp. highe VA e u n han an inc ease in ex a- egional ade, whose VA e u n o a doubling
o expo s is es ima ed be ween 10% and 24%. The empi ical esul s hus sugges ha egional
in eg a ion h ough ade is bene icial o economic g ow h in he egion.
Table 11: EAC5 Regional In eg a ion ia G oss T ade Reg essions
Dependen Va iable: log(VA)
Expo s Measu e: G oss Final Goods
Da a: EORA21 EORA15 EORA21 EORA15
Model: OLS IV OLS IV OLS IV OLS IV
Va iables
log(E) 0.2361∗∗∗ 0.0290 0.1034∗∗∗ 0.1051 0.1718∗∗∗ -0.1314 0.0837∗∗∗ 0.2198∗∗
(0.0463) (0.0910) (0.0202) (0.0988) (0.0378) (0.3728) (0.0195) (0.1026)
log(E) ×SHEAC5 0.0555 0.0661∗∗∗ 0.0749∗∗ 0.0468 0.0787∗∗∗ 0.1437 0.0531 -0.0397
(0.0405) (0.0174) (0.0279) (0.0309) (0.0260) (0.0898) (0.0335) (0.0352)
Fixed-e ec s
# coun y-sec o 130 130 129 129 130 130 129 129
# coun y-yea 110 110 80 80 110 110 80 80
# sec o -yea 572 572 416 416 572 572 416 416
Fi s a is ics
Obse a ions 2,740 2,740 2,023 2,023 2,740 2,740 2,023 2,023
R20.9859 0.9854 0.9929 0.9929 0.9856 0.9846 0.9928 0.9926
Wi hin R20.0507 0.0166 0.0307 0.0296 0.0325 -0.0340 0.0181 -0.0071
Wu-Hausman, p- alue 0.0001 0.4127 0.2302 0.2113
D iscoll-K aay (L=2) s anda d-e o s in pa en heses. Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
No es: Table epo s analogous es ima ions o Table 7, bu now including an in e ac ion e m o he log o expo s wi h he egional sha e in
expo s, which cap u es he addi ional e u ns om a egional expansion in ade.
Table 12 indica es a signi ican posi i e OLS in e ac ion e m o o wa d GVC in eg a ion
o o de 0.13-0.14, implying ha a 100% inc ease in o wa d in eg a ion h ough egional ade
yields a 13-14 pp. highe e u n han he al eady sizeable e u n o 73% o ex a- egional o wa d
linkages. Fo backwa d in eg a ion, he e ms on he OLS eg ession a e nega i e o o de 0.14-
0.19, bu he main e ec s a e also nega i e. Since backwa d in eg a ion is pa icula ly p one o
simul anei y, as e iden om Tables 9and 10, a s ong ins umen is needed o iden i ica ion, so
hese OLS coe icien s a e likely no e y meaning ul. Fu he wo k is equi ed o c ea e s onge
ins umen s o GVC pa icipa ion in de eloping coun ies.
Table 12: EAC5 Regional In eg a ion ia RVCs Reg essions
Dependen Va iable: log(VA)
GVC Indica o : Backwa d In eg a ion (VS) Fo wa d In eg a ion (E2R)
Da a: EORA21 EORA15 EORA21 EORA15
Model: OLS 2SLS OLS 2SLS OLS 2SLS OLS 2SLS
Va iables
log(GVC) -0.1926∗∗ 0.1478∗∗∗ -0.0605 0.0832∗∗ 0.7335∗∗∗ 0.7110∗∗∗ 0.7315∗∗∗ 0.7467∗∗∗
(0.0883) (0.0481) (0.0629) (0.0373) (0.0398) (0.1008) (0.0646) (0.1925)
log(GVC) ×SHEAC5 -0.1869∗-1.116∗∗∗ -0.1397 -1.960∗0.1332∗∗∗ 0.6306 0.1383∗0.0082
(0.1015) (0.2550) (0.2464) (1.038) (0.0280) (0.6673) (0.0754) (0.5347)
Fixed-e ec s
# coun y-sec o 130 130 129 129 130 130 129 129
# coun y-yea 110 110 80 80 110 110 80 80
# sec o -yea 572 572 416 416 572 572 416 416
Fi s a is ics
Obse a ions 2,740 2,740 2,023 2,023 2,740 2,733 2,023 2,016
R20.9858 0.9837 0.9927 0.9917 0.9950 0.9948 0.9976 0.9976
Wi hin R20.0459 -0.0980 0.0074 -0.1300 0.6648 0.6502 0.6717 0.6731
Wu-Hausman, p- alue <0.001 <0.001 0.4314 0.7855
D iscoll-K aay (L=2) s anda d-e o s in pa en heses. Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
No es: Table epo s analogous es ima ions o Table 9, bu now including an in e ac ion e m o he log o GVC pa icipa ion (VS o E2R) wi h
i s egional sha e, which cap u es he addi ional e u ns om a egional expansion in GVC pa icipa ion. Due o he weakness o he ins umen s,
only he 2SLS speci ica ion, including bo h ins umen al a iables and hei espec i e in e ac ion e ms, is epo ed.
31
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 116
7 Summa y and Conclusion
Using ich and no el da a sou ces, his s udy igo ously examines he EAC egion’s global and
egional in eg a ion h ough ade and alue chains and hei e ec s on economic de elopmen .
The analysis ocusses on i e membe coun ies: Uganda, Tanzania, Kenya, Rwanda, and Bu undi.
Se e al salien pa e ns s and ou . The i s is ha , exemp ing a small COVID- ela ed ebound
in he sha e o egional ade, he egion is no in eg a ing deepe h ough g oss ade. This is
pa icula ly he case o impo s, whe e he EAC sha e wi h i sel has declined om 10% in 2000
o 7.5% in 2020, while he expo sha e emained cons an a a ound 17%. Howe e , his decline
is mainly d i en by manu ac u ing and masks inc easing egional ade sha es in ag icul u e,
o es y and ishing (AFF) and p ocessed oods and be e ages (FBE). Conside ing o al ade
(expo s+impo s), AFF ade wi h ROW was 10x g ea e han inne -EAC ade in 2020, down
om 40x in 2000. In FBE, his a io declined om 16x (2000) o 11x (2020). In manu ac u ing, i
inc eased om 15x (2000) o 20x (2020). Manu ac u ing ade accoun s o 64% o EAC5 goods
ade, e sus 8.1% (AFF), 15% (FBE), and 13% (mining), and hus d i es agg ega e pa e ns.
EAC membe s assume di e en oles in egional ade. Kenya is a dominan egional expo e ,
pa icula ly o manu ac u ed p oduc s, whe e 40% o i s expo s a e egional, bu only a mode a e
impo e : 18% o Kenyan ag icul u al impo s and less han 1% o i s manu ac u ing impo s
come om he egion. Tanzania also impo s li le om he egion, only 5% o AFF/FBE and
3% o manu ac u ing impo s. I has egional expo sha es be ween 20% (AFF) and 11% (FBE).
The smalle economies a e much mo e in eg a ed, wi h egional expo and impo sha es gene ally
abo e 20%. Pa icula ly Uganda is becoming a signi ican egional expo e in AFF and FBE, wi h
egional expo sha es be ween 35 and 40%. Bu undi also ecen ly became a s ong ag icul u al
expo e , a a egional sha e ising om 5% in 2005 o 60% in 2020.
This sugges ha egional in eg a ion is unequal and ollows a pa e n whe e coun ies i s
become egional ag icul u al expo e s and hen expo e s o limi ed manu ac u es. Howe e ,
hese manu ac u es do no signi ican ly ca e o a la ge sha e o egional demand and hus do
no d i e egional in eg a ion as manu ac u e s become mo e o eign-o ien ed. The FBE sec o is
in e media e be ween hese wo and shows g ea e p omise o egional in eg a ion.
In alue added (VA) e ms, all membe s ha e a o eign con en sha e (VS) be ween 10% (Kenya)
and 30% (Congo). The EU and China a e he g ea es supplie s o EAC o eign con en . Only
Uganda, Rwanda, and Bu undi ha e a high egional sha e in VS o 15-30%. The la ges egional
supplie is Kenya, mos ly o manu ac u ing inpu s, ollowed by Uganda as a egional supplie o
mos ly p ima y ag icul u e. EAC sec o s wi h he highes egional VS sha es a e FBE and sale and
epai o ehicles, uel ade and ho els (SMH) a 14% each. Pe ochemicals (PCM) and ex iles
(TEX) also ha e EAC VS sha es o 7-9%. Co e manu ac u ing sec o s wi h o e all high VS ha e
small egional sha es, such as elec ical machine y (ELM), whe e VS in he EAC is a 35%, bu
he egional sha e in VS is only 2.8%. EAC egional in eg a ion in supply chains hus concen a es
on ood p ocessing and ligh manu ac u ing bu a low egional VS sha es. This highligh s bo h
he g ea po en ial and signi ican challenges in deepening manu ac u ing RVCs.
Fo o wa d GVC pa icipa ion ( e-expo ed expo s o VS1), he EU is he majo GVC pa ne .
Regional o wa d in eg a ion is s ill in i s in ancy, a egional VS1 sha es below 6% in all EAC
membe s. The s onges o wa d linkages a e be ween Kenya and Uganda, wi h Uganda accoun ing
o 4.4% o Kenya’s VS1 (app ox. 80 million USD) and Kenya accoun ing o 3.2% o Ugandan
VS1 (app ox. 30 million USD). A he sec o le el, 21% o ag icul u e and manu ac u ing expo s
a e e-expo ed, ollowed by 15% o mining expo s and 13% o FBE. Bila e ally, 54% o Kenyan
VS1 h ough Uganda a e manu ac u ing inpu s, whe eas 45% o Ugandan VS1 h ough Kenya a e
ag icul u al inpu s, highligh ing he di e en oles o hese wo coun ies in RVCs. 21% o Kenyan
manu ac u ing VS1 is ia i s EAC pa ne s, indica ing ha he supply o egional manu ac u ing
inpu s is quan i a i ely impo an o Kenya. Conside ing o e all EAC VS1 by sec o , he highes
egional sha es a e in manu ac u ing and FBE a 6.7% each, ollowed by AFF a 4%. Regional
o wa d linkages a e hus much weake han backwa d linkages, which, in FBE, a e wice as la ge.
The egion does no seem o be in eg a ing deepe in o GVCs. Backwa d linkages (VS) show
some imp o emen s in he 3 smalle economies in ecen yea s, coun e ed by a sligh decline in
32
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 117
he la ge economies. Fo wa d linkages (VS1) exhibi a e y weak decline. Unlike g oss ade, he
egional VS sha e is g owing a a slow pace o 0.5 pp. pe yea , and egional sha es in VS1 and
VA impo s (bo h g oss and inal) a e g owing a 0.2 pp./yea . Regional in eg a ion is ad ancing
in all sec o s, bu pa icula ly as in FBE, a a es abo e 0.5 pp./yea on all me ics. G ow h in
egional VS1 sha es in FBE is also pa icula ly equi able, whe eas in co e manu ac u ing, which
is in eg a ing in VS and VS1 a 0.25 pp./yea , he g ow h in egional VS1 is en i ely d i en by
Kenya, wi h o he coun ies expe iencing losses. The analysis hus highligh s he po en ial o he
FBE sec o and challenges os e ing mo e ho izon al manu ac u ing RVCs be ween membe s.
Examining he e ol ing posi ion o EAC sec o s in GVCs indica es a downs eam shi in all
membe s and sec o s, implying a mo e owa ds p oduc ion s ages close o inal demand unning
agains he global end owa ds longe GVCs. Fo AFF and FBE, his appea s o be good news as
i implies mo e local alue addi ion. Fo manu ac u ing sec o s, on he o he hand, i indica es a
shi owa ds p ocessing ade a he han high-quali y in e media es. The anspo and ou ism
(TRA) sec o in Kenya also saw a downs eam shi , sugges ing some local upg ading.
Compu ing (New) Re ealed Compa a i e Ad an age indices signi ies ha all membe s and he
egion as a whole ha e sizeable (N)RCA in AFF (4.2) and FBE (5) and a s ong disad an age in
co e manu ac u ing (below 0.1 in ELM and TEQ). The egion also has (N)RCA in a el se ices
(TRA) o 2.8, pa icula ly Kenya (3.1) and Tanzania (3). Rela i e o he egion, Kenya has sligh
(N)RCA in mos manu ac u ing sec o s apa om PCM, whe e Tanzania and Rwanda pe o m
s ongly. Uganda, Kenya, and Bu undi ha e egional (N)RCA in FBE, Kenya and Tanzania in
ou ism (TRA), and Uganda in elec ici y supply (EGW). Excep o he la e , hese es ima es
a e below 2 and hus mode a e. They may ne e heless equi e policy a en ion, pa icula ly in
manu ac u ing whe e Kenya has gained ela i ely. T ends also signi y a sligh o e all EAC (N)RCA
loss in AFF and FBE, encou aging policy e o s o inc ease oods p oduc ion and expo s.
OLS and IV es ima es imply ha EAC in eg a ion h ough ade and GVCs bene i s sec o -
le el economic g ow h a elas ici ies o VA o g oss expo s o 0.13-0.25 and 0.1-0.2 o expo s o
inal goods. This sugges s ha in e media es ade is mo e impo an o economic de elopmen
han ade in inal goods. Manu ac u ing sec o s show lowe e u ns o g oss ade. Examining
GVC pa icipa ion yields IV es ima es o 0.2 (all sec o s) and 0.45 (manu ac u ing sec o s) o
backwa d GVC pa icipa ion (VS) and 0.6 (all sec o s) and 0.9 (manu ac u ing sec o s) o o wa d
GVC pa icipa ion (VS1/E2R). These esona e wi h o he pape s and he 2020 Wo ld De elopmen
Repo inding ha GVC pa icipa ion bene i s economic de elopmen , pa icula ly o wa d linkages
in manu ac u ing. The pape also in es iga es he e u ns o deepe egional in eg a ion is-a- is
global in eg a ion. OLS es ima es sugges ha deepe egional linkages yield addi ional e u ns:
The elas ici y o g oss and inal goods expo s inc eases by 0.06-0.08 o egional ade, and he
elas ici y o o wa d GVC pa icipa ion (VS1) inc eases by 0.13-0.14 o egional links.
The pape hus highligh s bo h p ospec s o and challenges o EAC egional in eg a ion. The
egion demons a es a modes le el o in eg a ion h ough ade and RVCs, which a e concen a ed
in ce ain sec o s. In pa icula , he FBE sec o demons a es highe le els o egional in eg a ion
and g ow h. A he same ime, in eg a ion in manu ac u ing is concen a ing on Kenya’s ole as a
supplie o inpu s, wi h a limi ed supplie ole o o he coun ies. The e is no clea end owa ds
g ea e egional in eg a ion h ough g oss ade, and he pace o in eg a ion in VA ade, while
posi i e, is e y slow. Shi s in compa a i e ad an age sugges a loss o (N)RCA in manu ac u ing,
including FBE, alongside a downs eam shi . This should p omp policy ac ion o a leas inc ease
ou pu and deepen RVCs in he FBE sec o . B oade indus ial policy coo dina ion may also be
necessa y o mi iga e Kenya’s inc easing ole as a egional manu ac u ing hegemon. Sec o -le el
es ima es sugges ha in eg a ion h ough ade and GVCs bene i s domes ic ac i i y, pa icula ly
wi hin RVCs. Thus, any policy ac ion should be conside a e no o slow down o e e se he
(al eady sluggish) end owa ds inc eased EAC egional in eg a ion h ough RVCs.
Rega ding he A CFTA, his s udy shows ha es ablishing a common ma ke among economies
wi h di e en dis ibu ions o compa a i e ad an age may esul in e ical GVCs and RVCs,
leading o a loss o compe i i eness in ce ain sec o s and coun ies, pa icula ly in smalle manu-
ac u ing sec o s. Thus coo dina ion o indus ial and GVC- ela ed policies should be conside ed
oge he wi h he planned p o ocols o es ablish and egula e a common A ican ma ke .
33
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 118
Figu e A1: EORA Da a Quali y Repo s: EAC Mac oeconomic To als
40
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 125

B. Addi ional Tables and Figu es
Figu e B1: A e age T ade Flows by B oad Sec o , 2010-2015: EORA: USD Billions
Ag icul u e & Li es ock Foods & Be e ages Manu ac u ed Goods
0
0
0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0
0
0
0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
BDI
COD
KEN
RWA
SSD
TZA
UGA
0
0
0.01
0.02
0.03
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0
0.01
0.02
0
0
0.01
0.02
0.03
0.04
0.05
0.06
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
BDI
COD
KEN
RWA
SSD
TZA
UGA
0
0
0.09
0.18
0
0.09
0.18
0.27
0.36
0.45
0.54
0.63
0.72
0.81
0.9
0.99
1.08
1.17
1.26
1.35
0
0.09
0
0
0.09
0.18
0.27
0.36
0
0.09
0.18
0.27
0.36
0.45
0.54
0.63
0.72
0.81
BDI
COD
KEN
RWA
SSD
TZA
UGA
0
0
0.5
1
0
0.5
1
1.5
2
2.5
3
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
0
0
0
0.5
1
1.5
2
0
0.5
1
1.5
BDI
COD
KEN
ROW
RWA
SSD
TZA
UGA
0
0
0.2
0.4
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
0
0
0
0.2
0.4
0
0.2
BDI
COD
KEN
ROW
RWA
SSD
TZA
UGA
0
0
2
4
6
0
2
4
6
8
0
2
4
6
8
10
12
14
16
18
20
22
24
0
0
0
2
4
6
0
2
4
BDI
COD
KEN
ROW
RWA
SSD
TZA
UGA
Figu e B2: A e age T ade Flows by B oad Sec o , 2010-2015: EMERGING: USD
Billions
Ag icul u e & Li es ock Foods & Be e ages Manu ac u ed Goods
0
0
0
0.03
0.06
0.09
0.12
0.15
0.18
0.21
0
0.03
0.06
0
0.03
0.06
0.09
0.12
0
0.03
0.06
0.09
0
0.03
0.06
0.09
0.12
0.15
0.18
0.21
0.24
0.27
BDI
COD
KEN
RWA
SSD
TZA
UGA
0
0
0
0.06
0.12
0.18
0.24
0.3
0.36
0.42
0.48
0.54
0.6
0
0.06
0.12
0.18
0
0.06
0.12
0.18
0.24
0.3
0
0.06
0.12
0
0.06
0.12
0.18
0.24
0.3
0.36
0.42
0.48
0.54
0.6
0.66
BDI
COD
KEN
RWA
SSD
TZA
UGA
0
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
0
0.1
0.2
0.3
0.4
0
0.1
0.2
0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
BDI
COD
KEN
RWA
SSD
TZA
UGA
0
0
0.3
0.6
0
0.3
0.6
0.9
1.2
1.5
1.8
0
0.3
0.6
0.9
1.2
1.5
1.8
2.1
2.4
2.7
3
3.3
3.6
3.9
4.2
0
0
0
0.3
0.6
0.9
0
0.3
0.6
BDI
COD
KEN
ROW
RWA
SSD
TZA
UGA
0
0
0.4
0.8
1.2
0
0.4
0.8
1.2
1.6
2
2.4
2.8
0
0.4
0.8
1.2
1.6
2
2.4
2.8
3.2
3.6
4
4.4
4.8
5.2
5.6
6
0
0.4
0
0.4
0
0.4
0.8
0
0.4
0.8
1.2
BDI
COD
KEN
ROW
RWA
SSD
TZA
UGA
0
0
3
6
9
12
15
18
0
3
6
9
12
0
3
6
9
12
15
18
21
24
27
30
33
36
39
42
45
0
0
0
3
6
9
0
3
BDI
COD
KEN
ROW
RWA
SSD
TZA
UGA
41
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 126
Table B1: La ges 50 In e media e EAC T ade Flows: EMERGING 2015-19 A e age
Millions o Cu en USD a Basic P ices
O e all Inne -EAC
F om To Value F om To Value
2 UGA.PCM MEA.MIN 254.72 KEN.PCM UGA.CON 65.28
3 MEA.PCM KEN.FBE 245.28 KEN.PCM RWA.AFF 41.38
4 SAS.PCM TZA.TRA 244.89 KEN.PCM UGA.FBE 40.42
5 SAS.PCM KEN.FBE 233.83 UGA.FBE KEN.TRA 40.12
6 CHN.TEX TZA.TEX 222.33 UGA.FBE KEN.FBE 38.88
7 CHN.TEX KEN.TEX 213.58 TZA.PCM RWA.AFF 38.36
8 CHN.PCM KEN.PCM 213.41 UGA.PCM RWA.AFF 31.44
9 CHN.ELM TZA.ELM 211.46 KEN.MPR UGA.CON 31.15
10 CHN.TEX KEN.TRA 211.10 TZA.AFF KEN.FBE 30.07
11 KEN.TRA EUU.TRA 207.65 KEN.FBE UGA.FBE 29.40
12 TZA.PCM ECA.PCM 196.53 KEN.PCM UGA.AFF 29.18
13 KEN.FBE SAS.FBE 196.09 UGA.AFF KEN.FBE 28.80
14 KEN.AFF EUU.FBE 191.72 KEN.PCM TZA.PCM 27.12
15 MEA.PCM TZA.TRA 191.05 KEN.MPR UGA.MPR 22.62
16 CHN.MPR KEN.EGW 186.79 RWA.FBE KEN.FBE 21.90
17 UGA.PCM MEA.CON 180.61 TZA.TEX KEN.TEX 19.46
18 CHN.TEX KEN.WAP 175.45 RWA.FBE KEN.TRA 17.37
19 SAS.PCM KEN.PCM 172.61 UGA.EGW KEN.CON 17.14
20 TZA.AFF SAS.AFF 163.76 UGA.PCM RWA.TRA 16.29
21 KEN.AFF EUU.AFF 160.71 KEN.FBE UGA.SMH 14.99
22 CHN.PCM KEN.FBE 158.19 UGA.AFF KEN.AFF 14.28
23 SAS.PCM KEN.EGW 156.87 KEN.MPR UGA.PTE 13.76
24 TZA.PCM SSA.MPR 155.21 TZA.WAP KEN.WAP 13.70
25 MEA.PCM KEN.PCM 142.64 TZA.FBE KEN.TEX 13.64
26 MEA.PCM KEN.EGW 142.53 KEN.PCM TZA.FBE 13.10
27 CHN.PCM TZA.CON 136.05 UGA.FBE RWA.TRA 13.07
28 CHN.PCM TZA.PCM 135.90 KEN.AFF UGA.FBE 12.36
29 TZA.TRA EUU.TRA 131.02 UGA.FBE KEN.TEX 12.33
30 KEN.FBE SAS.PCM 129.12 KEN.PCM UGA.TRA 12.22
31 UGA.PCM MEA.PCM 129.11 KEN.PCM UGA.EGW 11.55
32 MEA.PCM KEN.CON 128.95 UGA.WAP KEN.WAP 11.23
33 EUU.PCM KEN.PCM 114.90 KEN.TEX UGA.TEX 10.58
34 CHN.PCM KEN.AFF 114.73 TZA.FBE KEN.FBE 10.45
35 SAS.PCM KEN.AFF 113.57 TZA.FBE KEN.PCM 10.21
36 EUU.PCM KEN.FBE 113.21 TZA.WAP KEN.FBE 9.72
37 CHN.ELM TZA.CON 113.02 UGA.FBE KEN.PCM 8.79
38 CHN.ELM UGA.EGW 112.73 UGA.WAP KEN.FBE 8.42
39 MEA.PCM TZA.CON 106.08 KEN.PCM RWA.FBE 8.40
40 KEN.FBE MEA.FBE 104.53 TZA.TEX KEN.TRA 8.00
41 CHN.MAN KEN.MAN 104.29 KEN.PCM BDI.AFF 8.00
42 UGA.FBE EUU.FBE 102.62 KEN.FBE UGA.TRA 7.66
43 TZA.AFF ASE.AFF 102.24 TZA.FBE KEN.TRA 7.18
44 CHN.ELM KEN.CON 100.89 TZA.AFF KEN.AFF 6.96
45 CHN.MPR TZA.ELM 98.29 KEN.PCM TZA.CON 6.93
46 KEN.FBE EUU.FBE 96.18 KEN.PCM TZA.TRA 6.83
47 TZA.PCM SSA.PCM 95.74 TZA.PCM BDI.AFF 6.80
48 CHN.ELM KEN.FBE 94.59 KEN.FBE UGA.PTE 6.49
49 SAS.PCM KEN.CON 93.59 KEN.PCM TZA.AFF 6.48
50 TZA.TRA SAS.TRA 91.13 KEN.WAP RWA.CON 6.43
42
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 127
Figu e B3: Re ined Koopman Wang Wei Decomposi ion o G oss Expo s
Sou ce: An `as & Cho (2022)
Figu e B4: KWW Decomposi ion o G oss Expo s
UGA
TZA
KEN
RWA
BDI
COD
EMERGING
EORA
2000
2005
2010
2015
2020
2000
2005
2010
2015
2020
2000
2005
2010
2015
2020
2000
2005
2010
2015
2020
2000
2005
2010
2015
2020
2000
2005
2010
2015
2020
2010
2012
2014
2016
2018
2010
2012
2014
2016
2018
2010
2012
2014
2016
2018
2010
2012
2014
2016
2018
2010
2012
2014
2016
2018
2010
2012
2014
2016
2018
0%
20%
40%
60%
80%
100%
0%
20%
40%
60%
80%
100%
VA Sha e in G oss Expo s
Componen
DAVAX
NDAVAX
REF
DDC
FVA
FDC
43
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 128
Table B2: (N)RCA Es ima es om Figu e 19
Coun y Sou ce Flow AFF MIN FBE TEX WAP PCM MPR ELM TEQ MAN EGW CON SMH TRA PTE FIB PAO
UGA WDR EORA GX 16.07 0.14 2.49 0.24 0.29 0.18 0.39 0.18 0.28 0.95 0.93 4.57 2.33 2.04 2.76 0.02 1.09
TZA WDR EORA GX 10.48 1.48 2.42 1.63 0.57 0.21 0.20 0.12 0.29 4.58 3.99 2.03 1.15 1.06 1.43 1.02 0.58
KEN WDR EORA GX 11.57 1.09 2.95 0.94 0.94 0.73 0.44 0.26 0.08 1.04 1.61 1.11 1.41 1.70 1.73 0.44 0.19
RWA WDR EORA GX 4.40 4.49 0.29 0.29 0.35 0.16 0.32 0.11 0.10 0.67 4.86 9.81 4.05 1.69 5.12 0.12 2.55
BDI WDR EORA GX 6.83 0.32 0.31 0.38 0.20 0.13 0.24 0.07 0.25 1.18 4.23 12.99 5.53 1.82 6.10 0.10 3.50
EAC5 WDR EORA GX 11.54 1.10 2.68 0.96 0.77 0.55 0.38 0.22 0.14 1.64 2.09 2.17 1.64 1.63 2.00 0.48 0.50
UGA EORA GX 17.33 0.11 2.83 0.22 0.36 0.19 0.39 0.16 0.35 0.79 1.25 3.98 2.05 1.81 2.69 0.02 1.35
TZA EORA GX 11.30 1.08 2.69 1.56 0.71 0.22 0.20 0.10 0.38 3.97 4.98 1.72 0.98 0.93 1.34 1.00 0.67
KEN EORA GX 11.95 0.79 3.41 0.89 1.20 0.77 0.44 0.23 0.11 0.92 2.20 0.95 1.24 1.56 1.73 0.44 0.25
RWA EORA GX 4.82 3.38 0.34 0.28 0.45 0.17 0.32 0.10 0.14 0.58 6.55 8.74 3.56 1.50 5.07 0.12 3.23
BDI EORA GX 7.43 0.24 0.36 0.35 0.26 0.13 0.25 0.07 0.34 0.98 5.49 11.58 4.86 1.62 6.05 0.10 4.40
EAC5 EORA GX 12.28 0.83 3.07 0.90 0.98 0.58 0.38 0.20 0.19 1.39 2.92 1.91 1.43 1.45 1.94 0.47 0.65
UGA EMERGING GX 4.76 0.00 7.16 0.52 0.70 0.82 0.46 0.03 0.05 0.14 7.29 0.19 4.42 1.92 0.03 0.16 0.12
TZA EMERGING GX 4.93 0.01 2.46 0.55 0.64 1.38 0.58 0.04 0.04 0.12 0.00 1.61 3.04 3.41 0.27 0.02 0.06
KEN EMERGING GX 4.16 0.01 5.98 1.01 0.94 0.55 0.47 0.06 0.10 0.33 1.17 0.00 2.17 3.28 0.29 0.13 6.78
RWA EMERGING GX 0.74 0.00 5.11 0.24 0.07 1.41 0.17 0.05 0.06 0.10 0.69 0.51 5.67 2.00 0.02 0.01 2.26
BDI EMERGING GX 0.16 0.00 10.83 0.31 0.06 0.95 0.22 0.04 0.08 0.07 0.00 0.01 0.02 1.30 0.12 0.01 24.16
EAC5 EMERGING GX 4.12 0.01 5.12 0.70 0.77 0.90 0.49 0.06 0.08 0.21 2.09 0.61 3.08 2.90 0.21 0.09 3.16
UGA BACI GX 5.03 0.08 7.60 0.88 0.73 0.68 0.80 0.18 0.20 0.24
TZA BACI GX 5.08 0.23 2.85 0.93 0.60 2.42 0.58 0.13 0.08 0.16
KEN BACI GX 5.74 0.45 6.61 1.63 0.89 0.77 0.59 0.17 0.12 0.40
RWA BACI GX 1.27 0.65 5.02 0.51 0.18 2.32 0.26 0.07 0.11 0.18
BDI BACI GX 0.25 0.02 5.53 0.42 0.06 2.84 0.30 0.07 0.09 0.11
EAC5 BACI GX 5.21 0.32 5.16 1.19 0.73 1.55 0.58 0.15 0.15 0.28
UGA WDR EORA VAX 13.87 0.11 2.24 0.20 0.25 0.16 0.33 0.15 0.24 0.82 0.78 4.02 1.94 1.79 2.37 0.02 3.42
TZA WDR EORA VAX 10.87 1.23 2.07 1.31 0.42 0.16 0.13 0.07 0.15 3.15 3.76 1.75 1.01 0.91 1.39 1.04 1.76
KEN WDR EORA VAX 10.62 0.84 2.63 0.91 0.84 0.72 0.29 0.19 0.06 0.98 1.52 0.86 1.22 1.42 1.53 0.38 0.64
RWA WDR EORA VAX 4.55 3.74 0.31 0.24 0.35 0.17 0.30 0.11 0.09 0.27 4.23 10.26 3.49 1.70 5.07 0.11 4.19
BDI WDR EORA VAX 6.36 0.19 0.31 0.30 0.20 0.13 0.23 0.08 0.26 0.82 3.41 12.58 4.41 1.72 5.43 0.09 9.87
EAC5 WDR EORA VAX 10.80 0.86 2.38 0.85 0.68 0.54 0.27 0.16 0.10 1.24 1.87 1.91 1.42 1.41 1.80 0.42 1.44
UGA EORA VAX 16.59 0.10 2.57 0.19 0.32 0.16 0.34 0.14 0.28 0.71 1.19 3.72 1.92 1.73 2.55 0.02 1.57
TZA EORA VAX 13.12 1.06 2.19 1.24 0.52 0.15 0.12 0.06 0.16 2.74 5.25 1.53 0.95 0.85 1.40 1.12 0.75
KEN EORA VAX 12.20 0.72 3.07 0.89 1.09 0.75 0.30 0.17 0.07 0.90 2.32 0.80 1.21 1.41 1.69 0.43 0.32
RWA EORA VAX 5.37 3.29 0.37 0.23 0.46 0.18 0.30 0.10 0.12 0.26 6.42 9.69 3.43 1.65 5.46 0.13 2.10
BDI EORA VAX 7.67 0.17 0.37 0.29 0.26 0.14 0.25 0.08 0.32 0.75 4.96 12.92 4.37 1.68 5.95 0.11 4.75
EAC5 EORA VAX 12.68 0.76 2.76 0.83 0.88 0.56 0.28 0.14 0.12 1.10 2.97 1.78 1.39 1.35 1.93 0.47 0.72
UGA EMERGING VAX 4.67 0.00 6.15 0.42 0.65 0.79 0.40 0.03 0.05 0.12 5.87 0.18 4.36 1.92 0.02 0.16 0.11
TZA EMERGING VAX 5.08 0.01 2.53 0.55 0.58 1.43 0.51 0.03 0.04 0.10 0.00 1.58 2.83 3.02 0.23 0.02 0.06
KEN EMERGING VAX 4.05 0.01 5.79 0.84 0.60 0.52 0.47 0.05 0.07 0.26 0.99 0.00 2.00 3.13 0.27 0.13 6.02
RWA EMERGING VAX 0.77 0.00 4.50 0.26 0.08 1.56 0.18 0.06 0.08 0.11 0.79 0.52 4.64 2.06 0.02 0.01 2.30
BDI EMERGING VAX 0.16 0.00 9.67 0.34 0.06 0.69 0.18 0.04 0.07 0.09 0.00 0.01 0.02 1.47 0.13 0.01 26.19
EAC5 EMERGING VAX 4.17 0.01 5.01 0.61 0.58 0.89 0.43 0.05 0.08 0.18 1.73 0.58 2.85 2.77 0.19 0.09 2.91
Table B3: (New) Re ealed Compa a i e Ad an age: Co ela ions o 2010-19 Medians
WDR EORA EORA EMERGING BACI
GX VAX GX VAX GX VAX GX
WDR EORA GX 1 .962 .990 .987 .179 .186 .550
WDR EORA VAX .962 1 .960 .967 .341 .362 .563
EORA GX .990 .960 1 .995 .215 .223 .571
EORA VAX .987 .967 .995 1 .217 .227 .570
EMERGING GX .179 .341 .215 .217 1 .994 .925
EMERGING VAX .186 .362 .223 .227 .994 1 .934
BACI GX .550 .563 .571 .570 .925 .934 1
No es: WDR EORA is only a ailable o 2010-15, EMERGING misses yea s 2011-14.
44
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 129
Figu e B5: (N)RCA in 2006-2010 and 2015-2019 (Medians)
O e all
Rela i e o EAC
In Inne −EAC T ade
UGA
TZA
KEN
RWA
BDI
EAC5
0.03
0.05
0.1
0.3
0.5
1
3
5
10
30
0.03
0.05
0.1
0.3
0.5
1
3
5
10
30
0.03
0.05
0.1
0.3
0.5
1
3
5
10
30
PAO
FIB
PTE
TRA
SMH
CON
EGW
MAN
TEQ
ELM
MPR
PCM
WAP
TEX
FBE
MIN
AFF
PAO
FIB
PTE
TRA
SMH
CON
EGW
MAN
TEQ
ELM
MPR
PCM
WAP
TEX
FBE
MIN
AFF
PAO
FIB
PTE
TRA
SMH
CON
EGW
MAN
TEQ
ELM
MPR
PCM
WAP
TEX
FBE
MIN
AFF
PAO
FIB
PTE
TRA
SMH
CON
EGW
MAN
TEQ
ELM
MPR
PCM
WAP
TEX
FBE
MIN
AFF
PAO
FIB
PTE
TRA
SMH
CON
EGW
MAN
TEQ
ELM
MPR
PCM
WAP
TEX
FBE
MIN
AFF
PAO
FIB
PTE
TRA
SMH
CON
EGW
MAN
TEQ
ELM
MPR
PCM
WAP
TEX
FBE
MIN
AFF
(New) Re ealed Compa a i e Ad an age
Sec o
Sou ce: EMERGING BACI Flow: 2006−2010 2015−2019
45
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 130

Table B4: (N)RCA Es ima es om Figu e 20
Coun y Sou ce Flow AFF MIN FBE TEX WAP PCM MPR ELM TEQ MAN EGW CON SMH TRA PTE FIB PAO
Rela i e o he EAC
UGA WDR EORA GX 1.39 0.13 0.93 0.26 0.38 0.33 1.02 0.81 1.95 0.58 0.43 2.12 1.41 1.25 1.37 0.05 2.17
TZA WDR EORA GX 0.91 1.32 0.91 1.71 0.74 0.38 0.52 0.54 2.04 2.80 1.90 0.93 0.70 0.65 0.71 2.12 1.16
KEN WDR EORA GX 1.00 0.98 1.10 0.99 1.22 1.32 1.14 1.19 0.56 0.64 0.77 0.51 0.87 1.04 0.88 0.92 0.38
RWA WDR EORA GX 0.39 4.06 0.11 0.30 0.45 0.29 0.84 0.50 0.69 0.40 2.35 4.57 2.47 1.04 2.56 0.24 5.20
BDI WDR EORA GX 0.60 0.29 0.12 0.39 0.26 0.23 0.64 0.34 1.73 0.71 2.01 6.05 3.38 1.12 3.07 0.21 7.14
UGA EORA GX 1.34 0.13 0.91 0.26 0.39 0.32 1.03 0.80 1.88 0.57 0.45 2.06 1.40 1.24 1.36 0.05 2.10
TZA EORA GX 0.92 1.25 0.87 1.68 0.72 0.37 0.50 0.52 2.02 2.76 1.83 0.90 0.68 0.64 0.69 2.12 1.11
KEN EORA GX 0.99 1.00 1.12 0.99 1.23 1.33 1.15 1.20 0.58 0.64 0.79 0.54 0.88 1.05 0.89 0.94 0.40
RWA EORA GX 0.40 3.96 0.11 0.31 0.46 0.30 0.86 0.50 0.72 0.41 2.24 4.48 2.46 1.01 2.55 0.24 5.02
BDI EORA GX 0.59 0.28 0.12 0.40 0.27 0.23 0.65 0.34 1.78 0.73 1.94 6.26 3.42 1.09 3.03 0.22 6.88
UGA EMERGING GX 1.02 0.07 1.43 0.74 0.82 0.82 0.85 0.47 0.70 0.51 4.06 0.31 1.40 0.65 0.13 1.71 0.04
TZA EMERGING GX 1.12 1.83 0.56 0.80 1.08 1.48 1.14 0.73 0.50 0.59 0.00 2.71 0.97 1.17 1.27 0.22 0.02
KEN EMERGING GX 1.04 0.81 1.18 1.46 1.08 0.63 1.02 1.30 1.34 1.52 0.54 0.00 0.71 1.11 1.36 1.42 2.14
RWA EMERGING GX 0.18 0.17 1.02 0.31 0.10 1.34 0.34 0.77 0.70 0.30 0.33 0.81 1.88 0.69 0.07 0.14 0.74
BDI EMERGING GX 0.04 0.13 2.08 0.42 0.07 1.03 0.44 0.72 0.95 0.38 0.00 0.02 0.01 0.46 0.51 0.14 7.64
UGA BACI GX 0.97 0.29 1.43 0.78 0.96 0.44 1.21 1.14 1.87 0.89
TZA BACI GX 0.97 0.65 0.53 0.75 0.87 1.57 0.97 0.83 0.51 0.63
KEN BACI GX 1.13 1.49 1.28 1.42 1.28 0.48 0.99 1.16 0.98 1.43
RWA BACI GX 0.25 1.95 1.00 0.42 0.24 1.44 0.48 0.52 0.89 0.67
BDI BACI GX 0.05 0.09 1.17 0.35 0.07 1.70 0.43 0.56 0.78 0.39
UGA WDR EORA VAX 1.28 0.13 0.94 0.24 0.37 0.29 1.22 0.97 2.38 0.65 0.41 2.13 1.35 1.27 1.30 0.04 2.28
TZA WDR EORA VAX 1.01 1.43 0.87 1.51 0.61 0.29 0.45 0.43 1.48 2.51 2.02 0.92 0.70 0.64 0.77 2.48 1.23
KEN WDR EORA VAX 0.98 0.98 1.10 1.07 1.24 1.32 1.08 1.16 0.60 0.78 0.81 0.46 0.87 1.02 0.86 0.90 0.44
RWA WDR EORA VAX 0.42 4.33 0.13 0.28 0.51 0.32 1.10 0.69 0.93 0.21 2.29 5.48 2.46 1.21 2.81 0.27 2.75
BDI WDR EORA VAX 0.60 0.22 0.13 0.35 0.29 0.25 0.85 0.50 2.57 0.65 1.82 6.74 3.11 1.22 3.03 0.22 6.76
UGA EORA VAX 1.28 0.13 0.92 0.23 0.38 0.29 1.16 0.93 2.16 0.61 0.42 2.06 1.34 1.26 1.29 0.05 2.21
TZA EORA VAX 1.03 1.36 0.79 1.49 0.59 0.27 0.45 0.41 1.31 2.46 1.93 0.86 0.68 0.63 0.73 2.35 1.12
KEN EORA VAX 0.97 1.00 1.12 1.09 1.25 1.33 1.10 1.17 0.61 0.80 0.84 0.48 0.89 1.02 0.87 0.92 0.47
RWA EORA VAX 0.44 4.28 0.13 0.29 0.52 0.32 1.05 0.68 0.95 0.23 2.27 5.30 2.43 1.20 2.77 0.27 2.75
BDI EORA VAX 0.62 0.22 0.13 0.33 0.29 0.25 0.87 0.51 2.64 0.65 1.74 6.94 3.19 1.21 3.13 0.23 6.76
UGA EMERGING VAX 1.05 0.06 1.26 0.71 1.09 0.80 0.80 0.66 0.87 0.53 3.98 0.31 1.50 0.67 0.13 1.73 0.04
TZA EMERGING VAX 1.15 1.89 0.60 0.87 1.21 1.53 1.12 0.71 0.57 0.62 0.00 2.86 0.97 1.09 1.15 0.21 0.02
KEN EMERGING VAX 1.00 0.80 1.24 1.37 0.91 0.58 1.00 1.19 1.07 1.43 0.52 0.00 0.70 1.13 1.41 1.37 2.05
RWA EMERGING VAX 0.19 0.19 0.93 0.37 0.15 1.60 0.41 1.03 0.88 0.42 0.44 0.88 1.63 0.75 0.09 0.15 0.80
BDI EMERGING VAX 0.04 0.11 2.06 0.53 0.10 0.75 0.41 0.76 0.86 0.57 0.00 0.03 0.01 0.54 0.63 0.15 8.59
In Inne -EAC T ade
UGA EORA GX 5.74 0.02 0.89 0.18 0.21 0.25 0.41 0.33 1.26 0.47 0.07 0.94 0.71 0.85 0.64 0.00 2.13
TZA EORA GX 1.14 0.37 3.74 0.92 0.49 0.38 0.24 0.71 1.49 2.81 1.62 0.98 0.63 0.85 0.90 2.88 2.64
KEN EORA GX 0.13 1.22 0.86 1.15 1.18 1.18 1.15 1.14 0.93 0.98 1.11 0.94 1.06 1.03 1.06 1.07 0.66
RWA EORA GX 1.64 0.33 0.66 0.43 0.24 0.36 0.43 0.38 0.75 0.54 7.71 15.20 3.40 1.41 3.10 0.45 15.32
BDI EORA GX 1.09 0.23 0.35 0.42 0.13 0.12 0.33 0.18 1.83 1.13 7.06 27.72 4.85 0.73 4.00 0.44 24.83
UGA EMERGING GX 2.04 0.01 1.61 0.51 1.18 0.53 0.95 0.16 0.17 0.33 2.78 0.00 0.40 0.85 0.98 2.47 0.06
TZA EMERGING GX 1.47 2.92 0.61 1.90 1.38 0.75 0.48 0.84 0.29 0.19 4.11 0.64 1.34 1.87 0.09 0.01
KEN EMERGING GX 0.28 0.70 0.72 0.99 0.93 1.52 1.34 1.37 1.79 1.63 0.47 0.00 1.60 1.01 0.74 0.64 0.56
RWA EMERGING GX 0.87 0.64 2.00 1.38 0.06 0.49 0.19 0.88 0.53 0.46 0.51 0.00 0.84 0.25 0.02 0.05 13.67
BDI EMERGING GX 0.24 0.15 2.11 0.99 0.12 0.57 0.65 0.95 0.84 0.60 0.00 0.00 0.05 0.94 1.26 0.20 21.95
UGA BACI GX 1.67 0.52 1.70 0.44 0.89 0.66 1.04 0.70 0.55 0.28
TZA BACI GX 2.07 0.94 0.58 1.83 1.36 0.84 0.50 0.59 0.70 0.63
KEN BACI GX 0.24 1.10 0.76 0.92 0.97 1.32 1.23 1.25 1.31 1.48
RWA BACI GX 0.50 1.07 2.08 1.96 0.11 0.21 0.58 0.73 1.04 0.55
BDI BACI GX 0.44 0.03 1.24 0.97 0.15 0.61 1.33 0.98 0.95 0.46
UGA EORA VAX 4.92 0.02 0.85 0.15 0.18 0.21 0.46 0.37 1.28 0.42 0.06 0.93 0.63 0.81 0.57 0.00 1.88
TZA EORA VAX 1.20 0.39 3.80 0.86 0.43 0.29 0.23 0.60 1.17 2.32 1.72 1.09 0.62 0.86 0.96 3.27 2.42
KEN EORA VAX 0.14 1.25 0.91 1.19 1.21 1.21 1.16 1.16 0.94 1.05 1.15 0.93 1.08 1.04 1.08 1.09 0.71
RWA EORA VAX 1.64 0.32 0.72 0.34 0.24 0.36 0.52 0.49 0.83 0.17 6.53 18.05 3.11 1.53 3.15 0.47 6.67
BDI EORA VAX 1.47 0.18 0.39 0.33 0.14 0.13 0.42 0.27 2.31 0.88 5.84 31.37 3.95 0.78 3.89 0.46 19.08
UGA EMERGING VAX 1.97 0.01 1.54 0.48 1.36 0.51 0.90 0.20 0.22 0.32 2.68 0.00 0.41 0.85 1.01 2.35 0.05
TZA EMERGING VAX 1.42 2.99 0.62 1.43 1.33 0.75 0.42 0.80 0.28 0.22 4.39 0.63 1.22 1.62 0.09 0.01
KEN EMERGING VAX 0.26 0.70 0.78 1.12 0.82 1.54 1.39 1.21 1.68 1.63 0.47 0.00 1.62 1.04 0.82 0.65 0.53
RWA EMERGING VAX 0.96 0.77 1.50 1.73 0.09 0.57 0.26 1.36 0.72 0.60 0.76 0.00 0.81 0.31 0.03 0.05 15.93
BDI EMERGING VAX 0.22 0.12 1.96 1.06 0.15 0.57 0.56 1.16 0.77 0.76 0.00 0.00 0.05 1.09 1.59 0.19 23.21
46
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 131
Table B5: (N)RCA Es ima es om Figu es B5 and 21
Coun y Sou ce Flow Pe iod AFF MIN FBE TEX WAP PCM MPR ELM TEQ MAN EGW CON SMH TRA PTE FIB PAO
Rela i e o he EAC
UGA EM VAX 2006-2010 1.06 0.09 1.41 0.68 0.76 0.64 0.51 1.30 0.79 0.25 4.02 0.00 1.71 0.83 0.11 2.12 0.03
UGA EM VAX 2015-2019 1.04 0.05 1.22 0.73 1.14 0.82 1.09 0.53 0.94 0.73 3.94 0.36 1.46 0.65 0.13 1.66 0.04
UGA EM VAX G ow h Ra e -1.39 -46.65 -13.86 6.01 50.98 27.69 112.59 -59.19 18.23 196.66 -2.04 In -15.04 -21.60 23.52 -21.53 35.46
TZA EM VAX 2006-2010 1.02 1.14 0.64 1.21 1.30 1.77 1.95 0.86 0.73 0.82 0.00 3.14 0.86 0.92 1.09 0.20 0.02
TZA EM VAX 2015-2019 1.21 2.01 0.60 0.87 1.14 1.50 0.68 0.56 0.41 0.61 0.00 2.79 0.98 1.10 1.18 0.22 0.02
TZA EM VAX G ow h Ra e 18.19 75.99 -6.40 -27.84 -12.20 -15.45 -65.06 -35.41 -43.39 -24.94 -11.18 13.80 20.31 8.45 12.72 -19.63
KEN EM VAX 2006-2010 1.07 1.39 1.08 1.05 0.94 0.56 0.58 1.03 1.36 1.54 0.52 0.00 0.71 1.13 1.22 1.21 1.94
KEN EM VAX 2015-2019 0.94 0.67 1.27 1.39 0.89 0.61 1.07 1.34 0.77 1.43 0.50 0.00 0.68 1.12 1.42 1.41 2.09
KEN EM VAX G ow h Ra e -11.93 -51.73 17.33 31.73 -5.04 7.90 83.87 29.57 -43.19 -7.11 -3.87 -46.04 -4.20 -0.80 16.61 16.47 7.66
RWA EM VAX 2006-2010 0.04 0.01 0.83 0.31 0.67 1.55 0.59 0.49 0.24 0.18 0.37 0.04 2.13 1.10 2.10 0.05 1.17
RWA EM VAX 2015-2019 0.21 0.19 0.96 0.37 0.12 1.65 0.39 1.07 0.93 0.49 0.48 0.90 1.56 0.72 0.05 0.18 0.73
RWA EM VAX G ow h Ra e 443.79 1341.66 16.12 19.09 -82.12 6.35 -34.65 120.96 296.51 177.25 29.12 2082.03 -26.75 -34.81 -97.83 251.07 -37.38
BDI EM VAX 2006-2010 0.05 0.00 1.91 0.71 0.13 0.48 0.14 0.38 0.49 0.32 0.00 0.00 0.02 0.55 0.56 0.23 7.95
BDI EM VAX 2015-2019 0.04 0.00 2.20 0.43 0.09 0.87 0.48 0.95 1.24 0.58 0.00 0.05 0.01 0.53 0.66 0.14 9.05
BDI EM VAX G ow h Ra e -28.04 15.16 -39.62 -30.48 80.02 252.01 148.31 155.41 80.37 -100.00 1936.67 -59.84 -3.48 17.94 -39.97 13.92
UGA BACI GX 2006-2010 1.07 0.29 1.37 0.74 0.60 0.49 1.13 1.16 1.86 0.64
UGA BACI GX 2015-2019 0.95 0.27 1.26 0.69 0.93 0.93 0.78 0.70 1.32 0.74
UGA BACI GX G ow h Ra e -11.13 -5.31 -8.41 -6.84 54.78 88.34 -31.09 -39.41 -28.98 16.22
TZA BACI GX 2006-2010 1.01 0.89 0.53 0.95 0.95 1.68 0.89 0.90 0.64 0.37
TZA BACI GX 2015-2019 1.09 0.67 0.48 0.74 0.94 1.43 1.23 0.74 0.50 0.49
TZA BACI GX G ow h Ra e 8.17 -25.00 -9.91 -22.37 -0.75 -14.85 38.13 -17.42 -22.21 32.79
KEN BACI GX 2006-2010 1.06 1.35 1.17 1.18 1.22 0.68 1.11 1.06 0.82 1.30
KEN BACI GX 2015-2019 1.10 1.42 1.38 1.49 1.26 0.51 1.03 1.37 1.21 1.62
KEN BACI GX G ow h Ra e 3.24 5.28 18.32 25.93 3.24 -26.02 -7.38 29.15 48.27 24.73
RWA BACI GX 2006-2010 0.17 0.28 1.30 0.26 0.20 1.53 0.30 0.80 0.76 0.56
RWA BACI GX 2015-2019 0.26 2.00 0.96 0.41 0.26 1.52 0.35 0.53 0.76 0.77
RWA BACI GX G ow h Ra e 57.96 607.63 -26.33 54.71 29.28 -0.60 15.23 -33.97 0.35 37.42
BDI BACI GX 2006-2010 0.08 0.50 1.57 0.71 0.14 0.97 0.38 1.55 1.92 0.42
BDI BACI GX 2015-2019 0.03 0.23 1.24 0.28 0.07 1.59 0.62 0.58 0.68 0.44
BDI BACI GX G ow h Ra e -55.95 -53.77 -20.90 -60.08 -46.96 64.66 63.99 -62.93 -64.74 5.04
In Inne -EAC T ade
UGA EM VAX 2006-2010 1.94 0.05 1.52 0.50 0.82 0.60 0.94 1.24 0.78 0.29 2.52 0.00 0.30 0.59 0.68 2.34 0.06
UGA EM VAX 2015-2019 2.00 0.01 1.57 0.45 1.50 0.43 0.86 0.19 0.19 0.36 2.84 0.00 0.41 0.87 1.03 2.36 0.04
UGA EM VAX G ow h Ra e 3.49 -90.65 3.57 -10.48 83.04 -28.89 -9.28 -84.33 -75.23 24.96 12.66 In 36.54 47.02 51.20 0.86 -21.50
TZA EM VAX 2006-2010 1.73 1.93 0.61 2.50 1.83 0.81 0.50 0.62 0.26 1.56 0.00 0.00 0.61 1.21 1.99 0.11 0.05
TZA EM VAX 2015-2019 1.11 3.15 0.63 1.13 1.30 0.69 0.33 0.98 0.30 0.21 0.00 4.39 0.66 1.22 1.41 0.08 0.01
TZA EM VAX G ow h Ra e -35.87 63.25 3.48 -54.80 -29.22 -15.67 -33.43 59.41 14.99 -86.53 In 7.54 0.41 -29.39 -27.59 -84.28
KEN EM VAX 2006-2010 0.18 1.26 0.82 0.65 0.83 1.34 1.19 0.99 1.42 1.24 0.47 0.72 1.56 1.17 0.81 0.56 0.56
KEN EM VAX 2015-2019 0.29 0.65 0.76 1.14 0.82 1.56 1.41 1.42 1.76 1.71 0.45 0.00 1.67 1.00 0.82 0.71 0.51
KEN EM VAX G ow h Ra e 67.56 -48.48 -7.78 74.19 -0.82 16.84 18.37 43.50 24.12 37.23 -4.84 -100.00 6.69 -14.63 1.69 26.88 -8.42
RWA EM VAX 2006-2010 0.64 0.00 1.23 3.12 0.28 0.33 1.95 1.25 0.67 0.33 0.95 0.00 1.33 0.74 0.93 0.09 30.72
RWA EM VAX 2015-2019 1.11 0.43 1.69 1.72 0.08 0.78 0.24 1.46 0.78 0.69 0.56 0.00 0.77 0.28 0.03 0.04 15.14
RWA EM VAX G ow h Ra e 75.06 In 36.84 -44.80 -69.86 136.03 -87.79 16.94 16.78 108.43 -40.66 In -41.92 -61.31 -97.23 -58.24 -50.72
BDI EM VAX 2006-2010 0.57 0.00 1.51 2.93 0.16 0.31 1.25 0.65 1.06 0.79 0.00 167.36 0.02 1.75 2.70 0.24 54.71
BDI EM VAX 2015-2019 0.22 0.00 2.10 0.78 0.13 0.65 0.56 1.17 0.62 0.72 0.00 0.00 0.06 0.86 1.25 0.13 16.18
BDI EM VAX G ow h Ra e -61.82 38.60 -73.45 -18.47 106.52 -55.19 80.69 -41.52 -9.43 -100.00 -100.00 150.84 -50.61 -53.85 -44.31 -70.42
UGA BACI GX 2006-2010 1.97 0.45 1.70 0.65 0.51 0.61 1.25 0.91 0.51 0.21
UGA BACI GX 2015-2019 1.77 0.98 1.61 0.42 1.07 0.47 1.06 0.47 0.35 0.43
UGA BACI GX G ow h Ra e -9.81 118.48 -5.50 -35.71 109.45 -22.41 -14.85 -48.72 -30.42 104.38
TZA BACI GX 2006-2010 2.42 0.51 0.74 1.68 1.67 0.93 0.38 0.90 1.10 0.34
TZA BACI GX 2015-2019 1.77 0.90 0.56 1.83 1.42 0.88 0.63 0.56 0.67 0.26
TZA BACI GX G ow h Ra e -26.89 77.56 -23.87 9.19 -15.04 -6.17 67.01 -38.13 -39.34 -22.19
KEN BACI GX 2006-2010 0.23 1.44 0.74 0.85 1.03 1.23 1.11 1.09 1.07 1.33
KEN BACI GX 2015-2019 0.29 1.19 0.79 0.95 0.85 1.40 1.23 1.36 1.35 1.54
KEN BACI GX G ow h Ra e 27.48 -17.40 5.71 11.44 -17.87 14.15 10.99 24.65 26.66 15.57
RWA BACI GX 2006-2010 1.07 0.20 2.95 0.36 0.15 0.20 0.29 1.10 1.16 0.16
RWA BACI GX 2015-2019 0.41 0.99 2.01 2.02 0.11 0.25 0.55 0.88 1.12 0.64
RWA BACI GX G ow h Ra e -62.09 405.49 -32.05 456.91 -28.76 22.00 89.77 -19.51 -3.06 293.22
BDI BACI GX 2006-2010 0.51 0.00 1.34 2.10 0.04 0.51 0.93 1.45 1.83 0.73
BDI BACI GX 2015-2019 0.34 0.17 1.04 0.99 0.24 0.95 1.32 1.55 0.62 0.57
BDI BACI GX G ow h Ra e -32.71 5411.76 -22.22 -52.85 434.09 84.86 41.59 7.56 -66.14 -21.93
47
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 132
Figu e B6: ESCAP Bila e al T ade Cos Measu e o he EAC5
BDI
KEN
RWA
TZA
UGA
Raw
3−Yea MA + LOCF
2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020
100
150
200
250
300
100
150
200
250
300
Ta i Equi alen To al T ade Cos (Pe cen )
Pa ne : BDI KEN RWA TZA UGA
Table B6: Ze o-S age Reg essions: G oss T ade
Dependen Va iable: G oss Expo s Final Goods Expo s
Da a: EORA EMERGING EORA EMERGING
log(τoiu ) 6.454∗∗∗ 1.245∗∗∗ 5.312∗∗∗ 1.023∗∗∗
(0.3439) (0.0792) (0.3959) (0.0695)
Obse a ions 155,584 208,635 155,584 208,519
R20.5971 0.4951 0.6306 0.4915
Wi hin R20.0225 0.0072 0.0188 0.0073
Fixed-e ec s
# coun y-sec o 442 2,202 442 2,201
# coun y-yea 374 102 374 102
# sec o -yea 572 780 572 780
Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
Table B7: Ze o-S age Reg essions: Value Added T ade
Dependen Va iable: log( beoiuj +1)
Da a: EORA EMERGING EORA EMERGING
log(τou ×δoiuj) -2.774∗∗∗ -0.1234∗∗∗ -1.820∗∗∗ -0.1959∗∗∗
(0.0717) (0.0049) (0.0300) (0.0062)
R20.6279 0.2629 0.4301 0.1906
Wi hin R20.3419 0.0395 0.0303 0.0451
log(τou ×δij) -3.018∗∗∗ -0.1484∗∗∗ -3.112∗∗∗ -0.2373∗∗∗
(0.0772) (0.0060) (0.0560) (0.0072)
R20.6241 0.2573 0.4402 0.1636
Wi hin R20.3352 0.0322 0.0475 0.0132
Fixed-e ec s Using Coun y Sou ce Coun y
# coun y-sec o 441 2,202 440 2,202
# coun y-yea 374 102 374 102
# sec o -yea 572 780 572 780
Obse a ions 3,928,956 27,381,322 3,928,956 27,381,322
Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
48
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 133
Table B8: GVC Pa icipa ion EAC5 Reg essions: Fi s S ages
Da a: EORA21 (2000-2021) WDR EORA15 (2000-2015)
Model: IV-δij IV-δoiuj 2SLS IV-δij IV-δoiuj 2SLS
Dependen Va iable: log(VS)
Va iables
log( ˆ
VSδij ) -0.9737 -24.23∗-2.472∗∗∗ -9.525
(0.7575) (12.41) (0.3597) (6.322)
log( ˆ
VSδoiuj ) -1.054 25.82∗-2.735∗∗∗ 7.846
(0.8474) (14.56) (0.3985) (7.331)
Fi s a is ics
Obse a ions 2,740 2,740 2,740 2,023 2,023 2,023
R20.9868 0.9868 0.9870 0.9914 0.9914 0.9914
Wi hin R20.0314 0.0299 0.0493 0.1952 0.1932 0.1976
Kleibe gen-Paap, F-s a . 1.652 1.547 32.23 47.22 47.07 53.78
Wald, p- alue 0.1988 0.2136 <0.001 <0.001 <0.001 <0.001
Dependen Va iable: log(E2R)
Va iables
log( ˆ
E2Rδij ) -0.1017 0.1118 -0.2892∗-0.7687∗∗
(0.1531) (0.5633) (0.1564) (0.3569)
log( ˆ
E2Rδoiuj ) -0.0529 -0.1306 -0.2005∗0.2822
(0.1334) (0.3157) (0.1003) (0.1743)
Fi s a is ics
Obse a ions 2,734 2,733 2,733 2,017 2,016 2,016
R20.9768 0.9767 0.9767 0.9879 0.9878 0.9879
Wi hin R20.0015 0.0006 0.0007 0.0202 0.0163 0.0278
Kleibe gen-Paap, F-s a . 0.4409 0.1569 0.2861 3.421 3.989 4.301
Wald, p- alue 0.5067 0.6920 0.7512 0.0645 0.0459 0.0137
Fixed-e ec s
# coun y-sec o 130 130 130 129 129 129
# coun y-yea 110 110 110 80 80 80
# sec o -yea 572 572 572 416 416 416
D iscoll-K aay (L=2) s anda d-e o s in pa en heses
Signi . Codes: ***: 0.01, **: 0.05, *: 0.1
49
CHAPTER 3.2. GLOBAL AND REGIONAL INTEGRATION IN THE EAC Page 134
Table 1: A ica In as uc u e Da abase: Places Da ase by Sou ce
Sou ce Coun o which Polygons Ca ego ies
Open S ee Map (OSM) 12,221,198 9,038,206 45
OpenCellid (Cell Towe s) 1,894,356 0 1
O e u e Maps Places (con idence >0.4) 823,786 0 44
All The Places (Open Web-Sc aped POIs) 114,382 0 12
Heal h Facili ies in SSA (Na u e Scien i ic Da a) 96,290 0 1
Global In eg a ed Powe T acke 871 0 1
WRI Global Powe Plan s Da abase 363 0 1
Global S eel Plan T acke 41 0 1
Open Zone Map (Special Economic Zones) 387 0 1
Wo ld Po Index 235 0 1
Wo ldBank Global In e na ional Po s 9 0 1
SUM 15,151,918 9,038,206 47
No es: Table shows places o in e es (POIs) da a collec ed om di e en sou ces. In OSM POIs may be agged buildings/ha e geome ies.
economic ca ego ies and 26 simpli ied ones. The ull classi ica ion p ocess is de ailed in Appendix
A. Appendix Table A3 shows he inal ha monized classi ica ion.
Following ha moniza ion, I also deduplica e he da a ac oss sou ces by allowing only ea u es o
he same ca ego y om one sou ce wi hin a 10m adius.5Cu a ed da ase s he eby ake p ecedence
o e OSM, which in u n supe sedes OVP. Tables 1and A3 summa ize al eady deduplica ed da a.
In addi ion o POIs, I ex ac lines ing ea u es such as (non- esiden ial) oads, la ge wa e ways,
powe lines, ailways, ae oways, pipelines, and elecommunica ion lines om OSM. I complemen
OSM powe lines wi h elec ici y g id maps om he Eu opean Commission (Kakoulaki & Mone -
Gi ona,2020) and he Wo ld Bank. Table 2p o ides a b eakdown. In To al, I collec ∼4.4 million
km o ne wo k in as uc u e, o which ∼1.6 million km a e oads, ∼1.5 million km a e wa e ways,
and 967 housand km a e powe lines. The o he ca ego ies sum o 272 housand km.
Table 2: A ica In as uc u e Da abase: Lines Da a by Ca ego y
Ca ego y Coun Leng h (Km)
oad 763,912 1,621,144
wa e way 359,756 1,507,112
powe 11,013,150 967,317
ailway 84,707 128,408
ae oway 27,360 11,019
pipeline 9,453 55,394
s o age 8,551 389
e y 2,412 48,259
ae ialway 171 175
elecom 87 28,682
SUM 2,269,903 4,379,392
1OSM powe lines we e combined wi h da ase s om he EC’s
Join Resea ch Cen e (JRC) and he Wo ld Bank.
Las ly, I also ob ain 2022 ixed and mobile download and upload speeds om OOKLA ia he
EU A ica Knowledge Pla o m - wi hin map iles a a esolu ion o a ound 610m a he equa o .
2.2 Spa ial Measu es o Weal h and Economic Ac i i y
To s udy he e u ns o in as uc u e, accu a e spa ial measu es o quan i ies o in e es such
as economic ac i i y/ alue-added o household weal h a e needed. A popula spa ial p oxy o
economic ac i i y, ollowing he seminal wo k o V. Hende son e al. (2011,2012), is emo ely sensed
nigh ligh luminosi y (Donaldson & S o eyga d,2016). Since 2011, nigh ligh s da a is a ailable
a high esolu ion (15 a c seconds o ∼500m a he equa o ) om he Visible In a ed Imaging
5This is done by shi ing a 10m g id o e he POI ea u es in s eps o 1m and deduplica ing ea u es wi hin each
10m ×10m squa e, hus he e is some pa h dependence in e ms o which POIs a e compa ed i s .
4
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 141

Radiome e Sui e (VIIRS) onboa d he Suomi sa elli e (Gibson e al.,2020). Recen ly, NASA’s
’Black Ma ble’ p oduc o e s a mo e p ocessed e sion o he VIIRS image y o moni o ing
human ac i i ies (Rom´an e al.,2018). Ea ly adop e s Peng & Chen (2021) show ha his da a is
subs an ially mo e accu a e han he con en ional VIIRS da a in acking Zambian GDP o e ime.
Se e al con ibu ions also spa ially dis ibu e GDP ( alue-added) om na ional and/o egional
accoun s ia high- esolu ion da a on popula ion, geophysical ea u es, and nigh ligh s. The widely
used G-Econ da abase (No dhaus e al.,2006) p o ides global GDP es ima es o 1-deg ee g id cells
om 1990 o 2005. Mo e ecen ly, Kummu e al. (2018) dis ibu e na ional GDP in cons an 2015
PPP dolla s a 5 a c-min (0.0833 deg ees o 9.3km a he equa o ) esolu ion, using subna ional
alue-added es ima es om Gennaioli e al. (2013) and popula ion om he HYDE 3.2 da abase.
Recen e o s, s a ing wi h Jean e al. (2016), also use iche da a sou ces o p edic weal h
a high spa ial esolu ions. No ably, Chi e al. (2022) combine as and he e ogeneous da a om
sa elli es, mobile phone ne wo ks, opog aphic maps, as well as connec i i y da a om Me a o
es ima e na ionally compa able es ima es o weal h - a Rela i e Weal h Index (RWI) - o all low
and middle-income coun ies a 2.4km esolu ion. Focussing on SSA, Lee & B ai hwai e (2022)
de elop a c oss-coun y p edic ion me hodology combining day- and nigh ime sa elli e image y,
high- esolu ion popula ion es ima es, and OSM o p edic he In e na ional Weal h Index (IWI)
- a compa able asse -based weal h index calcula ed om DHS Su eys o 25 SSA coun ies since
2017 - o 929,295 popula ed places in 44 SSA coun ies a 1-squa e mile esolu ion.6They ob ain
a c oss-coun y R2o 91.7% o he IWI, ou pe o ming all p e ious esea ch esul s.
Appendix Figu e A1 shows he weal h/ac i i y es ima es by Rom´an e al. (2018), Kummu e al.
(2018), Chi e al. (2022), and Lee & B ai hwai e (2022). None o hese measu es is ideal o s udy he
e u ns o in as uc u e. Nigh ligh s a e, by de ini ion, co ela ed wi h powe in as uc u e and
also ela i ely spa se since e y low-ligh a eas a e se o 0 in he Black Ma ble p oduc . G idded
GDP is, by de ini ion, highly co ela ed wi h popula ion and may hus be biased owa ds esiden ial
a eas. The RWI is no cons uc ed o be compa able ac oss coun ies and is no a ailable o (Sou h-
)Sudan, whe eas he IWI is no a ailable o No h A ica and uses pa s o OSM and popula ion
in i s cons uc ion. In he ollowing, I use all 4 es ima es shown in Figu e A1 o de e mine weigh s
applied du ing he agg ega ion o g anula da a, bu ocus on he IWI o inal es ima ion since i
is an accu a e high- esolu ion and c oss-coun y compa able es ima e. I also conduc obus ness
exe cises wi h nigh ligh s and g ound u h IWI es ima es om DHS su eys conduc ed since 2010
o ensu e ha key esul s a e no d i en by he ML model o Lee & B ai hwai e (2022).
2.3 Co a ia e Ras e Laye s
Many u he sou ces o high- esolu ion da a laye s abou geophysical ea u es, ag icul u e, clima e,
and con lic could be included as co a ia es in an analysis o in as uc u e and weal h/economic
ac i i y. Bu economic esea ch such as S o eyga d (2016), Jedwab & S o eyga d (2022), Donaldson
(2018) and Peng & Chen (2021) has ocussed on wo pa icula ly impo an dimensions o spa ial
a ia ion a ec ing economic ou comes: popula ion (u baniza ion) and ma ke access.
I ob ain popula ion es ima es o 2020 om he G idded Popula ion o he Wo ld Ve sion
4 (GPW4) p ojec (CIESIN,2016), which is based on adminis a i e da a. I b oadly dis inguish
be ween he in an (0-14 yea s) and wo king-age (15-49 yea s) popula ion o allow o local a ia ion
in demog aphic cha ac e is ics. To app oxima e ma ke access, I conside global accessibili y
indica o s om Weiss e al. (2018), who de elop a global map o a el ime (in minu es) o
ci ies wi h mo e han 50,000 people in he yea 2015 a 1 km2 esolu ion. The map is based on a
global ic ion su ace cons uc ed om de ailed spa ial da a on anspo ne wo ks and geophysical
ea u es. Nelson e al. (2019) expands his wo k o se lemen s o 9 di e en sizes, om ows o 5000
inhabi an s o megaci ies wi h mo e han 5 million inhabi an s. Nelson (2022) u he compu es
a el ime o po s o 4 di e en sizes ( e y small, small, medium, and la ge) using da a om he
2015 (26 h) edi ion o he WPI. F om hese 12 accessibili y maps, I compu e 4 which appea mos
6F om OSM, Lee & B ai hwai e (2022) employ he o al leng h o oads, dis ance o he closes oad, numbe o
junc ions, dis ance o he closes junc ion, o al building a ea, and he numbe o buildings o each 1 squa e-mile
popula ed a ea, and he numbe o and dis ance o 24 loca ions o in e es such as schools, hospi als, and ma ke s.
5
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 142
ele an in A ica: (1) a el ime o ci ies >50,000 as in Weiss e al. (2018); (2) a el ime o
ci ies >1 million; (3) a el ime o he nea es po , and (4) a el ime o one o 43 medium o
la ge A ican po s.7Figu e A3 shows 3 o hese accessibili y maps and o al GPW4 popula ion.
2.4 The Ideal Spa ial G id
Join ly analyzing in as uc u e and weal h/ac i i y equi es spa ial binning and da a agg ega ion.
Fo accu a e spa ial analysis, an equal a ea g id is desi able. Disc e e Global G id Sys ems (DGGS)
enable his ia hie a chical essella ion o cells pa i ioning he globe. Sah e al. (2003) p opose he
Icosahed al Snyde Equal A ea Ape u e 3 Hexagon (ISEA3H) as a good gene al-pu pose geodesic
DGGS.8ISEA3H is a ailable a 31 di e en esolu ions, om 12 global cells spaced 7054km apa
o 2059 illion cells spaced 0.5m apa . High spa ial esolu ions a e desi able bu inc ease he
compu a ional bu den and educe he numbe o ea u es in each cell, limi ing s a is ical models’
abili y o lea n abou he spa ial economy. I hus empi ically gauge he highes esolu ion g id ha
s ill yields accep able p edic ions o weal h/economic ac i i y by coun ing POIs in each cell and
ca ego y and compu ing he a e age co ela ion o hese coun s wi h he 4 indica o s in Figu e A1.
I also compu e he a e age R2o linea models p edic ing he weal h/ac i i y indica o om all
ca ego y coun s. Appendix Table A8 epo s he esul s o ISEA3H g ids o 7 di e en esolu ions,
anging om 3,901 cells 87km apa down o 557,766 cells 3.2km apa .
Bo h indi idual and join p edic ions become less accu a e wi h inc easing g id esolu ion. The
la ges d op occu s when mo ing om a esolu ion 11 g id (16.8km) o a esolu ion 12 g id (9.7km).
Resolu ion 12 cells ha e a size compa able o he ci y cen e o Kigali (a la ge ci y like Kampala
being co e ed by 3-4 cells) and con ain 94 POIs on a e age (10 in he median cell). To enable high-
esolu ion es ima ion o ci y cen e s and subu ban egions, I op o he esolu ion 12 g id and
mi iga e he d op in p edic i e pe o mance and he e ec s o ha d cell bo de s by allowing spa ial
spillo e s om up o 2nd-o de neighbou s. The implemen a ion o hese spillo e s is desc ibed
below. Figu e 1 isualizes he g id wi h GPW4 2020 o al popula ion es ima es.
2.5 Da a Agg ega ion
I i s agg ega e he as e da a o e he 96km2ISEA3H g id by aking he mean o weal h indices,
a el imes, and in e ne speed, and he sum o GDP, nigh ligh s, and popula ion wi hin each cell.
I also compu e he o al leng h in mo ne wo k ea u es (Table 2) pe cell, dis inguishing pa ed
om unpa ed oads and combining 3 ypes o wa e ways using a e age ha monized coe icien s om
a Ridge Reg ession agains he ou comes in Figu e A1.9I coun POIs in each cell and ca ego y
(Table A3), bu also conside a weigh ed app oach whe e I compu e qua iles o he building-
a eas o all OSM ea u es agged o buildings, and use coun s o 2/3/4 i he a ea is wi hin he
2nd/3 d/4 h qua ile. In his way, la ge ea u es such as la ge school buildings ecei e up o 4
imes he weigh o small school buildings o schools ha a e jus poin s. Simila ly, I also apply
hese qua iles coun s o he a eas o SEZ’s, he capaci y o powe and s eel plan s, and he ou low
o po s. The qua ile me hod hus akes in o accoun he in ensi e ma gin o ea u es.
To agg ega ed he 47 de ailed ca ego ies in Table A3 in o he 26 simpli ied ones, which a e
mo e independen and hus mo e use ul o analysis, I employ weigh s de i ed om penalized
eg essions p edic ing he 4 ou comes om Figu e A1. Fo mally, le he e be Pjde ailed ca ego ies
o simpli ied ca ego y jindexed by p. Fo example he ’communica ions’ ca ego y combines Pj=
7This choice o accessibili y maps is sensible as di e si ied economic ac i i y in SSA ends o ake place in he
la ges u ban cen e s, o which mos coun ies ha e one o wo. Towns o 50,000 people o en unc ion as impo an
hubs o ga he ag icul u al p oduce om he egion o sale on local ma ke s o anspo o la ge ci ies and po s
(Jedwab & S o eyga d,2022). The as majo i y o expo ing o impo ing in A ica also happens h ough medium
and la ge-sized po s, wi h mode n con aine e minals and o en in e sec ing in e na ional shipping ou es.
8An ape u e o 3 implies ha hexagon a eas dec ease by a ac o o 3 as g id esolu ion inc eases. The g id is
implemen ed in he DGGRID C++ lib a y (Sah ,2022), and accessed ia he ’dgg idR’ package (Ba nes,2020).
9Namely i e s, man-made canals, and man-made wa e ways ela ing o u ili ies o ag icul u al ac i i ies such as
d ains and di ches, which a e ini ially summed in o a a iable called ’wa e way o he ’. I hen use Ridge Reg ession
o es ima e an equa ion o he o m y=β0+β1+ i e + β2+ canal + β3+ wa e way o he + ϵ, whe e yis he log o
GDP, he IWI o he RWI (nigh ligh s is oo spa se). The coe icien s a e es ic ed o be g ea e han ze o, and he
op imal Ridge penal y is chosen by 10- old c oss- alida ion. I hen ob ain ela i e coe icien s by di iding h ough
by β1, and a e aging hem ac oss he 3 ou comes. The esul is β1= 1, β2= 9.035, and β3= 6.13, indica ing ha
man-made ea u es a e much mo e impo an o spa ial ac i i y. Thei leng h is hus inc eased by a ac o βj.
6
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 143
Figu e 1: GPW4 2020 Popula ion in 160,719 96km2ISEA3H Cells Co e ing A eas wi h any POI
No es: Figu e shows GPW4 popula ion summed o e an ISEA3H 96km2disc e e global g id co e ing a eas wi h any POI.
3 de ailed ca ego ies: ’communica ions ne wo k’ (cell owe s, an ennas), ’communica ions o he ’
(TV o adio s a ion, newspape , publishe ), and ’ elecom len’, which is he leng h (in m) o OSM
elecommunica ions lines in each cell. I combine hese de ailed ca ego ies in o a simpli ied one (xj)
using app op ia e linea weigh s βjp maximizing he co ela ion wi h he ou comes (y)
max
βjp
co (xj,y)∀js. . βjp ≥0 whe e xj=
Pj
X
p=1
βjpxjp.(1)
To p e en o e i ing and nega i e coe icien s βjp, his p oblem is sol ed using a Ridge Reg ession,
es ic ing βjp o be posi i e, and choosing he op imal penal y pa ame e (λ∗) ia 10- old c oss-
alida ion. The esul ing coe icien s βjp a e no malized by he coe icien o he mos populous
ca ego y (’communica ions ne wo k’), and su p isingly consis en ac oss ou comes. I hus a e age
hem o compu e inal weigh s. Appendix Table A6 p o ides h ee examples. Fo ’communica-
ions’, he coe icien on ’communica ions o he ’ is 11.7 and he coe icien on ’ elecom len’ is 0.1,
which a e sensible in ela ion o a cell owe (’communica ions ne wo k’) ha ing a weigh o 1.
Finally, I accoun o economic geog aphy and mi iga e cell-bo de e ec s by c ea ing addi ional
’spillo e ’ a iables (i.e., spa ial lags) as in e se-dis ance-weigh ed a e age o neighbou ing cells
xneigh
j=X
i=j
xi
δij
/X
i=j
1
δij ∀iwhe e δij < τ. (2)
I choose τ= 24.2km, which includes all second-o de neighbou s. The esul s a e obus o he
absence o spillo e a iables and also o using simple coun s ins ead o qua ile coun s, bu iche
p ocessing esul s in inc eased p edic i e powe and spa ial lags help limi con ounding in luences.
7
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 144
Appendix Table A4 shows summa y s a is ics o he inal g idded da ase (simple coun s).
Appendix Figu e A2 addi ionally shows his og ams o he agg ega ed weal h/ac i i y measu es,
and Appendix Table A5 shows pai wise Pea son’s co ela ions o hese measu es agg ega ed o e
he g id. All measu es a e mode a ely co ela ed bu ollow sligh ly di e en dis ibu ions.
3 A ica’s Spa ial Economy
Wi h e y ich da a on in as uc u e, popula ion, and weal h/ac i i y in A ica a hand, I s a
o by examining A ica’s spa ial economy and he cu en alloca ion o in as uc u e. I i s
analyze he o e all alloca ion and concen a ion o in as uc u e(s), and zoom in on 5 A ican
capi al ci ies o unco e u ban he e ogenei y. I hen examine he spa ial clus e ing o di e en
in as uc u es and c ea e an index o spa ial e iciency measu ing he p oximi y o esiden ial
a eas, co e in as uc u es, and clus e s o economic ac i i y. I ind ha his index is co ela ed
wi h de elopmen indica o s, logis ic pe o mance, and G a (2024)’s oad ne wo k ine iciency
measu e. Finally, I p edic he IWI using ML models and in e p e hem wi h XAI me hods,
yielding a global and local cha ac e iza ion o impo an in as uc u e p edic o s o weal h.
3.1 Spa ial Concen a ion
To s udy spa ial concen a ions, I ake 88,960 g id cells wi h mo e han 10 inhabi an s/km2
acco ding o bo h GPW4 and Wo ldPop 202010 es ima es, coun POIs in simpli ied ca ego ies
and compu e empi ical CDFs. Appendix Figu e A4 shows he esul s, bo h o indi idual ea u e
ca ego ies, some o which a e highligh ed in colou , and o a e ages ac oss ca ego ies, compu ed
be o e o a e he CDF calcula ion. GDP and GPW4 popula ion a e also included as a e e ence.
The op panel e eals ha POIs in A ica a e highly concen a ed - mo e han popula ion and,
o mos ca ego ies, GDP. The op 1000 cells (1.12% o 88,960) accoun o 62% o POIs in he
a e age ea u e ca ego y, bu only 49% o o al GDP and 27% o o al popula ion. Only educa ion
and powe in as uc u e a e less concen a ed han GDP. The a e age CDF sugges s ha close
o 100% o any gi en in as uc u e is alloca ed in less han 10,000 cells. The only widely mapped
ea u e p esen in nea ly all cells is esiden ial buildings. When in as uc u e is pooled ac oss
ca ego ies, i is less concen a ed, and he op 1000 cells only accoun o 30% o in as uc u e.
I esiden ial buildings, a mland, powe , and cons uc ion a e excluded, his sha e ises o 66%.
Excluding hese ou ca ego ies also yields 21,891 popula ed cells (24.6%) ha ha e no o he POI.
The bo om panel shows analogous esul s o line (ne wo k) ea u es, indica ing ha oads,
wa e ways, powe , and ailways a e less concen a ed han GDP, and, in he case o unpa ed oads
and wa e ways, also han popula ion. The op 1000 cells only accoun o 10% o wa e ways and
unpa ed oads, 17% o powe lines, 23% o pa ed oads, and 40% o ailways. Concen a ion also
p oceeds g adually: he op 3000 cells accoun o 20% o unpa ed oads and wa e ways, 39% o
pa ed oads and 70% o ailways, and he op 10,000 cells accoun o 40% o unpa ed oads and
70% o pa ed oads. Excluding wa e ways yields 27.3% o popula ed cells wi h no o he line ea u e.
To also p o ide a spa ial o e iew, Appendix Figu e A5 plo s he spa ial alloca ion o 9 c i ical
in as uc u es on a log10 scale. The op panel shows pa ed oads, powe , and communica ions,
indica ing la ge gaps in cen al A ica, he Sahel, and he Ho n o A ica egions. The g ea lakes
egion zoomed in in hese plo s is well connec ed in he cen al popula ed a eas, bu s ill lacks
connec i i y in spa sely popula ed pe iphe ies such as eas e n Congo and No he n Kenya. The
middle and bo om panels o Appendix Figu e A5 show educa ion and heal h acili es, public
se ices and u ili ies (excl. powe ), au omo i e acili ies, public anspo , and inancial se ices.
Exemp ing educa ion and heal h acili ies, hese ea u es a e la gely concen a ed in u ban a eas.11
10Wo ldPop (h ps://www.wo ldpop.o g/) p o ides high- esolu ion (1km2) global popula ion es ima es using
geospa ial big da a (including in as uc u e) and ML models o dis ibu e adminis a i e es ima es. Thus, i
is locally mo e accu a e han GPW4 da a based solely on adminis a i e sou ces, bu ul ima ely a unc ion o
in as uc u e and hus only used o desc ip i e analysis in his pape .
11Some o hese plo s e idence di e ences in da a co e age; o example, Tanzania and Uganda bo h ha e a e y
la ge amoun o educa ional acili ies, a e aging a ound 1 acili y pe 1000 people, sugges ing ha OSM mappe s
ha e been mo e ac i e in hese coun ies. Heal h seems mo e balanced ac oss coun ies pa ly due o he da a
con ibu ion by Maina e al. (2019). Public se ices (e.g., pos -o ice), au omo i e (e.g., gas s a ion) and public
8
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 145
3.2 U ban He e ogenei y
To unco e he e ogenei y in u ban a eas, I compa e 5 la ge A ican ci ies: Acc a, Cai o, Lagos,
Nai obi, and Johannesbu g. I ake 7 hexagons co e ing he cen al pa s o each ci y, 692km2in
o al, compu e he coun s o POIs and he leng h o lines wi hin hese a eas and di ide hem by
he Wo ldPop 2020 popula ion es ima e. Figu e 2shows he ea u e in ensi ies pe 1000 people.
Figu e 2: Fea u e Densi y in 5 Majo A ican Capi al Ci ies pe 1000 People
0.011 0.12
0.2 0.97
0.14 1.2
0.092 0.87
0.075 0.59
0.075 0.42
0.54 4.8
0.1 1
0.53 2.9
0.16 0.75
0.036 0.2
0.031 0.24
0.091 1.2
0.092 2.8
4.3 23
0.24 0.89
1.50.06
0.078 0.43
0.0970.012
0.051 1.1
25 410
11 370
33 750
1900.3
oads_unpa ed
oads_pa ed
ailway_len
powe _len
powe
s o age
mining_indus ial
cons uc ion
inancial
communica ions
anspo _o he
au omo i e
public_se ice_u ili y
ins i u ional
se ices
en e ainmen
ood
shopping
accommoda ion
ou ism_ ec ea ion
spo
heal h
educa ion
a ming
0.00 0.25 0.50 0.75 1.00
P opo ion o Maximum Ci y Value
Fea u e (Coun o m o Roads/Railway/Powe , pe 1000 People)
Ci y
Acc a
Lagos
Cai o
Nai obi
Johannesbu g
No es: Figu e shows ea u e coun s pe 1000 inhabi an s (Wo ldPop 2020 es ima es) o 5 signi ican A ican ci ies. Fo
each ci y, se en 96km2hexagons co e ing essen ial pa s o he ci y a e conside ed. Fea u es a e coun ed in de ailed
ca ego ies and hen combined in o simpli ied ca ego ies using he weigh ed agg ega ion p ocedu e desc ibed in Sec ion 2.5.
Os ensibly, he ci ies a e e y he e ogeneous. Johannesbu g leads in mos ea u e ca ego ies,
p o iding 750m o pa ed anspo oads, 370m o ailway, and 410m o powe lines pe 1000
inhabi an s, as well as signi ican ly highe au omo i e, o he (public) anspo , communica-
ions, educa ion, and heal h in as uc u e pe capi a han he o he ci ies. Con e sely, Lagos
lags in many ca ego ies, p o iding less han a qua e o he se ices pe capi a han Johannes-
bu g. Be ween hese wo, Nai obi and Acc a a e pe o ming well, wi h Nai obi ha ing he mos
ins i u ions pa capi a alongside high densi ies o educa ion, public se ices, indus ial acili ies,
cons uc ion, accommoda ion (ho els), se ices, ou ism and ec ea ion. Acc a also has a high
le el o ins i u ions, shopping, inancial se ices, accommoda ion, and educa ion acili ies pe
capi a. I is no ewo hy ha , apa om Johannesbu g, he le el o in as uc u e pe capi a
in hese ci ies does no align wi h hei IWI (Acc a: 73.5, Lagos: 67.4, Nai obi: 64.2, Cai o:
70.7 [RF p edic ion], Johannesbu g: 81.6) o GDP pe Capi a12 es ima es, wi h Lagos and Cai o
ela i ely unde pe o ming. In as uc u e concen a ion in A ica is hus a complex unc ion o
agglome a ion economies, economic de elopmen and ins i u ions, his o y, and u ban planning.
anspo (e.g., ain/bus s a ion) acili ies as well as inancial se ices (banks/ATMs) a e mo e consis en ly mapped
ac oss coun ies and la gely concen a ed in ci ies/ owns. Public anspo acili ies a e concen a ed along ailway
lines in some coun ies. In gene al, OSM is a om pe ec , and na ional di e ences in da a co e age may en ail
empi ical p oblems which econome ically can be alle ia ed ia ixed e ec s. In a ML se ing, his amoun s o
including coun y dummies (One Ho Encoding). P ac ically, I ind ha such dummies do no al e he esul s bu
signi ican ly inc ease aining imes, and hence omi hem. The lexible ML app oach wi h high-dimensional da a
(including spa ial lags) appea s o deals g ace ully wi h spa ial di e ences in mapping in ensi y. Ye , comp ehensi e
geospa ial in o ma ion would inc ease he eliabili y and obus ness o he esul s. Comme cial da a p o ide s like
Google o Da aplo may o e signi ican ly imp o ed co e age, bu a e oo expensi e o pu chase a scale.
12Acc a: 3953, Lagos: 7872, Cai o: 9672, Nai obi: 10439, Johannesbu g: 14317 in 2015 USD PPP. These es ima es
a e based on coa se adminis a i e da a by Gennaioli e al. (2013), scaled by Kummu e al. (2018).
9
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 146

3.3 Spa ial Clus e ing
S ill mo e can be lea ned abou in as uc u e concen a ions in A ica by clus e ing ea u es using
spa ial co ela ions− o de e mine which ypes o in as uc u e clus e oge he and which a e
o en ound in popula ed and/o high-income loca ions. I ake he na u al log o ea u e coun s in
he simpli ied classi ica ion, compu e Pea son’s co ela ions among all a iables, and use one minus
he co ela ions as a dis ance ma ix o hie a chical clus e ing wi h comple e linkage. Figu e 3
shows a dend og am, and Appendix Figu e A6 he co esponding clus e ed co ela ion ma ix.
Figu e 3: Hie a chical Clus e ing o Va iables using Co ela ion and Comple e Linkage
po
e y_len
his o ic
cons uc ion
mining_indus ial
educa ion
heal h
accommoda ion
ou ism_ ec ea ion
comme cial
ins i u ional
en e ainmen
beau y
se ices
inancial
public_se ice_u ili y
shopping
au omo i e
ood
eligion
spo
ae oway_len
s o age
mili a y_eme gency
oads_unpa ed
GDP_PPP
pop_gpw4_ages_0_14
pop_gpw4_ages_15_49
wa e way_len
a ming
esiden ial
dam_len
pipeline_len
anspo _o he
ailway_len
IWI
a g_ ad
RWI
communica ions
ime_po _any
ime_po _ml
ime_ci y_50k
ime_ci y_1m
powe
oads_pa ed
0.0 0.2 0.4 0.6 0.8 1.0
hclus (*, "comple e")
.
Dissimila i y (1 − Co ela ion)
No es: Figu e shows dend og am om hie achical clus e ing wi h comple e linkage using a co ela ion-based dis ance
me ic, see also Appendix Figu e A6. Va iables in simple coun s co espond o Appendix Table A4 and a e cas in logs
be o e compu ing co ela ions, excep o he IWI and RWI which a e included in le els. Also, a el ime es ima es (in
minu es) a e i s log- ans o med and hen nega ed o yield posi i e co ela ions wi h o he a iables.
Di e en dissimila i y cu o s e eal dis inc g oups o co ela ed a iables. No ably, se ing
he cu o a ound h= 0.98 e eals h ee p ominen g oups in he dend og am, co obo a ed by he
co ela ion ma ix (Figu e A6). The g oup on he RHS includes a el imes, household weal h,
nigh ligh s, powe in as uc u e, pa ed oads, and communica ions. These a iables seem o be
a p oxy o physical in as uc u e, which in u n co ela es highly wi h nigh ligh s and weal h.
The second g oup, comp ising mos a iables on he LHS o he dend og am, includes economic
ac i i ies in he b oades sense. Finally, he middle clus e includes popula ion, esiden ial a eas,
a ming, and GDP, which is in e pola ed ac oss space using popula ion da a. The dend og am and
co ela ion ma ix (Figu e A6) hus sugges spa ial dispa i ies be ween whe e people li e, whe e
hey wo k, and whe e mos physical in as uc u e is loca ed. P esumably, mo e e icien spa ial
o ganiza ions imply s onge spa ial co ela ions among hese h ee g oups o ea u es.
3.4 Spa ial (In)E iciency
In o med by hese obse a ions, I compu e an index o spa ial e iciency (ISE) along 3 dimensions:
he GPW4 wo king age (15-49) popula ion, he i s p incipal componen (PC1) o pa ed oads,
powe , and communica ions as a compound measu e o (ha d) in as uc u e, and he PC1 o
educa ion, ins i u ional, heal h, eligion, public se ice u ili y, ood, shopping, beau y, se ices,
comme cial, mining indus ial, ou ism ec ea ion, spo , cons uc ion, a ming, en e ainmen ,
inancial, and accommoda ion as a compound measu e o (b oadly concei ed) economic ac i i y.
The PC1 o oads, powe , and communica ions accoun s o 63% o hei join a iance, and he PC1
o he ac i i y a iables cap u es 56% o hei join a iance. Table 3shows Pea son’s co ela ions
among hese componen s. The ISE is hen compu ed as he geome ic mean o hese co ela ions
10
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 147
Table 3: Co ela ions o ISE Dimensions
N= 160,499 P I A
GPW4 POP 15-49 (P) 1.000
Roads & Powe PC1 (I) 0.589 1.000
Economic Ac i i y PC1 (A) 0.634 0.708 1.000
No es: Table epo s Pea son’s co ela ions among dimension indices
(PC1) and popula ion. Thei geome ic mean is he ISE (Eq. 3).
ISE = co (P, I)1
3co (P, A)1
3co (I, A)1
3= 0.642.(3)
I is hus an index on he ange [0, 1], a alue o 1 indica ing pe ec spa ial e iciency wi h all
p oduc i e esou ces concen a ed in he same loca ions. This is p ac ically unachie able, bu
an all-A ica ISE o 0.64 sugges s much oom o imp o emen . Table 4shows coun y-le el ISE
es ima es, compu ed om cells in each coun y, and Appendix Figu e A7 a co esponding map.
Table 4: Coun y-Le el ISE Es ima es (µ= 0.646, σ = 0.193)
# ISO3 ISE ISO3 ISE ISO3 ISE ISO3 ISE ISO3 ISE
1 SYC 0.950 TUN 0.788 ZAF 0.719 COD 0.624 BWA 0.508
2 MLI 0.908 MAR 0.783 BFA 0.717 MDG 0.621 LBY 0.486
3 MUS 0.889 TGO 0.779 SWZ 0.715 DJI 0.619 LBR 0.440
4 UGA 0.868 EGY 0.779 GMB 0.712 BDI 0.617 NAM 0.429
5 RWA 0.860 BEN 0.774 GAB 0.694 CAF 0.612 ESH 0.410
6 KEN 0.849 MWI 0.763 NGA 0.693 MOZ 0.593 ERI 0.360
7 GHA 0.830 LSO 0.750 ZWE 0.688 AGO 0.573 SOM 0.284
8 CIV 0.822 TZA 0.745 GIN 0.686 SDN 0.568 CPV 0.252
9 SLE 0.813 ETH 0.744 MRT 0.675 CMR 0.564 COM 0.214
10 STP 0.812 DZA 0.741 COG 0.672 TCD 0.543 GNQ 0.153
11 SEN 0.809 NER 0.729 ZMB 0.651 GNB 0.542 SSD 0.105
No es: Table epo s so ed coun y-le el ISE es ima es (Eq. 3) compu ed om cells wi hin each coun y.
The coun y-le el ISE es ima es a e mildly co ela ed wi h key de elopmen indica o s in 2020,
such as GDP pe Capi a PPP ( = 0.159), Li e-Expec ancy a Bi h ( = 0.215), and he Human
De elopmen Index ( = 0.185). In e es ingly, hey shows s onge co ela ions wi h he 2018
Logis ics Pe o mance Index ( = 0.396) and he 2020 Doing Business Index ( = 0.592). They a e
also nega i ely co ela ed o he hypo he ical wel a e gains (in pe cen ) om an op imal ealloca ion
o he oad ne wo k in each coun y as calcula ed by G a (2024)( =−0.360). The s onge
associa ion o he ISE wi h hese indica o s is-a- is de elopmen ou comes sugges s ha i indeed
measu es spa ial (in)e iciency. The index is unco ela ed wi h o al land a ea ( =−0.033),
al hough se e al small s a es like Seychelles, Mau i ius, and Rwanda sco e pa icula ly high.
3.5 Weal h P edic ion and In e p e a ion
Gi en he ich na u e o A ica’s spa ial economy, a ML app oach o p edic weal h om in as uc-
u e ha is able o cap u e non-linea associa ions in he da a can yield u he insigh s. Bo iso e
al. (2021) show ha g adien -boos ing machines (GBMs) (J. H. F iedman,2001) s ill ou pe o m
mos deep lea ning me hods on abula da a. Thus, I employ he compe i ion-winning XGBoos
algo i hm (Chen & Gues in,2016) and une i s hype pa ame e s wi h Op una (Akiba e al.,2019)
on a es se con aining 25% o he da a.13 The IWI model ained wi h ea ly s opping achie es a
es se R2o 78.8%. Fo compa ison, I also ain a Random Fo es (RF) (B eiman,2001) model
wi h de aul pa ame e s and 1000 ees, which achie es a es se R2o 73.3%. Appendix Figu e
A8 shows empi ical CDFs o he absolu e alues o he esiduals, indica ing ha he XGB model
p edic s 76% o es -se obse a ions wi h an e o o less han 5 IWI poin s.
13The op imal hype pa ame e s gene ally ea u e a low lea ning a e (η= 0.01 −0.02), deep ees (max dep h =
9−15), signi ican andomiza ion o e samples (subsample = 0.5−0.8) and signi ican egula iza ion, especially
h ough cons ain s on he minimal size o inal nodes (min child weigh = 6 −20).
11
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 148
To accu a ely a ibu e p edic ions ac oss he di e en a iables, I compu e Shapely Values, a
game heo e ic app oach o ai ly a ibu e he con ibu ion o a iables o a single p edic ion. In
pa icula , SHAP (SHapley Addi i e exPlana ions) ollowing Lundbe g & Lee (2017) gi e addi i e
a iable con ibu ions o each ins ance ha sum o he di e ence o he p edic ion om he
a e age model p edic ion. I use he T eeSHAP algo i hm o Lundbe g e al. (2020) o compu e
(in e en ional) SHAP alues o wo di e en XGBoos models: he model ained on he ull
da ase e alua ed abo e, and a model excluding popula ion and a el ime es ima es. The la e
emo es s ong co ela ions o hese a iables wi h weal h. The op panel o Figu e 4summa izes
o e all a iable impo ance o he IWI based on he a e age absolu e SHAP alue ac oss ins ances.
Figu e 4: (A e age) SHAP Values o XGBoos Models P edic ing he IWI
Wi h Popula ion and T a el Time No Popula ion and T a el Time
0123456
mean(|SHAP alue|)
pop_gpw4_ages_0_14
pop_gpw4_ages_15_49
ime_po _ml
oads_pa ed
ime_ci y_1m
communica ions
ime_po _any
oads_unpa ed
educa ion
esiden ial
ime_ci y_50k
in e ne _speed
heal h
powe
wa e way_len
spo
accommoda ion
inancial
anspo _o he
au omo i e
a ming
se ices
public_se ice_u ili y
eligion
Sum o 18 o he ea u es
pop_gpw4_ages_0_14
pop_gpw4_ages_15_49
ime_po _ml
oads_pa ed
ime_ci y_1m
communica ions
ime_po _any
oads_unpa ed
educa ion
esiden ial
ime_ci y_50k
in e ne _speed
heal h
powe
wa e way_len
spo
accommoda ion
inancial
anspo _o he
au omo i e
a ming
se ices
public_se ice_u ili y
eligion
Sum o 18 o he ea u es
+5.61
+4.18
+3.5
+1.61
+1.54
+1.1
+1.05
+0.97
+0.67
+0.56
+0.51
+0.42
+0.41
+0.33
+0.26
+0.24
+0.23
+0.2
+0.19
+0.15
+0.15
+0.12
+0.11
+0.1
+0.88
XGB: IWI
0.0 0.5 1.0 1.5 2.0 2.5
mean(|SHAP alue|)
oads_pa ed
communica ions
oads_unpa ed
in e ne _speed
esiden ial
educa ion
powe
wa e way_len
a ming
accommoda ion
spo
heal h
eligion
au omo i e
anspo _o he
se ices
dam_len
ailway_len
inancial
comme cial
public_se ice_u ili y
en e ainmen
mining_indus ial
cons uc ion
Sum o 12 o he ea u es
oads_pa ed
communica ions
oads_unpa ed
in e ne _speed
esiden ial
educa ion
powe
wa e way_len
a ming
accommoda ion
spo
heal h
eligion
au omo i e
anspo _o he
se ices
dam_len
ailway_len
inancial
comme cial
public_se ice_u ili y
en e ainmen
mining_indus ial
cons uc ion
Sum o 12 o he ea u es
+2.63
+1.65
+1.37
+1.2
+1.03
+0.87
+0.81
+0.52
+0.48
+0.47
+0.41
+0.37
+0.37
+0.26
+0.22
+0.2
+0.19
+0.18
+0.17
+0.17
+0.16
+0.15
+0.15
+0.14
+0.66
XGB: IWI
20 10 0 10 20 30 40
SHAP alue (impac on model ou pu )
Sum o 23 o he ea u es
anspo _o he
inancial
accommoda ion
spo
wa e way_len
powe
heal h
in e ne _speed
ime_ci y_50k
esiden ial
educa ion
oads_unpa ed
ime_po _any
communica ions
ime_ci y_1m
oads_pa ed
ime_po _ml
pop_gpw4_ages_15_49
pop_gpw4_ages_0_14
XGB: IWI
Low
High
Fea u e alue
10 0 10 20 30
SHAP alue (impac on model ou pu )
Sum o 17 o he ea u es
inancial
ailway_len
dam_len
se ices
anspo _o he
au omo i e
eligion
heal h
spo
accommoda ion
a ming
wa e way_len
powe
educa ion
esiden ial
in e ne _speed
oads_unpa ed
communica ions
oads_pa ed
XGB: IWI
Low
High
Fea u e alue
No es: Figu e shows (a e age absolu e) in e en ional SHAP alues o XGBoos models ollowing Lundbe g e al. (2020)
(i.e., using he T eeSHAP algo i hm o compu e SHAP alues on ee-based ensemble models like XGBoos e icien ly).
Shapely Values a e a game- heo e ic app oach o ai ly a ibu e ML model p edic ions o he p edic o s ( a iables) a he
ins ance le el. SHAP alues quan i y such a ibu ions as he con ibu ion o each p edic o o he di e ence be ween he
p edic ion and he a e age model p edic ion ac oss all ins ances. The a e age absolu e SHAP alue ac oss ins ances ( op
panel) hus gi es a p ecise summa y o a p edic o ’s global signi icance, whe eas plo ing indi idual SHAP alues using a
Beeswa m plo and colou ing hem by he ea u e le el (bo om panel) compac ly summa izes he di ec ion o he e ec
(which may a y non-linea ly wi h he p edic o le el, mo e clea ly isible in sca e plo s as in Appendix Figu e A14).
Figu e 4sugges s ha , apa om popula ion and a el ime o majo ci ies and po s, oads,
communica ions, educa ion, powe in as uc u e, and esiden ial buildings a e he mos signi ican
12
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 149
p edic o s o weal h. Many u he ea u es such as accommoda ion (ho els), spo , heal h, au o-
mo i e, and public anspo acili ies a e also impo an . Wi h nigh ligh s as ou come (Appendix
Figu e A10), popula ion, a el ime, communica ions, oads, and powe emain impo an , bu
spo s, au omo i e acili ies, and indus ial a eas ise in he anking, being la ge emi e s o ligh .
To gauge he di ec ion o he e ec s, which may be he e ogeneous as SHAP alues a e compu ed
a he ins ance le el and ee ensembles may be highly non-linea , he bo om panel o Figu e 4
p o ides a beeswa m plo o he SHAP alues colou ed by ea u e in ensi y. The plo sugges s ha
mos a iables ha e he in ended e ec , wi h highe in ensi y ansla ing in o la ge SHAP alues
(p edic ions). De ying he in ui ion, esiden ial buildings, educa ion, heal h, and eligion ha e
la gely nega i e e ec s. This spa ial pa e n needs o be cau iously in e p e ed ce e is pa ibus,
i.e., in he p esence o o he co ela ed ea u es o en ound in weal hy u ban loca ions (e.g.,
pa ed oads, powe , communica ions), mo e educa ion and heal h acili ies may dec ease model
p edic ions. Appendix Figu e A11 shows he same plo o nigh ligh s, wi h simila esul s.
Appendix Figu es A14 and A15 addi ionally p o ide de ailed sca e plo s be ween ea u e
in ensi y and SHAP alues o key p edic o s. They a e pa icula ly use ul owa ds de ec ing
h esholds whe e a ea u e’s e ec on he p edic ion begins o change. Fo example, he SHAP
alue o pa ed oads s ongly inc eases abo e 104= 10km in a cell, sugges ing ha oads a e
mo e impo an o p edic ion in u ban a eas. The same applies o powe beyond a h eshold o
102.5≈300 acili ies (e.g., ans o me s, gene a o s) pe cell. Wi h educa ion, he opposi e is he
case: up o 10 acili ies pe cell, he SHAP alue is high, bu beyond ha , i educes, indica ing ha
in cen al u ban spaces, he p esence o schools dec eases weal h p edic ions. Heal h and eligious
acili ies exhibi simila bu weake dynamics. In con as , communica ions (mainly cell owe s)
ha e an almos log-linea posi i e e ec on model p edic ions. In models wi h popula ion, he
in an (0-14) popula ion has a s ong nega i e e ec on p edic ions, whe eas he adul popula ion
(15-49) has a s ong posi i e e ec , implying signi ican Mal husian dynamics in he da a.
4 Es ima ing Ma ginal In as uc u e Bene i s
Ha ing explo ed he da a in some dep h, his sec ion ad ances by asking abou he ma ginal
e ec s o in as uc u e on weal h/ac i i y. In he absence o a iable iden i ica ion s a egy (such
as RCT, IV, RDD, DID) o causally iden i y in as uc u e e ec s a spa ial scale and ac oss
di e en ca ego ies, I use obse a ional causal in e ence−causal ML− o app oxima e such e ec s.
The adi ional econome ic iew, s ill held o some ex en in o he disciplines, is ha i one
can con ol o he mos impo an con ounding ac o s in an obse a ional se ing, a ca e ul ce e is
pa ibus in e p e a ion o he pa ial e ec is possible. So a , his p emise has been applied almos
exclusi ely in he con ex o linea eg ession, implying ha i all con ounde s a e obse ed and
he popula ion model is linea -addi i e, a ca e ul ce e is pa ibus in e p e a ion is possible.
Howe e , he ce e is pa ibus s a emen need no be ha s ong, as one can elax he assump ions
o linea i y and addi i i y. The p emise o causal ML (also known as ’double’ o ’debiased’ ML) is
ha i one obse es all ac o s ha con ound o p oxy o con ounding in luences, he ela ionship
be ween ea men and ou come can be speci ied condi ional on an op imal ML p edic ion o bo h
om obse ables. In he se ing a hand, his means ha i A ica’s spa ial economy is su icien ly
obse ed h ough he a ailable g anula da a on in as uc u e, POIs, popula ion, and ma ke
access, and i app op ia e ML models a e deployed, i may be possible o iden i y ma ginal pa ial-
equilib ium e ec s o indi idual in as uc u es on weal h/economic ac i i y. Be o e examining
hese iden i ica ion assump ions in mo e de ail, I in oduce his es ima ion s a egy mo e o mally.
I ollow Nie & Wage (2021) and Che nozhuko e al. (2017); Che nozhuko , Che e iko , e al.
(2018), and adop some no a ion om Hi ano & Imbens (2004). Le Ybe an ou come o in e es ,
Wa con inuous ea men o in e es (a speci ic in as uc u e) wi h possible alues ω∈Ω, and
X= [X′
H,X′
C]′be a ec o o obse ed con ounde s, whe e XHincludes co a ia es ha also a ec
ea men e ec he e ogenei y. The uncon oundedness assump ion is hen o mally s a ed as
Y(ω)⊥W|X∀ω∈Ω,(4)
13
CHAPTER 4.1. MAPPING AFRICA’S INFRASTRUCTURE POTENTIAL Page 150
le s he o al MA gain om upg ading all links d op o 27%. Again, pa icula ly pe iphe al and
ansna ional links lose alue. I es ima e he cos o upg ading he en i e ne wo k a
$
106B,
comp ising
$
67.8B ull upg ades,
$
36.5B mixed wo ks, and
$
12.3B asphal esu acing. Upg ading
all oads yields a e age gains o
$
6/min pe
$
spen , which d ops o
$
2.4/min/
$
unde ic ions.
To aise a e age e u ns, I conside in es men packages a ge ing high ma ginal gain links
(bo h upg ades and new oads). I p opose h ee packages wi h links a ma ginal gains >1, 2,
o 4
$
/min/
$
. They cos
$
60.9B/
$
36.6B/
$
17B, yield ic ionless MA gains o 38.1%/30.6%/20%,
and 26.8%/24%/19.5% unde ic ions. These packages a e globally mac oeconomically easible
unde ic ions i hey can aise A ica’s agg ega e g ow h a e (4.1%) by 0.33%/0.2%/0.09%. Fo
he >
$
1 (
$
60.9B) package, his would equi e ha g ow h in MA ansla es in o economic g ow h
a a a e o ≥81:1. This is plausible in ligh o empi ical esul s sugges ing economic e u ns o
MA gains as high as 2:1, bu he e a e se e al ac o s ha could aise he cos o hese packages
conside ably and lowe he mac oeconomic e u ns, especially i hey a e inanced h ough deb .
Calib a ing a gene al equilib ium amewo k by Fajgelbaum & Schaal (2020) pe mi ing global
op imiza ion o e he space o ne wo ks, I also de i e op imal in es men s in spa ial equilib ium,
le ing a wel a e-maximizing social planne spend a ixed in as uc u e budge . My calib a ion
accoun s o ade in mul iple goods and h ough in e na ional po s. I conside cases wi h/wi hou
inequali y a e sion, impo s, c oss-bo de ic ions, and inc easing e u ns o in as uc u e. I also
simula e wi h di e en alues o he elas ici y o subs i u ion (σ). Due o he compu a ional
complexi y o he p oblem, I spli he simula ions in o wo pa s. In Pa 1, a egional planne
imp o es connec i i y be ween small/medium/la ge ci ies and po s. In Pa 2, a ans-A ican
planne op imally in es s in con inen al anspo oads connec ing 47 majo (po -)ci ies.
The egional planne wi h a budge o
$
50B op imally connec s la ge ci ies and po s wi h
each o he and su ounding smalle ci ies, ocusing on popula ed/p oduc i e a eas. An inequali y-
a e se planne in es s sligh ly mo e in he poo e Cen al A ican and he Ho n o A ica egions.
Wi h inc easing e u ns o in as uc u e, he in es men s become less concen a ed a ound la ge
ci ies, and he planne upg ades mo e oads (building ew new ones). This inc eases wel a e gains
o 7.4%, up om 5.8% unde dec easing e u ns. Wi h inequali y a e sion, he gains become
mo e equi able a mino agg ega e losses, and he planne upg ades mo e oads in Cen al A ica.
Due o conca e u ili y o consump ion, emo e ci ies gain mo e despi e ecei ing less in as uc u e.
The ans-A ican planne , endowed wi h
$
10B, also mos ly imp o es egional connec i i y
unde s anda d assump ions o σbe ween 3.8 and 5. Much is in es ed along he Wes A ican
coas line, bu also in be e connec ing Daka o Bamako, Kha oum o Egyp , Kampala o
Mombasa, and Kinshasa o Mbuji Mayi. An inequali y-a e se planne ully connec s Kinshasa
o Buka u/Rwanda and u he o Mombasa, Mogadishu, and Addis Ababa. Thus, he planne
upg ades a co ido c ossing popula ed Cen al A ica. A low σ= 2, bo h planne s also close he
la ge in as uc u e gap in spa sely popula ed no he n Cen al A ica by upg ading oads om
Rwanda/Buka u and Kha oum o Wes A ica. Thus, op imal ans-A ican in es men s depend
c i ically on he p opensi y o ade. Only a low σdo majo in as uc u e gaps in Cen al A ica
become a policy p io i y. High bo de ic ions educe equilib ium in es men s along a ec ed links.
In conclusion, he pape cha ac e izes op imali y o oad ne wo k in es men s a di e en
spa ial scales and economic objec i es om a pan-A ican pe spec i e. I hus p o ides b oad
guidance o policymake s in e es ed in imp o ing A ica’s oad in as uc u e. An o e a ching
inding is ha ans-A ican oads a e nei he MA no wel a e maximizing compa ed o bene i s
om imp o ed local connec i i y in popula ed a eas o be ween nea by ci ies and coas al po s.
This is especially ue unde bo de ic ions, which educe he alue o ans-A ican links.
Ye , hese links a e essen ial o ans-A ican ade unde A CFTA o pick up. A gene al
policy ecommenda ion is hus o p io i ize egional connec i i y imp o emen s, educe bo de
ic ions, and in es in ans-A ican oads along speci ic high-yield links/co ido s. The g aphical
esul s p o ided in he pape and appendix should be help ul in p io i izing con inen al and
egional in es men s, wi h de ailed da a and esul s a ailable on Gi Hub. The mul i-le el app oach
combining ou ing engines, PE, and GE analysis can also be adap ed o u he examine speci ic
egions/in es men s. Towa ds his end, he Julia lib a y and ep oducibili y package a e u ile.
55
CHAPTER 4.2. OPTIMAL INVESTMENTS IN AFRICA’S ROAD NETWORK Page 253

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