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A G D I Working Paper
WP/20/003
An Index of African Monetary Integration (IAMI)
1
Samba Diop
Faculty of Economics and Management, P.O. Box, 30,
Alioune Diop University, Bambey, Senegal
E-mail: diopapasamba@gmail.com
Simplice A. Asongu
African Governance and Development Institute,
P. O. Box 8413, Yaoundé, Cameroon
E-mails: asongus@afridev.org / asongusimplice@yahoo.com
1
This working paper also appears in the Development Bank of Nigeria Working Paper Series.
2020 African Governance and Development Institute WP/20/003
Research Department
An Index of African Monetary Integration (IAMI)
Samba Diop & Simplice A. Asongu
January 2020
Abstract
This study improves the African Regional Integration Index (ARII) proposed by the African
Union, the African Development Bank and the United Nations Economic Commission for
Africa by providing a theoretical framework and addressing shortcomings related to
weighting and aggregation of the indicator. This paper measures monetary integration in the
eight African Regional Economic Communities (RECs) by constructing an Index of African
Monetary Integration (IAMI). It proposes an Optimal Currency Area as theoretical framework
and uses a panel approach to appreciate the dynamics of the index over different periods of
time. The findings show that: (i) inflation and finance (trade and mobility) present the highest
(lowest) score while ECOWAS is (EAC and IGAD are) the highest (least) performing. (ii)
Surprisingly, in most RECs, the highest contributors to wealth creation are not the top
performers in regional monetary integration. (iii) The RECs in Africa are characterized by a
stable monetary integration which is different from the gradual process usually observed in
monetary integration because with the exception of the EAC and UMA, the dynamics of
IAMI show a steady trend in the overall index across time. Policy implications are discussed.
Keywords: Monetary Integration; Currency Unions; Economic Communities; Africa
JEL Codes: E10; E50; O10; O55; P50
1. Introduction
In recent years, the debate about African regional integration has been renewed in policy and
scholarly circles (Akpan, 2014; Kayizzi-Mugerwa et al., 2014; Njifen, 2014; Charaf-Eddine
& Strauss, 2014; Baricako & Ndongo, 2014; Nshimbi & Fioramonti, 2014; Ebaidalla &
Yahia, 2014; Ofa & Karingi, 2014; Shuaibu, 2015; Tumwebaze & Ijjo, 2015; Asongu, 2016;
Asongu et al., 2020a). Consistent with the attendant literature, the political objective of
economic integration and a monetary union was formalized in the Treaty of Abuja in 1991.
Indeed, after the successful launch of the euro in 1999, the association of governors of
African central banks renewed their interest for monetary integration. Accordingly, the
Regional Economic Communities (RECs) should play an important role in such a monetary
integration
2
. In efforts to facilitate the process of monetary integration, in 2016, the African
Union (AU), the African Development Bank (AfDB) and the United Nations Economic
Commission for Africa (UNECA) developed and proposed an African Regional Integration
Index (ARII). The objective of this index is to gauge the degree of regional integration of
RECs in Africa. However, according to Gor (2017), the proposed index must be improved for
many reasons. Firstly, the ARII is not founded on any theoretical framework. Secondly, there
is a serious problem on weighting and aggregation of the indicator as well in the calculation
of overall index from RECs. The purpose of this study is to address the shortcomings
identified in Gor (2017) by constructing a quantitative monetary index for the eight existing
RECs in Africa.
The construction of the new index is relevant to scholars and policy makers because it
provides insights into how successful monetary policies are in promoting monetary
integration in Africa. More specifically, the aim of this article is threefold: (i) to improve the
ARII’s relevance in order to enhance its reliability; (ii) to expand the previous literature on
the feasibility of common currency in the whole Africa using a different approach; (iii) to
provide a quantitative tool for both researchers and policy makers to synthesize and monitor
the process of African monetary integration. In this paper, we refine the ARII’s methodology
to enhance its soundness to track the process of African integration. Our index differs from
the ARII in three main ways. Firstly, we use the Optimum Currency Area (OCA) as a
theoretical framework by anchoring the composite index on a sound theoretical footing.
Secondly, panel normalization is employed to build a dynamic monitor which allows us to
identify different changes over time. Finally, to avoid the problem of extreme values in the
2
For more details on the RECs, see Figure 1 in appendix.
dataset, we use many other techniques of normalization to check the robustness of our results.
Given the OCA theoretical underpinnings, we use macroeconomic indicators across African
member states to calculate a quantitative index in order to assess the feasibility of a potential
currency union.
In the light of the above, the positioning of this study departs from the extant literature
on the feasibility of the African Monetary Union (AMU) which has not focused on
developing an index, but on using existing macroeconomic indicators to assess the feasibility
of the proposed AMU (Masson & Patillo, 2004; Coulibaly & Gnimassoun, 2013; Asongu et
al., 2017). The existing literature which is documented in Asongu et al. (2017) can be
discussed in four main strands, notably, the: AMU, West African Monetary Zone (WAMZ),
East African Monetary Union (EAMU) and Southern African Monetary Union (SAMU).
Each of the strands is summarised into arguments for a currency union, arguments against a
currency union and arguments for a currency union contingent on compliance with certain
convergence criteria by potential member states. Each of the four strands is summarised in the
following passages.
First, arguments from the proposed AMU are supported by Guillaume and Stasavage
(2000) and Tsangarides et al. (2006), arguments against the proposed currency are in
Bayoumi and Ostry (1997) and Karras (2007) whereas the attendant literature supporting an
AMU, though with some reservations include: Yehoue (2005), Buigut (2006), Buigut and
Valev (2006), Masson (2006, 2008), Debrun et al. (2011) and Tsangarides and Qureshi
(2015). Second, with regard to the WAMZ, Ogunkola (2005) and Diop (2012) conclude on
its feasibility, while a bulk of the attendant literature is either of the position that the currency
is unfeasible (Debrun et al., 2005; Houssa, 2008; Tsangarides & Qureshi, 2006; Cham, 2009;
Chuku, 2012; Alagidede et al., 2012; Asongu, 2013b, 2014bc; Dufrénot & Sugimoto, 2013;
Harvey & Cushing, 2015; Asongu et al., 2019) or conditionally feasible if some criteria
converge (Bénassy-Quéré & Coupet, 2005; Asongu, 2014a; Ekpoh & Udoh, 2013; Bangaké,
2008; Saka et al., 2015). Third, in the EAMU, a substantial body of literature has been
sympathetic to arguments against the currency union (Rusuhuzwa & Masson, 2012; Buigut,
2011; Mafusire & Brixiova, 2013; Davoodi et al., 2013; Asongu, 2014b, 2014c; Lepetit et al.,
2014), perspectives for the currency union (Mkenda, 2001; Bangaké, 2008; Asongu, 2013b)
as well as views for the currency union after some conditions have been met (Buigui & Valev,
2005 ; Buigut & Valev, 2009; Falagiarda, 2010; Sheik et al., 2011; Kishor & Ssozi, 2011).
Fourth, for the SAMU, Grandes (2003) and Debrun and Masson (2013) provide perspectives
on its feasibility, Agdeyegbe, (2009) recommends against the union while the greater bulk of
the literature in the strand advocates for a currency zone subject to improvements in
compliant conditions in potential members states (Khamfula & Huizinga, 2004; Wang et al.,
2007; Jefferis, 2007; Masson, 2008; Masson, 2008; Bangaké, 2008; Zehirun et al., 2015;
Asongu et al., 2020b).
The rest of the study is structured as follows. Section 2 provides insights into the ARII
while Section 3 discusses the proposed index in the light of theoretical underpinnings,
imputation and normalization as well as weighting and aggregation. The main findings of the
proposed Index of African Monetary Integration (IAMI) are provided in Section 4 while
Section 5 concludes with implications and future research directions.
2. The African Regional Integration Index
The African Regional Integration Index (ARII) is a joint product of three main institutions,
namely, the: African Union Commission (AUC), African Development Bank (AfDB) and
United Nations Economic Commission for Africa (UNECA). The index is comprised of five
dimensions made up of sixteen indicators. The dimensions are: trade integration, regional
infrastructure, productive integration, free movement of people and, financial and
macroeconomic integration. Table 1 reports the average score for each REC on every
dimension and the average score for all RECs in each dimension. The scores are calculated on
a scale of 0 (low) to 1 (high).
Table 1: Average RECs Scores in all dimensions of regional integration
RECs
Trade
integration
Regional
infrastructure
Productive
integration
Free
movement
of people
Financial and
macroeconomic
integration
CEN-SAD
0.353
0.251
0.247
0.479
0.524
COMESA
0.572
0.439
0.452
0.268
0.343
EAC
0.780
0.496
0.553
0.715
0.156
ECCAS
0.526
0.451
0.293
0.400
0.599
ECOWAS
0.442
0.426
0.265
0.800
0.611
IGAD
0.505
0.630
0.434
0.454
0.221
SADC
0.508
0.502
0.350
0.530
0.397
UMA
0.631
0.491
0.481
0.493
0.199
Average
0.540
0.461
0.384
0.517
0.381
Sources: ARII (2016). RECs: Regional Economic Communities. CEN-SAD: Community of Sahel-Saharan
States. COMESA: Common Market for Eastern and Southern Africa. EAC: East African Community. ECCAS:
Economic Community of Central African States. ECOWAS: Economic Community of West African States.
IGAD: The Intergovernmental Authority on Development. SADC: Southern African Development Community.
UMA: Arab Maghreb Union.
We can note that the highest scores are on trade integration, with average of the eight
RECs scores of 0.540. EAC is the highest performing REC on the trade integration dimension
and CEN-SAD and ECOWAS are not in particular high performers on this dimension. The
lowest scores are on financial and macroeconomic integration. It is the lowest score overall
among RECs with a 0.381 average. For this dimension, ECOWAS is the highest performing
REC. The average REC scores are closest together on regional infrastructure and productive
integration. Average REC scores are furthest apart on free movement of people and financial
and macroeconomic integration. As noted earlier, this index is not based on any theoretical
framework and individual indicators appear to have been selected in an arbitrary manner.
Indeed, as documented in Gor (2017), the index suffers from issues of weighting,
normalization and calculation of overall index by REC. It is essentially for this shortcoming
that, in this study, the proposed monetary index is based on the theoretical framework of
OCA. Moreover, this study also engages sensitivity checks in order to provide an evaluation
of the robustness of the composite indicator.
3. Steps for constructing the Index of African Monetary Integration (IAMI)
In this section, we present the different steps for constructing the IAMI. To avoid risks and
lack of transparency in the process, especially in the methodology, we develop four steps that
are extremely important for understanding the construction.
3.1Theoretical framework and data selection
The theoretical framework and the data selection are the most important steps when
constructing an index. In effect, they provide the basis for the selection and combination of
variables into a meaningful composite indicator. This step represents the starting point in the
construction of the composite indicator. In our study, the choice of variables is guided by the
OCA theory. The concept of OCA was defined by Mundell (1961). This author presented the
mobility factor (especially labour mobility) as the most important criterion in the feasibility of
a monetary union. In chronological relevance, the second important contributor to the OCA
theory is Mckinnon (1963). For the author, the degree of openness is a crucial criterion. The
third contributor is Kenen (1969) who introduces product diversity as an important criterion.
It is important to note that theoretical underpinnings surrounding the OCA have evolved,
building on the attendant seminal papers. Beside these traditional criteria, a large number of
criteria have been introduced such as financial integration, trade openness, endogeneity of
OCA, effectiveness of exchange rate adjustments, synchronization of business cycles,
political and institutional factors, similarity of shocks, inter alia (Asongu et al., 2017, 2019,
2020b). For a more comprehensive approach, the variables used in this study in the light of
the OCA theory are provided in Table 2.
Table 2: Dimensions and variables
Dimensions
Variables
Authors
Factors mobility
Proportion of intraregional remittances to total
remittances
Mundell (1961)
Corden (1972)
Proportion of intraregional migrants to total
migrants (inbound plus outbound)
Proportion of intraregional migrants to total
migrants (outbound)
Proportion of intraregional tourists to total
tourists (inbound)
Trade integration
Intraregional trade intensity index
McKinnon (1963)
Proportion of intraregional goods exports to
total goods exports
Proportion of intraregional goods imports to
total goods imports
Inflation differential
Inflation rate differential
Haberler (1970)
Fleming (1971)
Mongelli (2002)
Exchange rate differential
Synchronicity
GDP growth differential
Kenen (1969),
Krugman (1993)
Frankel and Rose
(1998)
GDP per capita differential
GDP per capita growth differential
Financial integration
Difference between number of commercial
banks
Ingram (1962)
Difference between the spread of interest rate
Difference of credit provided by commercial
banks
Sources: Authors’ compilation
3.2 Imputation and Normalization
There are in general three methods for dealing with missing data. The methods are: case
deletion, simple imputation and multiple imputations. We have a great number of missing
data because of lack of observations for a set of countries. In order to minimize the missing
observations, we replace some missing data by the mean of their values. Our data are annual
and cover the period 2012-2016. The countries used are presented in the appendices. For the
normalization, there are a large number of methods (see Table 3). In this work, we use
different methods to normalize the data. They are summarized in the following table. Given
the value of indicator q for country c at time t. is the reference country.
Table 3: Normalisation methods
Methods
Equations
Ranking
Standardization (or z-scores)
Min-Max
Softmax
Distance to a reference country
or
Indicator above or below the mean
where
Cyclical indicator
% of annual differences over consecutive
years
Sources: Authors’ adaptation from OECD (2008)
In this paper, we use panel normalization to take into account the time consistency in the
computation of the index. Then, the minimum and the maximum values for each indicator are
calculated across individuals and time periods. The transformation is :
For indicators representing a differential such as inflation, exchange and GDP, where higher
values imply lower integration, we use the following transformation:
3.3 Weighting and aggregation
The weighting and aggregation are of significant importance in the calculation of the overall
index and by extension, the rankings. There are many weighting methods. In this paper, we
use a multivariate data analysis technique. More specifically, we employ a panel principal
component analysis (PPCA). This choice is justified by the fact that with this method, we can
summarize a set of variables without losing the important variability in the original data
(Tchamyou, 2017, 2020). Also, with the panel dimension, it is able to take into account the
evolution of the index over time. The objective of PPCA is to explain the variance of the
observed data through a few linear combinations of the original data.
In a panel situation, we have a multidimensional data vector
3
:
where is the number of periods and is the number of variables.
Let be the correlation matrix of the variables. The principal component
is defined as:
3
For the Panel Principal Components Analysis, we follow the criteria of Park and Claveria (2018).
Accordingly, in a matrix form, , where , the coefficient matrix
maximizes the variance of subject to the following constraints:
and
The solution to the eigenvalue-eigenvector problem resulting from of this optimization
program is which is equal to the variance of , with .
Loadings obtained from the PPCA can now be used to compute the different weights.
4
In the
first step, the PPCA is applied on the variables in every dimension to obtain the different
weights. Once the weights are obtained, PPCA is again applied to the weighted sub-indexes to
compile the overall index.
4. Main findings of the Index of African Monetary Integration (IAMI)
In the first step, we apply the PPCA to select the number of component factors. The general
rule is the Kaiser criterion which drops all factors with eigenvalues below 1 (Tchamyou,
2017, 2020). As we can see in Table 4, in all cases, with the exception of factor mobility in
CEN-SAD, where the first component contributes to 85% of the explanation of the overall
variance, the first-two factors explain the most variance. Following this information, we
conclude that the first-two principal factors explain the variability of the five dimensions. The
second step deals with the construction of the weights (see Table 4).
4.1 Analysis of the indexes (average 2012-2017)
Table 5 presents the sub-indexes and the overall index for every REC. Average REC scores
are closest together on financial integration and are furthest on trade integration. Moreover,
highest scores are in inflation and financial integration while lowest scores are noted in trade
and mobility. When we consider the overall index, among the eight RECs, the ECOWAS is
the most regionally integrated with the highest score (0.672). This result confirms those of the
dimension of financial and macroeconomic integration of the ARII. This is not surprising as
the ECOWAS is the oldest REC in Africa. Indeed, in this community, we have the eight West
4
For more details on how to calculate the loadings and weights, see Huh and Park (2017).
Africa Economic and Monetary Union (WAEMU) economies which have been sharing the
same currency for more than 70 years. The second integrated community is SADC with a
score of 0.618. The EAC and IGAD are the lowest integrated regions. The EAC is the highest
performing REC in terms of trade integration (0.478). The ECOWAS earns its highest scores
from the mobility sub-index, while the SADC scores higher on synchronicity and finance with
respectively, 0.731 and 0.793. With regards to inflation, the CEN-SAD is the top performer
(0.802).
Table 6 summarizes the scores for each economy and its ranking. In the CEN-SAD,
the top ten performers on all indexes are mostly ECOWAS countries. It is worthwhile to note
that in this REC, we have all fifteen ECOWAS countries. This result is confirmed by the
overall index where with the exception of Chad (10th), the top ten performers are in the
ECOWAS. Cote d’Ivoire, which is leading in top performance, scores high across dimensions
such as trade and mobility. Many ECOWAS countries in the CEN-SAD attain high scores for
mobility. This result can be explained by the fact that to facilitate the free movement of
people in this region, member states established in December 2000 a common passport,
formally known as the ECOWAS travel certificate. Indeed, for the other sub-indexes, with the
exception of synchronicity, countries in the ECOWAS exhibit high levels of integration.
For COMESA countries, Rwanda earns the highest score for the overall index (0.804).
It is followed by Congo Democratic Republic (0.710), Zambia (0.705) and Zimbabwe
(0.675). Madagascar ranks last with a score value of 0.401, far below COMESA’s regional
average. When sub-dimensions are taken on board, it is surprisingly apparent that this country
(i.e. Madagascar), even if it occupies the last position in the overall index, has a good rank in
terms of synchronicity (5th). Paradoxically, Rwanda and Congo Democratic Republic which
are, respectively 1st and 2nd in overall index perform weakly in inflation (11th and 13th,
respectively). In the EAC, Burundi, Rwanda and Uganda are at the top both for sub-indexes
and the overall index. Kenya and Tanzania have the worse rankings.
In the ECCAS, high scores in the overall index are traceable to the Congo Republic,
the Central African Republic, Rwanda and Tanzania. Angola and Equatorial Guinea perform
weakly in the overall index scores even though the latter country is 3rd out of 11 in terms of
financial integration. Within ECOWAS countries, 7 of the top performing that are deeply
integrated (score higher than the average of the community) are in the WAEMU. Burkina
Faso, Niger and Côte d’Ivoire are the top performers. The surprising result is the rank of
Nigeria. Nigeria is the first contributor towards wealth creation in the region (i.e. more than
65% of the regional GDP). Unfortunately, it is positioned at one place to the bottom (14th) in
terms of integration. This finding could call into question the appropriateness of the future
common currency “Eco” in the ECOWAS. It is worth noting that during the 55th Ordinary
Session of the Authority of Heads of State and Government of the ECOWAS, the members
were requested to speed-up the convergence process for a single currency in 2020. Weak
scores are noted for Nigeria specifically with respect to synchronicity, mobility and finance.
The same remarks are observed for other WAMZ countries.
In IGAD, the top performing countries on overall index are South Sudan, Djibouti and
Uganda. Ethiopia, Sudan and Kenya which are the principal contributors in term of GDP are
not in the top five countries with respect to monetary integration. South Sudan and Kenya
score low on all sub-dimensions especially on synchronicity and mobility (0.298 and 0.241,
respectively). Best sub-indexes are in inflation and exchange. Zimbabwe is the top performing
economy on the overall index while Seychelles occupies the last place in the SADC.
Concerning sub-indexes, Zimbabwe has best scores especially in trade and inflation. Lesotho
is first both in synchronicity and finance. Moreover, the country ranks 2nd in the overall index.
In spite of the economic weight of South Africa (i.e. more than 65% of regional GDP), it is
ranked 10th on the overall dimension. Finally, Tunisia, with highest performing scores in
some sub-dimensions (trade and mobility) has the highest overall index. Algeria, the top
contributor of the regional GDP occupies the last place after Mauritania. The worse scores for
Algeria are in the dimensions of mobility, synchronicity and inflation. Libya earns the best
sores in finance and inflation.
4.2 Analysis of the dynamic indexes
Figure 2 presents the dynamic scores = for the overall index and sub-indexes throughout the
sample period. From the graph, the evolution of every REC over time can be appreciated.
Contrary to the ARII (2016) which was static, our approach is more refined by introducing the
dynamic aspect. The advantage of this method is that we can interpret an increase in the index
through time as an improvement of the integration and a decrease as a decline in the
integration of RECs. This comparability also helps to identify the dimensions that are driving
major changes in the composite index for each region across different time periods (Park &
Claveria, 2018).
Many patterns emerge from this figure. Firstly, it shows a fairly high variability of the
sub-indexes especially for synchronicity and trade. EAC and UMA exhibit the highest
volatility of indexes. Secondly, trade and mobility (movement of people) have the lowest
scores for the entire period of analysis while inflation and financial integration show
relatively highest scores. Finally, a broadly steady trend of the overall index is apparent over
time in all RECs with the exception of the EAC and UMA. In effect, the movement of the
overall index is stable over the period 2012-2017. The RECs in Africa are characterized by a
stable monetary integration which is different from the gradual process usually observed in
monetary integration because with the exception of the EAC and UMA, the dynamics of
IAMI show a steady trend in the overall index across time. Furthermore, the figure shows that
the overall index is highest in the ECOWAS and EAC during the sample period.
4.3 Robustness and sensitivity checks
In Table 7, we present the results of a robustness check for our monetary index. To this end,
we consider alternative methods both for normalization and aggregation. The min-max
scaling used is criticized by the fact that extreme values can distort the distribution of
normalized values. To avoid this issue, we consider the softmax method. One of the
advantages of this technique is its ability to reduce the influence of extreme values or outliers.
To further assess the robustness, the weighting method is also changed. Contrary to the PPCA
approach, the same weight is assigned for every dimension of the index. The results do not
change much. Thus we conclude that results are robust to the use of alternatives normalization
and weighting methods.
5. Concluding implications and future research directions
This study improves the African Regional Integration Index (ARII) proposed by the African
Union, the African Development Bank and the United Nations Economic Commission for
Africa by providing a theoretical framework and addressing shortcomings related to
weighting and aggregation of the indicator. This paper measures monetary integration in the
eight African Regional Economic Communities (RECs) by constructing an Index of African
Monetary Integration (IAMI). It proposes an Optimal Currency Area as theoretical framework
and uses a panel approach to appreciate the dynamics of the index over different periods of
time. The findings show that: (i) inflation and finance (trade and mobility) present the highest
(lowest) score while ECOWAS is (EAC and IGAD are) the highest (least) performing. (ii)
Surprisingly, in most RECs, the highest contributors to wealth creation are not the top
performers in regional monetary integration. For instances, Nigeria in ECOWAS, Ethiopia,
Sudan and Kenya in IGAD, South Africa in SADC, Algeria in UMA are not among the top
performers in regional monetary integration. (iii) The RECs in Africa are characterized by a
stable monetary integration which is different from the gradual process usually observed in
monetary integration because with the exception of the EAC and UMA, the dynamics of
IAMI show a steady trend in the overall index across time.
Overall, our results highlight the importance of measuring the monetary integration
process in Africa particularly within a dynamic setting. The main policy implication emerging
from our findings is that deep reforms are needed in the RECs especially in trade and
movement of people in order to reinforce the monetary integration. This policy implication
builds on the fact that the monetary integration is low, stable and not characterised by the
usual gradual process over time. In what follows, some measures that facilitate integration
are discussed.
Regardless of RECs, monetary integration in the assessed dimensions can be improved
by keeping in check some factors that inhibit monetary convergence, inter alia: budget
deficits, government debts and inflation. Furthermore, monetary integration should also be
enhanced by curtailing setbacks to common markets creation that constraint the feasibility of
common currency areas. Some recommendations in these directions are, inter alia: (i) taking
on board adjustment devoted to aligning monetary measures in various RECs; (ii)
consolidating relevant institutional frameworks for the enforcement of fiscal discipline as well
as surveillance at the macroeconomic level; (iii) implementation of reforms at the structural
level that are imperative in reducing policy and infrastructural gaps; (iv) complementing
national currencies with a basket of common currency and (v) construction of a robust
institutional framework for boosting financial, monetary and fiscal stability.
The process of convergence could be further improved by building capacities of data
collection that would facilitate information sharing. Furthermore, the harmonization of
statistics would boost the improvement of skills, knowledge acquisition, competences as well
as the behavior of central bank officials in the various RECs. Furthermore, beyond the need to
tackle these infrastructural issues, boosting awareness campaigns is important in order to
share information and by extension, improve perceptions of the rewards of adopting a
common currency across Africa.
Further studies can assess how to facilitate monetary integration in the light of the
African Continental Free Trade Area (AfCFTA). Moreover, using this new measure of
monetary integration to examine the feasibility of the proposed trade area and a unique
currency for the entire African continent, are worthwhile.
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6. Appendices
Figure 1: Regional Economic Communities in Africa
Table 4: Number of principal components
ECOWAS
Trade integration
Factor mobility
Synchronicity
Financial integration
Inflation diff
Overall
1
2
3
1
2
3
4
1
2
3
1
2
3
1
2
1
2
3
4
5
Eig. val.
1.288
1.058
0.654
3.150
0.599
0.196
0.054
1.549
0.971
0.480
2.168
0.776
0.056
1.327
0.673
2.237
1.172
0.801
0.636
0.154
Prop.
0.429
0.352
0.218
0.787
0.150
0.049
0.140
0.516
0.324
0.160
0.723
0.259
0.019
0.663
0.337
0.447
0.244
0.160
0.127
0.031
Cum
0.429
0.782
1.000
0.787
0.937
0.986
1.000
0.516
0.840
1.000
0.723
0.981
1.000
0.663
1.000
0.447
0.682
0.842
0.969
1.000
Squared loadings
Variables
TI
PE
PI
PMIG
PMIT
MIGD
MIGS
GDPg
GDPp
GDP
CB
CRED
SPRE
DI
DER
TR
MOB
SYN
FIN
INF
F1
0.053
0.412
0.536
0.270
0.301
0.183
0.245
0.050
0.473
0.476
0.429
0.406
0.165
0.500
0.500
0.187
0.251
0.186
0.364
0.011
F2
0.774
0.210
0.016
0.025
0.000
0.685
0.289
0.949
0.029
0.022
0.051
0.120
0.828
0.500
0.500
0.021
0.202
0.001
0.009
0.766
Weights
Weights
0.378
0.321
0.301
0.231
0.253
0.264
0.252
0.396
0.302
0.301
0.330
0.330
0.340
0.500
0.500
0.130
0.234
0.122
0.242
0.270
CEN-SAD
Trade integration
Factor mobility
Synchronicity
Financial integration
Inflation diff
Overall
1
2
3
1
2
3
4
1
2
3
1
2
3
1
2
1
2
3
4
5
Eig.
val.
1.490
0.953
0.557
3.413
0.382
0.155
0.048
1.399
1.014
0.587
1.558
0.896
0.545
1.131
0.869
2.179
1.557
0.533
0.478
0.253
Prop.
0.497
0.318
0.186
0.853
0.096
0.039
0.012
0.466
0.338
0.195
0.519
0.299
0.182
0.566
0.434
0.436
0.311
0.106
0.096
0.051
Cum
0.497
0.814
1.000
0.853
0.949
0.988
1.000
0.466
0.804
1.000
0.519
0.818
1.000
0.566
1.000
0.436
0.747
0.854
0.949
1.000
Squared loadings
Variabl
es
TI
PE
PI
PMIG
PMIT
MIGD
MIGS
GDPg
GDPp
GDP
CB
CRED
SPRE
DI
DER
TR
MOB
SYN
FIN
INF
F1
0.111
0.416
0.469
0.250
0.274
0.221
0.252
0.056
0.508
0.436
0.443
0.187
0.368
0.500
0.500
0.267
0.355
0.088
0.267
0.022
F2
0.852
0.138
0.011
-
-
-
-
0.861
0.001
0.138
0.016
0.753
0.230
0.500
0.500
0.083
0.001
0.402
0.054
0.460
Weights
Weight
s
0.400
0.307
0.290
0.250
0.274
0.221
0.252
0.394
0.295
0.310
0.287
0.394
0.318
0.500
0.500
0.190
0.208
0.219
0.178
0.204
COMESA
Trade integration
Factor mobility
Synchronicity
Financial integration
Inflation diff
Overall
1
2
3
1
2
3
4
1
2
3
1
2
3
1
2
1
2
3
4
5
Eig. val.
1.650
0.709
0.632
3.243
0.516
0.210
0.040
1.025
0.997
0.978
1.421
0.821
0.757
1.014
0.986
2.596
1.009
0.938
0.348
0.108
Prop.
0.553
0.236
0.211
0.809
0.129
0.052
0.010
0.342
0.332
0.326
0.474
0.274
0.252
0.507
0.493
0.519
0.202
0.188
0.007
0.022
Cum
0.553
0.789
1.000
0.809
0.938
0.990
1.000
0.342
0.674
1.000
0.474
0.748
1.000
0.507
1.000
0.519
0.721
0.909
0.978
1.000
Squared loadings
Variables
TI
PE
PI
PMIG
PMIT
MIGD
MIGS
GDPg
GDPp
GDP
CB
CRED
SPRE
DI
DER
TR
MOB
SYN
FIN
INF
F1
0.336
0.309
0.354
0.244
0.290
0.198
0.267
0.142
0.392
0.466
0.309
0.365
0.326
0.500
0.500
0.271
0.271
0.153
0.229
0.074
F2
0.296
0.653
0.051
0.178
0.005
0.672
0.144
0.801
0.191
0.009
0.577
0.009
0.413
0.500
0.500
0.004
0.003
0.000
0.229
0.074
Weights
Weights
0.324
0.412
0.263
0.235
0.251
0.263
0.250
0.467
0.293
0.241
0.407
0.234
0.358
0.500
0.500
0.196
0.196
0.110
0.235
0.261
EAC
Trade integration
Factor mobility
Synchronicity
Financial integration
Inflation diff
Overall
1
2
3
1
2
3
4
1
2
3
1
2
3
1
2
1
2
3
4
5
Eig. val.
1.581
1.329
0.090
2.686
0.926
0.321
0.065
2.094
0.834
0.072
1.492
1.016
0.492
1.034
0.966
3.512
1.488
0.000
0.000
0.000
Prop.
0.527
0.443
0.03à
0.672
0.232
0.080
0.016
0.698
0.278
0.024
0.497
0.339
0.164
0.517
0.483
0.702
0.293
0.000
0.000
0.000
Cum
0.527
0.970
1.000
0.672
0.903
0.984
1.000
0.698
0.976
1.000
0.497
0.836
1.000
0.517
1.000
0.702
0.293
1.000
1.000
1.000
Squared loadings
Variables
TI
PE
PI
PMIG
PMIT
MIGD
MIGS
GDPg
GDPp
GDP
CB
CRED
SPRE
DI
DER
TR
MOB
SYN
FIN
INF
F1
0.518
0.000
0.486
0.281
0.339
0.113
0.267
0.140
0.446
0.413
0.240
0.508
0.252
0.500
0.500
0.170
0.278
0.000
0.284
0.267
F2
0.116
0.733
0.150
0.025
0.005
0.740
0.221
0.846
0.032
0.121
0.511
0.000
0.489
0.500
0.500
0.270
0.017
0.671
0.001
0.041
Weights
Weights
0.335
0.335
0.332
0.215
0.253
0.274
0.255
0.341
0.328
0.330
0.350
0.302
0.348
0.500
0.500
0.200
0.200
0.200
0.200
0.200
ECCAS
Trade integration
Factor mobility
Synchronicity
Financial integration
Inflation diff
Overall
1
2
3
1
2
3
4
1
2
3
1
2
3
1
2
1
2
3
4
5
Eig. val.
1.397
1.104
0.500
1.731
1.400
0.792
0.076
1.36
1.075
0.565
2.250
0.476
0.274
1.118
0.881
2.835
1.888
0.276
0.000
0.000
Prop.
0.466
0.368
0.166
0.433
0.350
0.198
0.019
0.453
0.358
0.188
0.750
0.157
0.091
0.559
0.441
0.567
0.378
0.055
0.000
0.000
Cum
0.466
0.834
1.000
0.433
0.783
0.981
1.000
0.453
0.812
1.000
0.750
0.909
1.000
0.559
1.000
0.567
0.945
1.000
1.000
1.000
Squared loadings
Variables
TI
PE
PI
PMIG
PMIT
MIGD
MIGS
GDPg
GDPp
GDP
CB
CRED
SPRE
DI
DER
TR
MOB
SYN
FIN
INF
F1
0.533
0.021
0.446
0.172
0.424
0.349
0.054
0.389
0.539
0.072
0.361
0.298
0.341
0.500
0.500
0.305
0.310
0.159
0.210
0.017
F2
0.038
0.797
0.166
0.109
0.114
0.193
0.616
0.246
0.012
0.741
0.064
0.677
0.259
0.500
0.500
0.003
0.010
0.284
0.211
0.491
Weights
Weights
0.314
0.364
0.322
0.144
0.285
0.279
0.291
0.326
0.306
0.367
0.309
0.364
0.328
0.500
0.500
0.184
0.190
0.209
0.210
0.206
IGAD
Trade integration
Factor mobility
Synchronicity
Financial integration
Inflation diff
Overall
1
2
3
1
2
3
4
1
2
3
1
2
3
1
2
1
2
3
4
5
Eig. val.
1.432
0.943
0.625
1.957
1.230
0.696
0.117
1.599
0.983
0.418
2.593
0.317
0.09
1.032
0.968
5.000
0.000
0.000
0.000
0.000
Prop.
0.477
0.314
0.209
0.489
0.307
0.174
0.029
0.553
0.328
0.139
0.864
0.106
0.03
0.516
0.484
1.000
0.000
0.000
0.000
0.000
Cum
0.477
0.791
1.000
0.489
0.797
0.971
1.000
0.553
0.861
1.000
0.864
0.97
1.000
0.516
1.000
1.000
1.000
1.000
1.000
1.000
Squared loadings
Variables
TI
PE
PI
PMIG
PMIT
MIGD
MIGS
GDPg
GDPp
GDP
CB
CRED
SPRE
DI
DER
TR
MOB
SYN
FIN
INF
F1
0.244
0.464
0.292
0.280
0.202
0.472
0.046
0.037
0.472
0.491
0.336
0.304
0.359
0.500
0.500
0.200
0.200
0.200
0.200
0.200
F2
0.559
0.000
0.439
0.038
0.377
0.000
0.584
0.952
0.043
0.004
0.298
0.654
0.047
0.500
0.500
0.000
0.000
0.000
0.000
0.000
Weights
Weights
0.370
0.280
0.350
0.187
0.270
0.290
0.253
0.386
0.309
0.306
0.332
0.342
0.325
0.500
0.500
0.200
0.200
0.200
0.200
0.200
SADC
Trade integration
Factor mobility
Synchronicity
Financial integration
Inflation diff
Overall
1
2
3
1
2
3
4
1
2
3
1
2
3
1
2
1
2
3
4
5
Eig. val.
1.836
0.883
0.280
3.356
0.452
0.163
0.028
1.314
0.905
0.781
1.597
0.799
0.604
1.108
0.892
2.607
1.590
0.440
0.252
0.110
Prop.
0.612
0.294
0.093
0.839
0.113
0.041
0.007
0.438
0.302
0.260
0.532
0.266
0.201
0.554
0.446
0.521
0.318
0.088
0.005
0.022
Cum
0.612
0.907
1.000
0.839
0.952
0.993
1.000
0.438
0.740
1.000
0.532
0.799
1.000
0.554
1.000
0.521
0.839
0.927
0.978
1.000
Squared loadings
Variables
TI
PE
PI
PMIG
PMIT
MIGD
MIGS
GDPg
GDPp
GDP
CB
CRED
SPRE
DI
DER
TR
MOB
SYN
FIN
INF
F1
0.132
0.451
0.416
0.196
0.282
0.242
0.276
0.383
0.232
0.384
0.305
0.399
0.300
0.500
0.500
0.321
0.347
0.013
0.234
0.085
F2
0.852
0.028
0.119
0.724
0.028
0.240
0.008
0.120
0.767
0.111
0.480
0.000
0.518
0.500
0.500
0.001
0.006
0.476
0.117
0.401
Weights
Weights
0.366
0.314
0.319
0.259
0.255
0.242
0.244
0.276
0.450
0.273
0.363
0.266
0.373
0.500
0.500
0.200
0.218
0.188
0.190
0.204
UMA
Trade integration
Factor mobility
Synchronicity
Financial integration
Inflation diff
Overall
1
2
3
1
2
3
4
1
2
3
1
2
3
1
2
1
2
3
4
5
Eig. val.
1.966
1.002
0.033
2.165
1.028
0.672
0.135
1.288
1.002
0.710
1.288
1.921
0.848
1.407
0.592
3.469
1.530
0.000
0.000
0.000
Prop.
0.655
0.334
0.011
0.541
0.257
0.168
0.034
0.429
0.334
0.237
0.640
0.283
0.077
0.704
0.296
0.694
0.306
0.000
0.000
0.000
Cum
0.655
0.989
1.000
0.541
0.798
0.966
1.000
0.429
0.763
1.000
0.640
0.923
1.000
0.704
1.000
0.694
1.000
1.000
1.000
1.000
Squared loadings
Variables
TI
PE
PI
PMIG
PMIT
MIGD
MIGS
GDPg
GDPp
GDP
CB
CRED
SPRE
DI
DER
TR
MOB
SYN
FIN
INF
F1
0.434
0.500
0.065
0.279
0.140
0.301
0.280
0.091
0.407
0.503
0.410
0.442
0.148
0.500
0.500
0.229
0.260
0.274
0.001
0.234
F2
0.132
0.000
0.867
0.088
0.581
0.253
0.078
0.812
0.188
0.000
0.125
0.036
0.839
0.500
0.500
0.133
0.063
0.031
0.650
0.121
Weights
Weights
0.332
0.331
0.336
0.217
0.282
0.286
0.215
0.406
0.311
0.283
0.322
0.318
0.360
0.500
0.500
0.200
0.200
0.200
0.200
0.200
Table 5: sub-indexes and overall index 2012-2017 (average)
RECs
Trade
Mobility
Synchronicity
Finance
Inflation
Overall
Rank
CEN-SAD
0.212
0.510
0.698
0.686
0.802
0.589
4
COMESA
0.336
0.321
0.683
0.777
0.774
0.588
5
EAC
0.478
0.486
0.519
0.569
0.513
0.513
8
ECCAS
0.257
0.427
0.676
0.532
0.658
0.617
3
ECOWAS
0.294
0.688
0.706
0.781
0.769
0.672
1
IGAD
0.341
0.431
0.497
0.516
0.761
0.508
7
SADC
0.316
0.522
0.731
0.793
0.785
0.618
2
UMA
0.421
0.405
0.521
0.743
0.644
0.547
6
Average
0.332
0.474
0.629
0.675
0.713
0.581
-
Table 6: Economy rankings
CEN-SAD
Countries
Trade
Rank
Mobility
Rank
Synchro
Rank
Finance
Rank
Inflation
Rank
Overall
Rank
Benin
0.270
9
0.871
4
0.736
14
0.863
3
0.902
3
0.735
5
Burkina Faso
0.346
7
0.980
1
0.735
15
0.850
4
0.900
4
0.767
2
Cabo Verde
0.017
27
0.195
23
0.665
22
0.263
29
0.759
20
0.404
28
Central Afr
0.046
25
0.357
20
0.816
3
0.686
12
0.741
24
0.537
18
Chad
0.071
24
0.730
10
0.791
5
0.686
12
0.826
16
0.644
10
Comoros
0.012
28
0.064
27
0.772
9
0.726
10
0.894
6
0.497
22
Cote d'Ivoire
0.655
1
0.917
3
0.648
23
0.824
6
0.887
10
0.787
1
Djibouti
0.139
19
0.479
16
0.678
20
0.686
12
0.855
14
0.577
16
Egypt
0.217
12
0.052
28
0.453
28
0.513
26
0.716
26
0.392
29
Eritrea
0.073
23
0.248
21
0.833
1
0.686
12
0.887
8
0.578
17
Gambia
0.473
3
0.647
13
0.782
6
0.473
27
0.755
22
0.641
9
Ghana
0.238
11
0.666
12
0.685
19
0.686
12
0.723
25
0.604
14
Guinea
0.143
18
0.739
9
0.701
18
0.686
12
0.413
29
0.531
19
Guinea-Biss
0.187
14
0.643
14
0.758
10
0.875
2
0.870
13
0.668
7
Kenya
0.114
21
0.216
22
0.667
21
0.648
23
0.795
19
0.488
23
Liberia
0.027
26
0.758
8
0.798
4
0.575
24
0.758
21
0.601
15
Libya
0.161
16
0.188
24
0.474
27
0.817
8
0.907
2
0.507
21
Mali
0.551
2
0.729
11
0.727
16
0.836
5
0.898
5
0.750
4
Mauritania
0.087
22
0.771
7
0.740
13
0.521
25
0.807
17
0.606
13
Morocco
0.190
13
0.021
29
0.602
26
0.686
12
0.871
12
0.478
26
Niger
0.266
10
0.933
2
0.745
11
0.900
1
0.916
1
0.755
3
Nigeria
0.359
6
0.418
18
0.390
29
0.698
11
0.752
23
0.514
20
Sao Tome
0.006
29
0.140
25
0.780
7
0.360
28
0.794
18
0.437
27
Senegal
0.399
4
0.451
17
0.708
17
0.818
7
0.894
7
0.655
8
Sierra Leone
0.272
8
0.824
5
0.777
8
0.686
12
0.569
28
0.627
11
Somalia
0.123
20
0.510
15
0.831
2
0.686
12
0.887
8
0.621
12
Sudan
0.167
15
0.369
19
0.624
24
0.686
12
0.585
27
0.483
24
Togo
0.384
5
0.797
6
0.742
12
0.784
9
0.874
11
0.722
6
Tunisia
0.158
17
0.083
26
0.617
25
0.686
12
0.833
15
0.479
25
COMESA
Countries
Trade
Rank
Mobility
Rank
Synchro
Rank
Finance
Rank
Inflation
Rank
Overall
Rank
Burundi
0.499
6
0.659
2
0.832
1
0.777
9
0.595
16
0.649
6
Comoros
0.178
13
0.422
7
0.786
2
0.884
4
0.927
2
0.654
5
Congo Dem.
0.654
4
0.580
4
0.707
9
0.816
8
0.769
13
0.710
2
Djibouti
0.095
17
0.317
9
0.688
12
0.777
9
0.861
6
0.567
12
Egypt
0.197
12
0.017
17
0.473
18
0.757
16
0.760
14
0.470
16
Eritrea
0.083
18
0.581
3
0.683
13
0.777
9
0.911
4
0.632
7
Ethiopia
0.312
10
0.143
13
0.595
16
0.777
9
0.748
15
0.531
15
Kenya
0.516
5
0.193
12
0.663
14
0.830
7
0.815
8
0.617
10
Libya
0.154
14
0.085
16
0.640
15
0.895
3
0.919
3
0.568
11
Madagascar
0.095
16
0.132
14
0.764
5
0.573
18
0.532
17
0.401
19
Malawi
0.287
11
0.435
6
0.769
3
0.777
9
0.529
18
0.536
14
Mauritius
0.152
15
0.014
18
0.573
17
0.604
17
0.821
7
0.460
17
Rwanda
0.686
1
0.977
1
0.700
10
0.850
6
0.786
11
0.804
1
Seychelles
0.053
19
0.101
15
0.469
19
0.445
19
0.771
12
0.401
18
Sudan
0.319
9
0.272
10
0.693
11
0.777
9
0.911
4
0.618
9
Swaziland
0.337
8
0.000
19
0.725
7
0.777
9
0.814
9
0.541
13
Uganda
0.667
3
0.538
5
0.728
6
0.868
5
0.489
19
0.630
8
Zambia
0.682
2
0.387
8
0.721
8
0.904
1
0.806
10
0.705
3
Zimbabwe
0.427
7
0.245
11
0.768
4
0.896
2
0.940
1
0.675
4
EAC
Countries
Trade
Rank
Mobility
Rank
Synchro
Rank
Finance
Rank
Inflation
Rank
Overall
Rank
Burundi
0.315
4
0.874
1
0.968
1
0.569
3
0.407
4
0.627
1
Kenya
0.449
3
0.057
5
0.142
5
0.293
5
0.577
2
0.304
5
Rwanda
0.758
1
0.567
3
0.548
3
0.414
4
0.771
1
0.612
2
Tanzania
0.208
5
0.623
2
0.304
4
0.960
1
0.453
3
0.510
4
Uganda
0.659
2
0.309
4
0.636
2
0.608
2
0.355
5
0.513
3
ECCAS
Countries
Trade
Rank
Mobility
Rank
Synchro
Rank
Finance
Rank
Inflation
Rank
Overall
Rank
Angola
0.002
11
0.287
8
0.335
11
0.430
10
0.613
8
0.341
11
Burundi
0.305
3
0.427
6
0.826
1
0.532
3
0.306
11
0.485
8
Cameroon
0.229
6
0.314
7
0.627
8
0.532
3
0.731
4
0.495
6
Central Afr
0.258
5
0.824
1
0.829
2
0.532
3
0.571
10
0.607
2
Chad
0.206
7
0.228
11
0.744
6
0.532
3
0.698
6
0.492
7
Congo Dem.
0.195
8
0.673
2
0.600
9
0.532
3
0.609
9
0.526
4
Congo Rep.
0.758
1
0.424
5
0.757
4
0.929
1
0.748
2
0.728
1
Equa Gui
0.151
9
0.287
9
0.548
10
0.532
3
0.711
5
0.455
10
Gabon
0.093
10
0.228
10
0.746
5
0.532
3
0.741
3
0.481
9
Rwanda
0.295
4
0.525
3
0.646
7
0.632
2
0.659
7
0.558
3
Sao Tome
0.338
2
0.477
4
0.782
3
0.137
11
0.856
1
0.521
5
ECOWAS
Countries
Trade
Rank
Mobility
Rank
Synchro
Rank
Finance
Rank
Inflation
Rank
Overall
Rank
Benin
0.231
10
0.827
5
0.743
9
0.947
3
0.908
6
0.789
4
Burkina Faso
0.349
6
0.978
1
0.757
7
0.926
4
0.914
4
0.838
1
Cabo Verde
0.005
15
-
0.576
13
0.153
15
0.820
9
0.329
15
Cote d'Ivoire
0.625
1
0.894
3
0.570
14
0.892
6
0.900
7
0.819
3
Gambia
0.495
3
0.634
9
0.859
2
0.494
14
0.742
10
0.637
10
Ghana
0.208
11
0.631
10
0.642
12
0.782
9
0.583
13
0.600
12
Guinea
0.126
13
0.833
4
0.699
11
0.782
9
0.243
15
0.551
13
Guinea-Biss
0.186
12
0.581
11
0.824
3
0.962
2
0.921
3
0.742
7
Liberia
0.020
14
0.753
7
0.862
1
0.691
13
0.714
11
0.644
8
Mali
0.560
2
0.558
12
0.762
5
0.913
5
0.909
5
0.763
5
Niger
0.272
9
0.897
2
0.757
6
0.998
1
0.935
1
0.832
2
Nigeria
0.293
7
0.329
13
0.322
15
0.718
12
0.650
12
0.504
14
Senegal
0.370
5
0.187
14
0.705
10
0.884
7
0.923
2
0.641
9
Sierra Leone
0.287
8
0.818
6
0.748
8
0.749
11
0.493
14
0.634
11
Togo
0.381
4
0.717
8
0.767
4
0.832
8
0.886
8
0.751
6
IGAD
Countries
Trade
Rank
Mobility
Rank
Synchro
Rank
Finance
Rank
Inflation
Rank
Overall
Rank
Djibouti
0.394
3
0.686
2
0.504
3
0.516
3
0.862
4
0.592
2
Eritrea
0.092
7
0.344
6
0.497
4
0.516
3
0.915
2
0.473
5
Ethiopia
0.351
5
0.399
3
0.342
7
0.516
3
0.741
6
0.470
6
Kenya
0.343
6
0.241
8
0.389
6
0.331
8
0.758
5
0.412
7
Somalia
0.379
4
0.380
4
0.497
4
0.516
3
0.915
2
0.538
4
South Sudan
0.424
2
0.726
1
0.876
1
0.586
2
0.927
1
0.697
1
Sudan
0.088
8
0.298
7
0.224
8
0.516
3
0.502
7
0.326
8
Uganda
0.658
1
0.373
5
0.645
2
0.632
1
0.466
8
0.555
3
SADC
countries
Trade
Rank
Mobility
Rank
Sync
Rank
Finance
Rank
Inflation
Rank
Overall
Rank
Angola
0.081
12
0.421
9
0.709
11
0.810
8
0.730
12
0.544
11
Botswana
0.513
3
0.800
3
0.642
12
0.894
4
0.886
4
0.748
4
Congo Dem.
0.299
8
0.170
11
0.774
8
0.866
6
0.768
11
0.563
9
Lesotho
0.496
4
0.800
2
0.897
1
0.925
1
0.890
3
0.800
2
Madagascar
0.052
13
-
-
0.881
2
0.586
13
0.502
15
0.390
14
Malawi
0.272
10
0.775
5
0.873
3
0.793
9
0.508
14
0.642
8
Mauritius
0.034
14
0.054
14
0.573
13
0.585
14
0.869
5
0.415
13
Mozambique
0.281
9
0.738
6
0.800
7
0.860
7
0.898
2
0.714
7
Namibia
0.599
2
0.779
4
0.709
10
0.792
11
0.862
6
0.749
3
Seychelles
0.007
15
0.104
13
0.409
15
0.502
15
0.845
8
0.369
15
South Africa
0.460
5
0.251
10
0.478
14
0.756
12
0.854
7
0.554
10
Swaziland
0.316
7
0.859
1
0.807
5
0.793
9
0.844
9
0.725
6
Tanzania
0.138
11
0.151
12
0.749
9
0.915
3
0.543
13
0.486
12
Zambia
0.442
6
0.680
8
0.805
6
0.925
2
0.824
10
0.732
5
Zimbabwe
0.752
1
0.730
7
0.864
4
0.893
5
0.962
1
0.838
1
UMA
countries
Trade
Rank
Mobility
Rank
Synchro
Rank
Finance
Rank
Inflation
rank
Overall
Rank
Algeria
0.380
2
0.281
4
0.281
5
0.840
2
0.230
5
0.402
5
Libya
0.195
5
0.585
2
0.485
4
0.881
1
0.913
1
0.612
2
Mauritania
0.225
4
0.338
3
0.754
1
0.507
5
0.598
4
0.484
4
Morocco
0.347
3
0.220
5
0.506
3
0.743
3
0.865
2
0.536
3
Tunisia
0.960
1
0.603
1
0.577
2
0.743
3
0.614
3
0.699
1
Sources: authors
Figure 2: Indexes evolution over time
Sources: authors
Table 7: sub-indexes and overall index 2012-2017 (average) with softmax normalization
Sources: authors
RECs
Trade
Mobility
Synchronicity
Finance
Inflation
Overall
Rank
CEN-SAD
0.483
0.506
0.514
0.529
0.525
0.588
3
COMESA
0.489
0.488
0.511
0.522
0.519
0.587
4
EAC
0.498
0.503
0.501
0.519
0.503
0.513
7
ECCAS
0.483
0.494
0.512
0.494
0.511
0.517
6
ECOWAS
0.487
0.513
0.515
0.514
0.517
0.672
1
IGAD
0.491
0.483
0.457
0.501
0.522
0.508
8
SADC
0.500
0.497
0.519
0.518
0.523
0.623
2
UMA
0.495
0.494
0.502
0.618
0.526
0.547
5
Average
0.491
0.497
0.504
0.527
0.518
0.570
-
