Description
A major lesson of the European Monetary Union (EMU) crisis is that serious disequilibria
result from regional monetary arrangements not designed to be robust to a variety of shocks. The
purpose of this paper is to assess these disequilibria within the Economic and Monetary Community of
Central Africa (CEMAC), West African Economic and Monetary Union (UEMOA) and Financial
Community of Africa (CFA) zones.
Journal of Financial Economic Policy
Real and monetary policy convergence: EMU crisis to the CFA zone
Simplice A. Asongu
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To cite this document:
Simplice A. Asongu, (2013),"Real and monetary policy convergence: EMU crisis to the CFA zone", J ournal
of Financial Economic Policy, Vol. 5 Iss 1 pp. 20 - 38
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Simplice A. Asongu, (2013),"Fighting consumer price inflation in Africa: What do dynamics in money,
credit, efficiency and size tell us?", J ournal of Financial Economic Policy, Vol. 5 Iss 1 pp. 39-60 http://
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Real and monetary policy
convergence: EMU crisis to the
CFA zone
Simplice A. Asongu
HEC-Management School, University of Lie `ge, Lie `ge, Belgium
Abstract
Purpose – A major lesson of the European Monetary Union (EMU) crisis is that serious disequilibria
result from regional monetary arrangements not designed to be robust to a variety of shocks. The
purpose of this paper is to assess these disequilibria within the Economic and Monetary Community of
Central Africa (CEMAC), West African Economic and Monetary Union (UEMOA) and Financial
Community of Africa (CFA) zones.
Design/methodology/approach – In the assessments, monetary policy targets in?ation and
?nancial dynamics of depth, ef?ciency, activity and size while real sector policy targets economic
performance in terms of GDP growth. The author also provides the speed of convergence and time
required to achieve a 100 percent convergence.
Findings – But for ?nancial intermediary size within the CFA zone, ?ndings, for the most part,
support only unconditional convergence. There is no form of convergence within the CEMAC zone.
Practical implications – The broad insigni?cance of conditional convergence results has
substantial policy implications. Monetary and real policies, which are often homogenous for member
states, are thwarted by heterogeneous structural and institutional characteristics, which give rise to
different levels and patterns of ?nancial intermediary development. Therefore, member states should
work towards harmonizing cross-country differences in structural and institutional characteristics that
hamper the effectiveness of monetary policies.
Originality/value – The paper provides warning signs to the CFA zone in the heat of the Euro zone
crises.
Keywords Africa, Monetary policy, National economy, Economic disequilibrium,
Financial Community of Africa, Economic and Monetary Community of Central Africa, Currency area,
Convergence, Policy coordination
Paper type Research paper
1. Introduction
The European Monetary Union (EMU) crisis has sent a strong signal to other common
currency regions on the goals of real and monetary policy convergence. A major lesson
of the EMU crisis is that serious disequilibria result from regional monetary
arrangements not designed to be robust to a variety of shocks (Willett, 2011; Willett
and Srisorn, 2011). In designing the euro zone, institutions’ almost exclusive concern
was placed on limiting crises caused by ?nancial sectors. The of?cial position of the
German Government today appears to remain that failure of these safeguards is the
predominant cause of the crisis. This position can be reasonably argued for Greece,
although its loss of competitiveness has also been a major cause for worry.
In this paper we examine the nature of real and monetary policy convergence within
the Financial Community of Africa (CFA) zone. The work contributes to the discussion
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – F15, F36, F42, O55, P52
Journal of Financial Economic Policy
Vol. 5 No. 1, 2013
pp. 20-38
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381311317763
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of ?nancial integration in common currency unions. By dissecting elements of
macroeconomic and monetary policies into ?nancial intermediary dynamics (of depth,
ef?ciency, activityand size), in?ation andeconomic performance, we provide anin depth
picture of real and monetary convergence analysis within the Economic and Monetary
Community of Central Africa (CEMAC), West African Economic and Monetary Union
(UEMOA) and CFA zones. In a bid to provide more signi?cant policy implications,
we also calculate the speed of convergence and the time needed for full convergence.
Beside the premise of the EMU crisis, the absence of studies that focus on ?nancial
convergence in the African continent constitute another motivation for this work.
Although a number of papers have investigated the dynamic co-movements of ?nancial
markets worldwide, the emphasis has often been on developed markets and the emerging
economies of Latin America and Asia. According to Alagidede (2008), such neglect is far
fromsurprising as African?nancial markets are viewed as too riskyand less developed in
operating institutional environments. Economic instability and political strife have
plagued many African countries and continue to pose a signi?cant deterrent to foreign
investments and private capital ?ows (Kenyan post elections crises in 2007/2008,
Zimbabwe’s economic meltdown, Nigeria’s marred transition in 2008, the unending
Egyptian revolution and resurfacing religious tensions in Nigeria). But for South Africa,
no African country has yet risen as an emerging economic power. This might partly
explain the relative lack of academic research on the banking sector of the continent.
However, recently Africa has witnessed signi?cant economic and ?nancial developments.
This provides some basis for assessing multidimensional ?nancial convergence;
especially in single currency areas amid pending EMU crisis.
The rest of the paper is structured in the following manner. Section 2 outlines the
motivations for assessing convergence within the CFA zone. Section 3 presents data
and discusses the methodology. Empirical analysis, discussion and policy implications
are covered by Section 4. Section 5 concludes.
2. Motivations for ?nancial system convergence
Financial and economic integration in the CFAzone is expected to provide gains in growth
by favoring a breeding atmosphere for competition and ef?ciency in the banking sector.
These gains emanate fromprice reductions in ?nancial services leading to direct gains for
consumers and indirect bene?ts through the reduction of loaning rate that favor
investment (Weill, 2009). Investigating ?nancial intermediary convergence is therefore
relevant inthe Africancontinent. More so, ?nancial integrationandconvergence are crucial
in assessing the outcome of deregulation policies aimed at improving the performance and
ef?ciency of the ?nancial intermediary sector (Casu and Girardone, 2010).
Financial theory deems integrated markets to be relatively more ef?cient in
comparison to divergent ones. An integrated ?nancial intermediary market improves
cross-border ?ow of funds, stimulates trading volume which in-turn improves stock
market liquidity. Integrated banking markets provide investors with the opportunity to
ef?ciently allocate capital to economic operators (Chen et al., 2002). The resulting effect is
lower cost of capital for ?rms and lower transaction costs for investors (Kim et al., 2005).
An integrated banking market has the positive rewards to ?nancial stability as
it diminishes the probability of asymmetric shocks (Umutlu et al., 2010). Financial
intermediation stability in-turn could mitigate the risk of cross-border ?nancial contagion
(Beine et al., 2010) and improve the capacityof economies to absorbshocks (Yuet al., 2010).
Real and
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The ?nancial system exerts a signi?cant in?uence on modern economic literature
debates (Scholtens and Naaborg, 2005). First, the monetary policy transmission
mechanism’s effectiveness is contingent on the ?nancial system(Bondt, 2000). Second, it
is believed to impact channels via which ?nancial development is linked to economic
growth (Allen and Gale, 2000). The ?nancial system interacts with the economy by
producing information ex ante about possible investments, monitoring of investments,
allocation of capital, facilitating trade, diversifying and managing risks, mobilizing and
pooling savings as well as easing the exchange of goods and services (Levine, 2004).
The need for convergence in the banking sector of the CFA zone draws on the tenets
of arbitrage and the hypothesis proffered by the portfolio theory to devise a framework
that inspires convergence in ?nancial markets. The motivations for convergence in
banking markets has premises on the literature of ?nancial intermediary sector
interdependence and portfolio diversi?cation (Grubel, 1968; Levy and Sarnat, 1970).
These papers have for the most part considered short-term links of stock markets and
have found the existence of short-term ?nancial market co-movements. These results
have been extended to cover co-variations of ?nancial markets over the long-run
(Bessler and Yang, 2003). Majority of these papers have shown evidence of cointegration
as well as short-termlinks which depict some formof convergence in ?nancial markets.
Dynamics of ?nancial intermediation therefore converge to re?ect the level of
arbitrage activity. When they converge, it means there is a common force such as
arbitrage activity that brings the markets together. This implies that convergence in
markets will reduce the potential for making above normal pro?ts through
international diversi?cation (Von Furstenberg and Jeon, 1989). By the same token, if
deterrents or potential barriers generating country risks and exchange rate premiums
are absent, the consequence is similar yields for ?nancial assets of similar risk and
liquidity regardless of locality and nationality (Von Furstenberg and Jeon, 1989).
Within the framework of this paper, real and monetary policy convergence implies
the integration of banking sector dynamics of depth, ef?ciency, activity and size; as
well as in?ation and GDP growth.
3. Data and methodology
3.1 Data
We examine a sample of 11 Central and West African countries with data from African
Development Indicators (ADI) and the ?nancial development and structure database
(FDSD) of the World Bank. While openness, in?ation, population growth, public
investment and GDP growth indicators are obtained from the former source, ?nancial
intermediary dynamics are fetched from the later. Owing to constraints in data
availability, dataset spans from 1981 to 2009. More information on summary statistics
(Appendix 1), correlation analysis (Appendix 2), variable de?nitions (Appendix 3) and
presentation of countries (Appendix 4) is available in the appendices. Concurring with
Narayan et al. (2011) it is unlikely to ?nd convergence within a very heterogeneous set
of countries. Thus, we disaggregate the CFA zone into two homogenous panels based
on regions (CEMAC and UEMOA). The choice of variables is premised on two facts:
(1) real economic sector policies are designed to achieve macroeconomic
performance through growth in GDP; and
(2) monetary policies are designed to keep in?ation in check and improve
?nancial intermediary dynamics of depth (money supply and liquid liabilities),
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ef?ciency (at banking and ?nancial levels), activity (from banking and ?nancial
perspectives) and size.
For clarity in presentation, selected variables are classi?ed into the following strands.
3.1.1 Financial variables.
(a) Financial depth. Borrowing from recent ?nance literature (Asongu, 2011a, b, c, e)
and the FDSD, we measure ?nancial depth both from overall-economic and ?nancial
system perspectives with indicators of broad money supply (M2/GDP) and ?nancial
system deposits (Fdgdp), respectively. The former represents the monetary base plus
demand, saving and time deposits while the later denotes liquid liabilities. The basis
for distinguishing these aspects of ?nancial depth is that we are dealing exclusively
with developing countries, where-in a signi?cant chunk of the monetary base does not
transit through the banking sector (Asongu, 2011d). Both measures are in ratios of
GDP (see Appendix 3) and can robustly cross-check one another as either account for
over 89 percent of variability in the other (see Appendix 2).
(b) Financial ef?ciency. Financial intermediation ef?ciency here neither refers to the
production ef?ciency of decision making units nor to the pro?tability-oriented concept
in the banking industry. What this paper elicits is the ability of banks to effectively
meet their fundamental role of transforming mobilized deposits into credit for
economic operators. We therefore employ proxies for banking-system-ef?ciency and
?nancial-system-ef?ciency (respectively “bank credit on bank deposits: Bcbd” and
“?nancial system credit on ?nancial system deposits: Fcfd”). Like with ?nancial depth,
these two intermediation ef?ciency proxies can cross-check each other as either
represent more than 92 percent of variability in the other (see Appendix 2).
(c) Financial size. In line with the FDSD we measure ?nancial intermediary size as
the ratio of “deposit bank assets” to the “total assets” (deposit bank assets on central
bank assets plus deposit bank assets: Dbacba).
(d) Financial activity. The concept of ?nancial intermediary activity here highlights
the ability of banks to grant credit to economic operators. In a bid for robustness we
proxy for both banking intermediary activity and ?nancial intermediary activity with
“private domestic credit by deposit banks: Pcrb” and “private credit by domestic banks
and other ?nancial institutions: Pcrbof”, respectively. The later measure cross-checks
the former as it accounts for more than 99 percent of information in the former
(see Appendix 2).
3.1.2 Other variables. In accordance with the convergence literature we also measure
the outcome of monetary policy with in?ation (Bruno et al., 2012), account for macro
economic performance with GDP growth rate and control for openness, public investment
and population growth in the regressions (Prichett, 1997; Bruno et al., 2012; Narayan et al.,
2011). In the literature on convergence of per capita incomes and the root of the
convergence theory, the premise is that per capita incomes of countries identical in
structural characteristics such as preferences in technologies, rate of population growth
and government policies have the tendency to converge to one another if the countries
share similar fundamental characteristics (Prichett, 1997). In determining these structural
characteristics in our analysis, we proxy for preferences in technology, population growth
and government policy with openness (trade), population growth rate and public
investment, respectively. As concerns common fundamental characteristics, we assume
countries share similar monetary and real policies owing to common central banks.
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3.2 Methodology
According to Fung (2009, p. 3) the two equations below are the standard approaches in
the literature for investigating conditional convergence if W
i,t
is taken as strictly
exogenous:
lnðY
i;t
Þ 2lnðY
i;t2t
Þ ¼ blnðY
i;t2t
Þ þ dW
i;t2t
þ h
i
þ j
t
þ 1
i;t
ð1Þ
lnðY
i;t
Þ ¼ slnðY
i;t2t
Þ þ dW
i;t2t
þ h
i
þ j
t
þ 1
i;t
ð2Þ
where s ¼ 1 þ b, Y
i,t
is the proxy for per capita ?nancial or real sector development in
country i at period t. W
i,t
is a vector of determinants (openness, public investment and
population growth) of per capita ?nance (or real sector per capita), h
i
is a country
speci?c effect, j
t
is a time speci?c constant and 1
i,t
an error term. In line with the
neo-classical growth model, a statistically signi?cant negative coef?cient on b in
equation (1) means that countries relatively close to their steady state of per capita
growth will experience a slowdown in growth of the per capita banking (real sector)
development, known as conditional convergence (Narayan et al., 2011, p. 2). Borrowing
from Fung (2009, p. 3), if 0 , jsj , 1 in equation (2), then Y
i,t
is dynamically stable
around the path with a trend growth rate the same as that of W
t
, and with a height
relative to the level of W
t
. The variables contained in W
i,t2t
and the individual effect h
i
are measures of the long-term level the market (real sector) is converging to. Thus, the
country speci?c effect h
i
depicts the existence of other determinants of a country’s
steady state not captured by W
i,t2t
.
Conditions for convergence elucidated above are contingent on the strict exogeneity
of W
i,t
. Unfortunately, this is not the case in the real world because, whereas openness,
public investment and population growth (components of W
i,t
) in?uence per capita
?nancial (real sector) development, the reverse effect cannot be ruled-out. Thus, we are
confronted with the issue of endogeneity where openness (trade), public investment
and population growth are correlated with the error term (1
i,t
). More so country and
time speci?c effects could be correlated with other variables in the model, which is
often the case when lagged dependent variables are included in the equations. Away of
dealing with the problem of the correlation between the individual speci?c-effect and
the lagged dependent variables involves getting rid of the individual effect by ?rst
differencing. Thus, equation (2) becomes:
lnðY
i;t
Þ 2lnðY
i;t2t
Þ ¼ slnðY
i;t2t
2Y
i;t22t
Þ þ dðW
i;t2t
2W
i;t22t
Þ þ ð1
i;t
21
i;t2t
Þ
ð3Þ
Even with this individual ?xed effect elimination, estimation by ordinary least square
(OLS) is still biased because there remains a correlation between the lagged endogenous
independent variable and the disturbance term. Arellano and Bond (1991) proposed an
application of the generalized method of moments (GMM) that exploits all the
orthogonality conditions between the dependent lagged variables and the error term. This
GMM approach has been extensively used in the convergence literature and recently
applied by Narayan et al. (2011). While Narayan et al. (2011) use equation (1) without ?xed
effects, this paper applies equation (3) instead; as de?ned by Fung (2009). We prefer the
second-step GMMbecause it corrects the residuals for heteroscedasticity. In the ?rst-step
the residuals are considered to be homoscedastic. The hypothesis of no auto-correlation in
residuals is crucial as past lagged variables are to be used as instruments for the
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dependent variables. Also the estimation depends on the assumption that the lagged
values of the outcome variable and other explaining variables are valid instruments in the
regression. When the error terms of the level equation are not auto-correlated, the
?rst-order auto-correlation of the differenced residuals should be signi?cant while their
second-order auto-correlation should not be the case. The validity of the instruments is
investigated by virtue of the Sargan over-identifying restrictions test (OIR).
As emphasized by Islam (1995, p. 14), annual time spans are too short to be
convenient for studying convergence, as short run disturbances may loomsubstantially
in such brief time spans. Therefore, with respect to our data span of 28 years (yrs), we
borrow from Narayan et al. (2011) in using a four-year non-overlapping interval such
that we have seven time intervals: 1982-1985; 1986-1989 and so on. This implies in the
analysis, the autoregressive order t is set to 4.
We also compute the impliedrate of convergence bycalculating(s/4) whichbyvirtue of
equations (1) and (2) is the equivalent of the Narayan et al. (2011) computation with
(1 þ b)/4. Thus, the paper divides the estimated coef?cient of the lagged differenced
endogenous variable by 4 because we have used a four-year non-overlapping span in a
bid to absorb short-term disturbances. When the absolute value of the estimated
autoregressive coef?cient is greater than zero but less than one (0,jsj,1), we conclude
the presence of convergence. A broader interpretation suggests, past differences have a
less proportionate impact onfuture differences, meaningthe variationonthe left handside
of equation (3) is diminishing overtime as the country is converging to a stable state.
4. Empirical analysis
This section addresses three main issues:
(1) assessment of the presence of convergence;
(2) determination of the speed of convergence; and
(3) computation of the time needed for full (100 percent) convergence.
Table I presents a summary of overall ?ndings and addresses the ?rst two issues,
while Tables II and III, respectively, present results for unconditional and conditional
convergence.
Unconditional (absolute) convergence is estimated with only the lagged difference of
the endogenous variable as exogenous variable while conditional convergence is in
respect of equation (3). Thus, unconditional convergence is estimated without W
i,t
: vector
of determinants (openness, public investment and population growth) of per capita
?nance (or real sector per capita). To investigate the validity of the model and indeed the
convergence hypothesis, we carry-out two tests, namely the Sargan test which examines
the over-identi?cation restrictions, and the Arellano and Bond test for autocorrelation
which assesses the null hypothesis of no autocorrelation. The Sargan test investigates
whether the instruments are uncorrelated with the error term in the estimated equation.
Its null hypothesis is the stance that the instruments as a group are strictly exogenous
(do not suffer from endogeneity), which is needed for the validity of the GMM estimates.
We also report the Wald statistics for the joint signi?cance of estimated coef?cients.
The autocorrelation, Sargan and Wald tests statistics with associated p-values for each of
the panels are reported in the tables. The Sargan test statistics often appear with a p-value
.0.10, hence its null hypothesis is not rejected for the most part. We onlyreport the AR(2)
test in?rst difference because it is more important thanAR(1) as it detects autocorrelation
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in levels. For most estimated models we are unable to reject the AR (2) null hypothesis of
no autocorrelation. There is therefore robust evidence that most of the models are free
from autocorrelation at the 1 percent signi?cance level.
4.1 Synthesis of results
In Table I, we provide a summary of our results. This summary results is based on
details presented in Tables II and III. AC, CC, SAC, SCC; represent absolute convergence,
conditional convergence, speed of absolute convergence and speed of conditional
convergence, respectively. We notice that for the three panels, with respect to all
dynamics only ?nancial size within the CFA zone re?ects a conditional convergence.
There is no form of convergence within the CEMAC zone. The UEMOA and CFA zones
both re?ect absolute convergence in liquid liabilities, banking activity, ?nancial activity
and ?nancial size. There is the absence of any form of convergence within any zone for
in?ation and GDP growth regressions. On a speci?c note, we observe absolute
convergence within the UEMOA (CFA zone) in money supply (banking and ?nancial
ef?ciency).
Financial depth
Money supply Liquid liabilities
AC CC SAC SCC AC CC SAC SCC
CEMAC zone No No – – No No – –
UEMOA zone Yes (5%) No 7.87% – Yes (10%) No 10.82% –
Franc CFA zone No No – – Yes (10%) No 7.5% –
Financial ef?ciency
Banking system ef?ciency Financial system ef?ciency
AC CC SAC SCC AC CC SAC SCC
CEMAC zone No No – – No No – –
UEMOA zone No No – – No No – –
Franc CFA zone Yes (1%) No 12.32% – Yes (1%) No 14.72% –
Financial activity
Banking system activity Financial system activity
AC CC SAC SCC AC CC SAC SCC
CEMAC zone No No – – No No – –
UEMOA zone Yes (1%) No 11.02% – Yes (1%) No 11.02% –
Franc CFA zone Yes (1%) No 12.67% – Yes (1%) No 12.75% –
Financial size
AC CC SAC SCC
CEMAC zone No No – –
UEMOA zone Yes (5%) No 16.75% –
Franc CFA zone Yes (5%) Yes (1%) 7.2% 18.62%
In?ation and GDP growth
In?ation (CPI) GDP growth
AC CC SAC SCC AC CC SAC SCC
CEMAC zone No No – – No No – –
UEMOA zone No No – – No No – –
Franc CFA zone No No – – No No – –
Notes: AC – absolute convergence; CC – conditional convergence; SAC – speed of absolute
convergence; SCC – speed of conditional convergence; CEMAC – Economic and Monetary
Community of Central African States; UEMOA – Economic and Monetary Community of Western
African States; CFA – Franc of African French Colonies; CPI – consumer price index
Table I.
Summary of results
on convergence
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5,1
26
D
o
w
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o
a
d
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d
b
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P
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D
I
C
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R
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Y
A
t
2
1
:
4
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2
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a
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1
6
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(
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P
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)
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r
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1
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1
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2
3
3
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N
o
t
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s
:
S
i
g
n
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?
c
a
n
t
a
t
:
*
1
0
,
*
*
5
a
n
d
*
*
*
1
p
e
r
c
e
n
t
l
e
v
e
l
s
;
C
E
M
A
C
–
E
c
o
n
o
m
i
c
a
n
d
M
o
n
e
t
a
r
y
C
o
m
m
u
n
i
t
y
o
f
C
e
n
t
r
a
l
A
f
r
i
c
a
n
S
t
a
t
e
s
;
U
E
M
O
A
–
E
c
o
n
o
m
i
c
a
n
d
M
o
n
e
t
a
r
y
C
o
m
m
u
n
i
t
y
o
f
W
e
s
t
e
r
n
A
f
r
i
c
a
n
S
t
a
t
e
s
;
C
F
A
–
F
r
a
n
c
o
f
A
f
r
i
c
a
n
F
r
e
n
c
h
C
o
l
o
n
i
e
s
,
s
y
s
t
.
–
s
y
s
t
e
m
,
i
n
i
t
i
a
l
–
l
a
g
g
e
d
a
u
t
o
r
e
g
r
e
s
s
i
v
e
e
s
t
i
m
a
t
e
d
c
o
e
f
?
c
i
e
n
t
,
2
n
d
a
u
t
o
–
s
e
c
o
n
d
-
o
r
d
e
r
a
u
t
o
c
o
r
r
e
l
a
t
i
o
n
t
e
s
t
;
O
I
R
–
o
v
e
r
i
d
e
n
t
i
f
y
i
n
g
r
e
s
t
r
i
c
t
i
o
n
s
S
a
r
g
a
n
t
e
s
t
;
W
a
l
d
–
s
t
a
t
i
s
t
i
c
s
f
o
r
j
o
i
n
t
s
i
g
n
i
?
c
a
n
c
e
o
f
e
s
t
i
m
a
t
e
s
,
o
b
s
e
r
.
–
o
b
s
e
r
v
a
t
i
o
n
s
;
C
P
I
–
c
o
n
s
u
m
e
r
p
r
i
c
e
i
n
d
e
x
;
G
D
P
–
g
r
o
s
s
d
o
m
e
s
t
i
c
p
r
o
d
u
c
t
Table II.
Absolute convergence
Real and
monetary
policy
27
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
i
n
a
n
c
i
a
l
d
e
p
t
h
F
i
n
a
n
c
i
a
l
e
f
?
c
i
e
n
c
y
M
o
n
e
y
s
u
p
p
l
y
L
i
q
u
i
d
l
i
a
b
i
l
i
t
i
e
s
B
a
n
k
i
n
g
s
y
s
t
.
e
f
?
c
i
e
n
c
y
F
i
n
a
n
c
i
a
l
s
y
s
t
.
e
f
?
c
i
e
n
c
y
C
E
M
A
C
U
E
M
O
A
C
F
A
C
E
M
A
C
U
E
M
O
A
C
F
A
C
E
M
A
C
U
E
M
O
A
C
F
A
C
E
M
A
C
U
E
M
O
A
C
F
A
I
n
i
t
i
a
l
1
.
4
8
9
2
0
.
3
2
4
2
0
.
3
0
2
1
.
0
2
4
2
0
.
0
0
8
0
.
1
5
2
1
.
1
7
9
0
.
0
3
4
0
.
2
2
2
2
.
2
2
3
2
0
.
6
7
6
0
.
1
6
3
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0
.
4
2
2
)
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0
.
7
8
0
)
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0
.
5
8
)
(
0
.
7
4
3
)
(
0
.
9
8
9
)
(
0
.
5
1
)
(
0
.
3
0
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t
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s
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1
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b
s
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.
2
2
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3
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2
2
4
3
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5
6
(
c
o
n
t
i
n
u
e
d
)
Table III.
Conditional convergence
JFEP
5,1
28
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
I
n
?
a
t
i
o
n
a
n
d
G
D
P
g
r
o
w
t
h
I
n
?
a
t
i
o
n
(
C
P
I
)
G
D
P
g
r
o
w
t
h
C
E
M
A
C
U
E
M
O
A
C
F
A
C
E
M
A
C
U
E
M
O
A
C
F
A
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n
i
t
i
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l
2
0
.
8
4
1
2
0
.
3
5
7
2
0
.
1
8
4
2
0
.
4
5
6
0
.
2
0
7
0
.
0
0
8
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0
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4
8
1
)
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7
2
2
)
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0
.
6
6
5
)
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4
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2
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0
.
8
0
6
)
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0
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9
8
6
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t
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6
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2
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1
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2
2
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2
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2
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0
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3
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2
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1
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2
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3
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3
3
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7
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6
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0
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1
8
2
)
P
o
p
g
2
7
.
3
1
*
–
2
0
.
6
6
2
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Table III.
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4.2 Results of absolute convergence
Table II reports results of absolute convergence regressions. First and foremost, we
notice that for all models, the instruments are valid as the null hypotheses of the AR (2)
and Sargan OIR tests are not rejected. Where the lagged endogenous estimated
coef?cient is signi?cant, the Wald statistics is also signi?cant. We expected this result
for the Wald statistic because only one endogenous regressor is used in the absolute
convergence regressions.
For ?nancial depth, with respect to money supply we notice convergence only within
the UEMOA zone (at a 5 percent signi?cance level) with a speed of 7.78 percent per
annum (p.a). This implies that a 100 percent convergence will be achieved in about
51.41 yrs. With regard to the liquid liability dimension of ?nancial depth, there is
evidence of convergence within the UEMOAand CFAzones at a 10 percent signi?cance
level, with speeds (time) of (for full) convergence: 10.82 percent p.a (36.96 yrs) and
7.5 percent p.a (53.33 yrs), respectively.
Looking at results for ?nancial ef?ciency, only those of the CFA zone are signi?cant
at the 1 percent level from both banking and ?nancial system perspectives. Banking
(?nancial) system ef?ciency has a convergence rate of 12.32 percent (14.72 percent)
p.a and a 100 percent convergence time of 32.46 (27.17) years.
We also notice evidence of AC in banking (?nancial) system activity within the
UEMOA and CFA zones at a 1 percent signi?cance level. For UEMOA, the speeds
(time) of (for full) convergence in banking and ?nancial system activity are (is) equal
at (in) 11.02 percent p.a (36.29 yrs). With regard to the CFA zone the speeds (time)
of (for full) convergence in banking and ?nancial system activity are, respectively,
12.67 percent p.a (31.57 yrs) and 12.75 percent p.a (31.37 yrs).
In the convergence analysis of ?nancial size we also ?nd evidence of AC within the
UEMOA and CFA zones at a 5 percent signi?cance level with speeds (time) of (for full)
convergence: 16.75 percent p.a (23.88 yrs) and 7.2 percent p.a (55.55 yrs), respectively.
We ?nd no support for convergence in in?ation and GDP growth. The highest and
lowest rates of AC are found in the ?nancial size analysis. While the highest rate of AC
convergence is within UEMOA(23.88 yrs at a convergence rate of 16.75 percent p.a), the
lowest rate is within the CFA zone (55.55 yrs at a convergence rate of 7.2 percent p.a).
4.3 Results of conditional convergence
Table III reports results of conditional convergence. First and foremost, we notice that
but for the CFA zone (in ?nancial system ef?ciency and ?nancial size: with respect to
AR (2) at a 10 percent level) in all models the instruments are valid at 5 and 1 percent
signi?cance levels as the null hypotheses of the AR (2) and Sargan OIR tests are not
rejected. Of all combinations of panels with elements of real and monetary policy, we
?nd the presence of conditional convergence only in ?nancial size within the CFA zone
with a speed of 18.62 percent p.a and a time for 100 percent convergence of 21.48 yrs.
4.4 Discussion and policy implications
Before we delve into the discussion of results, it is important at the outset to
understand the economic intuition motivating absolute and conditional convergence in
real and monetary policies within the Franc CFA zone. The EMU crisis has sent a
strong signal to other common currency regions on the goals of real and monetary
convergence. A major lesson of the EMU crisis is that serious disequilibria result from
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regional monetary arrangements not designed to be robust to a variety of shocks
(Willett, 2011; Willett and Srisorn, 2011). In designing the euro zone, institutions’
almost exclusive concern was placed on limiting crises caused by ?nancial sectors. The
of?cial position of the German Government today appears to maintain that failure of
these safeguards is the predominant cause of the crisis. The present analysis has been
based on two hypotheses:
H1. Real economic sector policies are designed to achieve macroeconomic
performance through growth in GDP.
H2. Monetary policies are designed to keep in?ation in check and improve ?nancial
intermediary dynamics of depth (money supply and liquid liabilities),
ef?ciency (at banking and ?nancial levels), activity (from banking and
?nancial perspectives) and size.
4.4.1 Absolute convergence. Absolute convergence is the result from factors such as
monetary unions and the adoption of a single currency, among others (Narayan et al.,
2011). Absolute convergence in real and monetary policy implies countries share the
same fundamental characteristics with respect to the ?nancial intermediary market
(or monetary policy) such that the only difference across countries is in initial levels
of ?nancial intermediary market development. In the CFA zone, the CEMAC and
UEMOA regions that constitute it have distinct central banks which have independent
monetary policies. This explains the large disparity in signi?cance between their
absolute and conditional convergence estimates.
Since the mid-1980s countries of the CFA Franc zone have undertaken structural
reform programs engineered by the International Monetary Fund (IMF) which include
?nancial liberalization for the most part. The vested objective has been to reduce barriers
to trade and increase foreign investment. Unlike other African countries without a single
currency, CFA member states are expected to bene?t more in the reforms by virtue of
reduced risk and low cross-border currency conversion costs in the ?ows of trade and
investment among member countries. Holding all other things constant (such as political
instability, market isolation and macroeconomic conditions), ?nancial liberalization
reduces barriers to trade and improves investment as it obviates the need for investor
preference for one over the other. Owing to this ?nancial liberalization, capital controls
and control on exchange rate transactions have been substantially eased in the CFAzone;
together with advances in computer and communication technologies which have
rendered the banking industry increasingly synchronized. This synchronization has also
increased the speed of shock adjustment; implying the rate at which one bank in the
monetary zone adjusts when there is a shock in the other has increased. All these factors
have resulted in absolute convergence.
4.4.2 Conditional convergence. According to the economic growth literature
(Barro, 1991), conditional convergence depicts convergence whereby one’s own long-term
steady state (equilibrium) is contingent on the different structural characteristics or
fundamentals of each economy or market (Narayan et al., 2011). Borrowing from
Narayan et al. (2011) still, when ?nancial intermediary markets across countries differ in
terms of factors relating to the performance of their markets, there could be conditional
convergence. The convergence in dynamics of the banking sector is contingent on
variables which we observe and empirically test. Our results are thus conditional on
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the macroeconomic variables we have used. Note should be taken of the fact that, owing
to constraints in data availability and degrees of freedom required for the OIR test, we
conditioned our analysis on three macroeconomic variables: consistent with the
convergence literature (Prichett, 1997; Bruno et al., 2012; Narayan et al., 2011). But for
the case of ?nancial size within the CFA zone, our results do not ?nd convergence in
other dimensions of monetary and real policy for the UEMOA and CEMAC zones. It
follows that small-sized ?nancial intermediary countries within the CFA zone are
catching up with the large ?nancial sized markets. If evidence is considered based on
both absolute and conditional convergence, then we ?nd strong backing for
convergence only in ?nancial size within the CFA zone. This broadly con?rms the
position of Benassy-Quere and Coupet (2005) that CEMAC and UEMOA countries do
not share macroeconomic similarities and hence the CFA zone cannot be viewed as an
optimal currency area.
4.4.3 Policy implications. In spite of homogenous monetary policies for countries in
the CEMAC, UEMOA and CFA zones, we have only found strong evidence of some
unconditional convergence. The absence of evidence for this form of convergence (AC)
in certain dynamics could be understood from dissimilar initial conditions of ?nancial
development (among member states) and poor implementation of monetary policies
(by member states). But for ?nancial size in the CFAzone analysis, similar fundamental
characteristics (policies) in member states exhibited for unconditional convergence fail
to play, in ?ne-tuning conditional convergence. It follows that despite homogenous
fundamental conditions, structural characteristics and institutional differences
(including varying levels of democracy, political strife and quality of government) are
playing a crucial role in deterring convergence in real and monetary policies.
As a policy implication monetary policies which are often homogenous for all member
states are thwarted by heterogeneous structural and institutional characteristics which
give rise to different levels and patterns of ?nancial intermediary development. Therefore,
member states should work towards harmonizing cross-country differences in structural
and institutional characteristics that hamper the effectiveness of monetary policies.
5. Conclusion
The purpose of this paper has been to assess convergence within the CFA zone. It is
motivated by major lessons from the EMU crisis which suggest that the serious
disequilibria among EMU member states has resulted from regional monetary
arrangements not designed to be robust to a variety of shocks. In the investigations we
have distinguished the CEMAC from the UEMOA region, before assessing their
combined effect in the CFAzone. In the examinations we have assumed monetary policy
targets in?ation and ?nancial dynamics of depth, ef?ciency, activity and size, while real
sector policy targets economic performance in terms of GDP growth. We have also
provided the speed (time) of (for full) convergence.
From the ?ndings we notice that for the three panels (with respect all dynamics)
only ?nancial size within the CFA zone re?ects conditional convergence. There is no
form of convergence within the CEMAC zone. The UEMOA and CFA zones both re?ect
absolute convergence in liquid liabilities, banking activity, ?nancial activity and
?nancial size. There is absence of any form of convergence within any zone for
in?ation and GDP growth. On a speci?c note, we observe absolute convergence within
the UEMOA (CFA zone) for money supply (banking and ?nancial ef?ciency).
JFEP
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As a policy implication monetary policies which are often homogenous for all
member states are thwarted by heterogeneous structural and institutional characteristics
which give rise to different levels and patterns of ?nancial intermediary development.
Therefore, member states should work towards harmonizing cross-country differences
in structural and institutional characteristics that hamper the effectiveness of monetary
policies.
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European Union’s core and recent member countries: a rolling cointegration approach”,
Journal of Banking and Finance, Vol. 29, pp. 249-70.
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Appendix 1
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6
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n
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n
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o
p
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2
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8
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5
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3
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1
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7
4
0
6
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3
9
8
8
8
N
o
t
e
s
:
S
D
–
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
;
G
D
P
–
g
r
o
s
s
d
o
m
e
s
t
i
c
p
r
o
d
u
c
t
Table AI.
Summary statistics
Real and
monetary
policy
35
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Appendix 2
F
i
n
.
d
e
p
t
h
F
i
n
.
e
f
?
c
i
e
n
c
y
F
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n
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a
c
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i
v
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y
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i
n
.
s
i
z
e
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a
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a
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s
M
2
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d
g
d
p
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c
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d
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c
F
d
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2
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p
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c
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2
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2
4
3
P
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r
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1
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n
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D
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0
0
0
P
o
p
g
N
o
t
e
s
:
M
2
–
m
o
n
e
y
s
u
p
p
l
y
;
F
d
g
d
p
–
l
i
q
u
i
d
l
i
a
b
i
l
i
t
i
e
s
;
B
c
B
d
–
b
a
n
k
c
r
e
d
i
t
o
n
b
a
n
k
d
e
p
o
s
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t
;
F
c
F
d
–
?
n
a
n
c
i
a
l
c
r
e
d
i
t
o
n
?
n
a
n
c
i
a
l
d
e
p
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s
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t
;
P
r
c
b
–
p
r
i
v
a
t
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d
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m
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t
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c
c
r
e
d
i
t
b
y
d
e
p
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s
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t
b
a
n
k
s
;
P
r
c
b
o
f
–
p
r
i
v
a
t
e
d
o
m
e
s
t
i
c
c
r
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d
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t
b
y
d
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p
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t
b
a
n
k
s
a
n
d
o
t
h
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r
?
n
a
n
c
i
a
l
i
n
s
t
i
t
u
t
i
o
n
s
;
D
b
a
c
b
a
–
d
e
p
o
s
i
t
b
a
n
k
a
s
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t
s
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n
d
e
p
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i
t
b
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k
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p
l
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s
c
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n
t
r
a
l
b
a
n
k
a
s
s
e
t
s
;
P
u
b
.
I
–
p
u
b
l
i
c
i
n
v
e
s
t
m
e
n
t
;
G
D
P
g
–
G
D
P
g
r
o
w
t
h
;
P
o
p
g
–
p
o
p
u
l
a
t
i
o
n
g
r
o
w
t
h
;
F
i
n
.
–
?
n
a
n
c
i
a
l
Table AII.
Correlation analysis
JFEP
5,1
36
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Appendix 3
V
a
r
i
a
b
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s
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s
V
a
r
i
a
b
l
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t
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n
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n
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.
C
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%
)
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k
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2
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d
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a
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k
(
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N
o
t
e
s
:
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2
–
m
o
n
e
y
s
u
p
p
l
y
;
F
d
g
d
p
–
l
i
q
u
i
d
l
i
a
b
i
l
i
t
i
e
s
;
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c
B
d
–
b
a
n
k
c
r
e
d
i
t
o
n
b
a
n
k
d
e
p
o
s
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t
s
;
F
c
F
d
–
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n
a
n
c
i
a
l
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s
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e
m
c
r
e
d
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t
o
n
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a
n
c
i
a
l
s
y
s
t
e
m
d
e
p
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s
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t
s
;
P
c
r
b
–
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r
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v
a
t
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d
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m
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c
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d
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t
b
y
d
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,
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f
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Table AIII.
Variable de?nitions
Real and
monetary
policy
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Appendix 4
Corresponding author
Simplice A. Asongu can be contacted at: [email protected]
Zones De?nitions Countries Number
CEMAC Economic and Monetary Community
of Central African States
Cameroon, Central African Republic,
Chad, Equatorial Guinea, Gabon
5
UEMOA Economic and Monetary Community
of West African States
Burkina Faso, Ivory Coast, Mali,
Niger, Senegal, Togo
6
Franc CFA Franc of African French Colonies Cameroon, Central African Republic,
Chad, Equatorial Guinea, Gabon,
Burkina Faso, Ivory Coast, Mali,
Niger, Senegal, Togo
11
Table AIV.
Presentation of countries
JFEP
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This article has been cited by:
1. Simplice A. Asongu. 2015. The Comparative Economics of Knowledge Economy in Africa: Policy
Benchmarks, Syndromes, and Implications. Journal of the Knowledge Economy . [CrossRef]
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cross-country comparison. Intelligence 51, 1-9. [CrossRef]
3. Simplice A. Asongu. 2015. Knowledge Economy Gaps, Policy Syndromes, and Catch-Up Strategies: Fresh
South Korean Lessons to Africa. Journal of the Knowledge Economy . [CrossRef]
4. Simplice Asongu. 2014. African Development: Beyond Income Convergence. South African Journal of
Economics 82:10.1111/saje.2014.82.issue-3, 334-353. [CrossRef]
5. Simplice Asongu. 2014. REER Imbalances and Macroeconomic Adjustments in the Proposed West
African Monetary Union. South African Journal of Economics 82:10.1111/saje.2014.82.issue-2, 276-289.
[CrossRef]
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Macroeconomic Shocks and Financial Policies Implications: The Case of CEMAC Sub-region. African
Development Review 26, 102-117. [CrossRef]
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doc_491366015.pdf
A major lesson of the European Monetary Union (EMU) crisis is that serious disequilibria
result from regional monetary arrangements not designed to be robust to a variety of shocks. The
purpose of this paper is to assess these disequilibria within the Economic and Monetary Community of
Central Africa (CEMAC), West African Economic and Monetary Union (UEMOA) and Financial
Community of Africa (CFA) zones.
Journal of Financial Economic Policy
Real and monetary policy convergence: EMU crisis to the CFA zone
Simplice A. Asongu
Article information:
To cite this document:
Simplice A. Asongu, (2013),"Real and monetary policy convergence: EMU crisis to the CFA zone", J ournal
of Financial Economic Policy, Vol. 5 Iss 1 pp. 20 - 38
Permanent link to this document:http://dx.doi.org/10.1108/17576381311317763
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Simplice A. Asongu, (2013),"Fighting consumer price inflation in Africa: What do dynamics in money,
credit, efficiency and size tell us?", J ournal of Financial Economic Policy, Vol. 5 Iss 1 pp. 39-60 http://
dx.doi.org/10.1108/17576381311317772
Peter T. Calcagno, J ustin D. Benefield, (2013),"Economic freedom, the cost of public borrowing,
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Real and monetary policy
convergence: EMU crisis to the
CFA zone
Simplice A. Asongu
HEC-Management School, University of Lie `ge, Lie `ge, Belgium
Abstract
Purpose – A major lesson of the European Monetary Union (EMU) crisis is that serious disequilibria
result from regional monetary arrangements not designed to be robust to a variety of shocks. The
purpose of this paper is to assess these disequilibria within the Economic and Monetary Community of
Central Africa (CEMAC), West African Economic and Monetary Union (UEMOA) and Financial
Community of Africa (CFA) zones.
Design/methodology/approach – In the assessments, monetary policy targets in?ation and
?nancial dynamics of depth, ef?ciency, activity and size while real sector policy targets economic
performance in terms of GDP growth. The author also provides the speed of convergence and time
required to achieve a 100 percent convergence.
Findings – But for ?nancial intermediary size within the CFA zone, ?ndings, for the most part,
support only unconditional convergence. There is no form of convergence within the CEMAC zone.
Practical implications – The broad insigni?cance of conditional convergence results has
substantial policy implications. Monetary and real policies, which are often homogenous for member
states, are thwarted by heterogeneous structural and institutional characteristics, which give rise to
different levels and patterns of ?nancial intermediary development. Therefore, member states should
work towards harmonizing cross-country differences in structural and institutional characteristics that
hamper the effectiveness of monetary policies.
Originality/value – The paper provides warning signs to the CFA zone in the heat of the Euro zone
crises.
Keywords Africa, Monetary policy, National economy, Economic disequilibrium,
Financial Community of Africa, Economic and Monetary Community of Central Africa, Currency area,
Convergence, Policy coordination
Paper type Research paper
1. Introduction
The European Monetary Union (EMU) crisis has sent a strong signal to other common
currency regions on the goals of real and monetary policy convergence. A major lesson
of the EMU crisis is that serious disequilibria result from regional monetary
arrangements not designed to be robust to a variety of shocks (Willett, 2011; Willett
and Srisorn, 2011). In designing the euro zone, institutions’ almost exclusive concern
was placed on limiting crises caused by ?nancial sectors. The of?cial position of the
German Government today appears to remain that failure of these safeguards is the
predominant cause of the crisis. This position can be reasonably argued for Greece,
although its loss of competitiveness has also been a major cause for worry.
In this paper we examine the nature of real and monetary policy convergence within
the Financial Community of Africa (CFA) zone. The work contributes to the discussion
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – F15, F36, F42, O55, P52
Journal of Financial Economic Policy
Vol. 5 No. 1, 2013
pp. 20-38
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381311317763
JFEP
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of ?nancial integration in common currency unions. By dissecting elements of
macroeconomic and monetary policies into ?nancial intermediary dynamics (of depth,
ef?ciency, activityand size), in?ation andeconomic performance, we provide anin depth
picture of real and monetary convergence analysis within the Economic and Monetary
Community of Central Africa (CEMAC), West African Economic and Monetary Union
(UEMOA) and CFA zones. In a bid to provide more signi?cant policy implications,
we also calculate the speed of convergence and the time needed for full convergence.
Beside the premise of the EMU crisis, the absence of studies that focus on ?nancial
convergence in the African continent constitute another motivation for this work.
Although a number of papers have investigated the dynamic co-movements of ?nancial
markets worldwide, the emphasis has often been on developed markets and the emerging
economies of Latin America and Asia. According to Alagidede (2008), such neglect is far
fromsurprising as African?nancial markets are viewed as too riskyand less developed in
operating institutional environments. Economic instability and political strife have
plagued many African countries and continue to pose a signi?cant deterrent to foreign
investments and private capital ?ows (Kenyan post elections crises in 2007/2008,
Zimbabwe’s economic meltdown, Nigeria’s marred transition in 2008, the unending
Egyptian revolution and resurfacing religious tensions in Nigeria). But for South Africa,
no African country has yet risen as an emerging economic power. This might partly
explain the relative lack of academic research on the banking sector of the continent.
However, recently Africa has witnessed signi?cant economic and ?nancial developments.
This provides some basis for assessing multidimensional ?nancial convergence;
especially in single currency areas amid pending EMU crisis.
The rest of the paper is structured in the following manner. Section 2 outlines the
motivations for assessing convergence within the CFA zone. Section 3 presents data
and discusses the methodology. Empirical analysis, discussion and policy implications
are covered by Section 4. Section 5 concludes.
2. Motivations for ?nancial system convergence
Financial and economic integration in the CFAzone is expected to provide gains in growth
by favoring a breeding atmosphere for competition and ef?ciency in the banking sector.
These gains emanate fromprice reductions in ?nancial services leading to direct gains for
consumers and indirect bene?ts through the reduction of loaning rate that favor
investment (Weill, 2009). Investigating ?nancial intermediary convergence is therefore
relevant inthe Africancontinent. More so, ?nancial integrationandconvergence are crucial
in assessing the outcome of deregulation policies aimed at improving the performance and
ef?ciency of the ?nancial intermediary sector (Casu and Girardone, 2010).
Financial theory deems integrated markets to be relatively more ef?cient in
comparison to divergent ones. An integrated ?nancial intermediary market improves
cross-border ?ow of funds, stimulates trading volume which in-turn improves stock
market liquidity. Integrated banking markets provide investors with the opportunity to
ef?ciently allocate capital to economic operators (Chen et al., 2002). The resulting effect is
lower cost of capital for ?rms and lower transaction costs for investors (Kim et al., 2005).
An integrated banking market has the positive rewards to ?nancial stability as
it diminishes the probability of asymmetric shocks (Umutlu et al., 2010). Financial
intermediation stability in-turn could mitigate the risk of cross-border ?nancial contagion
(Beine et al., 2010) and improve the capacityof economies to absorbshocks (Yuet al., 2010).
Real and
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The ?nancial system exerts a signi?cant in?uence on modern economic literature
debates (Scholtens and Naaborg, 2005). First, the monetary policy transmission
mechanism’s effectiveness is contingent on the ?nancial system(Bondt, 2000). Second, it
is believed to impact channels via which ?nancial development is linked to economic
growth (Allen and Gale, 2000). The ?nancial system interacts with the economy by
producing information ex ante about possible investments, monitoring of investments,
allocation of capital, facilitating trade, diversifying and managing risks, mobilizing and
pooling savings as well as easing the exchange of goods and services (Levine, 2004).
The need for convergence in the banking sector of the CFA zone draws on the tenets
of arbitrage and the hypothesis proffered by the portfolio theory to devise a framework
that inspires convergence in ?nancial markets. The motivations for convergence in
banking markets has premises on the literature of ?nancial intermediary sector
interdependence and portfolio diversi?cation (Grubel, 1968; Levy and Sarnat, 1970).
These papers have for the most part considered short-term links of stock markets and
have found the existence of short-term ?nancial market co-movements. These results
have been extended to cover co-variations of ?nancial markets over the long-run
(Bessler and Yang, 2003). Majority of these papers have shown evidence of cointegration
as well as short-termlinks which depict some formof convergence in ?nancial markets.
Dynamics of ?nancial intermediation therefore converge to re?ect the level of
arbitrage activity. When they converge, it means there is a common force such as
arbitrage activity that brings the markets together. This implies that convergence in
markets will reduce the potential for making above normal pro?ts through
international diversi?cation (Von Furstenberg and Jeon, 1989). By the same token, if
deterrents or potential barriers generating country risks and exchange rate premiums
are absent, the consequence is similar yields for ?nancial assets of similar risk and
liquidity regardless of locality and nationality (Von Furstenberg and Jeon, 1989).
Within the framework of this paper, real and monetary policy convergence implies
the integration of banking sector dynamics of depth, ef?ciency, activity and size; as
well as in?ation and GDP growth.
3. Data and methodology
3.1 Data
We examine a sample of 11 Central and West African countries with data from African
Development Indicators (ADI) and the ?nancial development and structure database
(FDSD) of the World Bank. While openness, in?ation, population growth, public
investment and GDP growth indicators are obtained from the former source, ?nancial
intermediary dynamics are fetched from the later. Owing to constraints in data
availability, dataset spans from 1981 to 2009. More information on summary statistics
(Appendix 1), correlation analysis (Appendix 2), variable de?nitions (Appendix 3) and
presentation of countries (Appendix 4) is available in the appendices. Concurring with
Narayan et al. (2011) it is unlikely to ?nd convergence within a very heterogeneous set
of countries. Thus, we disaggregate the CFA zone into two homogenous panels based
on regions (CEMAC and UEMOA). The choice of variables is premised on two facts:
(1) real economic sector policies are designed to achieve macroeconomic
performance through growth in GDP; and
(2) monetary policies are designed to keep in?ation in check and improve
?nancial intermediary dynamics of depth (money supply and liquid liabilities),
JFEP
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ef?ciency (at banking and ?nancial levels), activity (from banking and ?nancial
perspectives) and size.
For clarity in presentation, selected variables are classi?ed into the following strands.
3.1.1 Financial variables.
(a) Financial depth. Borrowing from recent ?nance literature (Asongu, 2011a, b, c, e)
and the FDSD, we measure ?nancial depth both from overall-economic and ?nancial
system perspectives with indicators of broad money supply (M2/GDP) and ?nancial
system deposits (Fdgdp), respectively. The former represents the monetary base plus
demand, saving and time deposits while the later denotes liquid liabilities. The basis
for distinguishing these aspects of ?nancial depth is that we are dealing exclusively
with developing countries, where-in a signi?cant chunk of the monetary base does not
transit through the banking sector (Asongu, 2011d). Both measures are in ratios of
GDP (see Appendix 3) and can robustly cross-check one another as either account for
over 89 percent of variability in the other (see Appendix 2).
(b) Financial ef?ciency. Financial intermediation ef?ciency here neither refers to the
production ef?ciency of decision making units nor to the pro?tability-oriented concept
in the banking industry. What this paper elicits is the ability of banks to effectively
meet their fundamental role of transforming mobilized deposits into credit for
economic operators. We therefore employ proxies for banking-system-ef?ciency and
?nancial-system-ef?ciency (respectively “bank credit on bank deposits: Bcbd” and
“?nancial system credit on ?nancial system deposits: Fcfd”). Like with ?nancial depth,
these two intermediation ef?ciency proxies can cross-check each other as either
represent more than 92 percent of variability in the other (see Appendix 2).
(c) Financial size. In line with the FDSD we measure ?nancial intermediary size as
the ratio of “deposit bank assets” to the “total assets” (deposit bank assets on central
bank assets plus deposit bank assets: Dbacba).
(d) Financial activity. The concept of ?nancial intermediary activity here highlights
the ability of banks to grant credit to economic operators. In a bid for robustness we
proxy for both banking intermediary activity and ?nancial intermediary activity with
“private domestic credit by deposit banks: Pcrb” and “private credit by domestic banks
and other ?nancial institutions: Pcrbof”, respectively. The later measure cross-checks
the former as it accounts for more than 99 percent of information in the former
(see Appendix 2).
3.1.2 Other variables. In accordance with the convergence literature we also measure
the outcome of monetary policy with in?ation (Bruno et al., 2012), account for macro
economic performance with GDP growth rate and control for openness, public investment
and population growth in the regressions (Prichett, 1997; Bruno et al., 2012; Narayan et al.,
2011). In the literature on convergence of per capita incomes and the root of the
convergence theory, the premise is that per capita incomes of countries identical in
structural characteristics such as preferences in technologies, rate of population growth
and government policies have the tendency to converge to one another if the countries
share similar fundamental characteristics (Prichett, 1997). In determining these structural
characteristics in our analysis, we proxy for preferences in technology, population growth
and government policy with openness (trade), population growth rate and public
investment, respectively. As concerns common fundamental characteristics, we assume
countries share similar monetary and real policies owing to common central banks.
Real and
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3.2 Methodology
According to Fung (2009, p. 3) the two equations below are the standard approaches in
the literature for investigating conditional convergence if W
i,t
is taken as strictly
exogenous:
lnðY
i;t
Þ 2lnðY
i;t2t
Þ ¼ blnðY
i;t2t
Þ þ dW
i;t2t
þ h
i
þ j
t
þ 1
i;t
ð1Þ
lnðY
i;t
Þ ¼ slnðY
i;t2t
Þ þ dW
i;t2t
þ h
i
þ j
t
þ 1
i;t
ð2Þ
where s ¼ 1 þ b, Y
i,t
is the proxy for per capita ?nancial or real sector development in
country i at period t. W
i,t
is a vector of determinants (openness, public investment and
population growth) of per capita ?nance (or real sector per capita), h
i
is a country
speci?c effect, j
t
is a time speci?c constant and 1
i,t
an error term. In line with the
neo-classical growth model, a statistically signi?cant negative coef?cient on b in
equation (1) means that countries relatively close to their steady state of per capita
growth will experience a slowdown in growth of the per capita banking (real sector)
development, known as conditional convergence (Narayan et al., 2011, p. 2). Borrowing
from Fung (2009, p. 3), if 0 , jsj , 1 in equation (2), then Y
i,t
is dynamically stable
around the path with a trend growth rate the same as that of W
t
, and with a height
relative to the level of W
t
. The variables contained in W
i,t2t
and the individual effect h
i
are measures of the long-term level the market (real sector) is converging to. Thus, the
country speci?c effect h
i
depicts the existence of other determinants of a country’s
steady state not captured by W
i,t2t
.
Conditions for convergence elucidated above are contingent on the strict exogeneity
of W
i,t
. Unfortunately, this is not the case in the real world because, whereas openness,
public investment and population growth (components of W
i,t
) in?uence per capita
?nancial (real sector) development, the reverse effect cannot be ruled-out. Thus, we are
confronted with the issue of endogeneity where openness (trade), public investment
and population growth are correlated with the error term (1
i,t
). More so country and
time speci?c effects could be correlated with other variables in the model, which is
often the case when lagged dependent variables are included in the equations. Away of
dealing with the problem of the correlation between the individual speci?c-effect and
the lagged dependent variables involves getting rid of the individual effect by ?rst
differencing. Thus, equation (2) becomes:
lnðY
i;t
Þ 2lnðY
i;t2t
Þ ¼ slnðY
i;t2t
2Y
i;t22t
Þ þ dðW
i;t2t
2W
i;t22t
Þ þ ð1
i;t
21
i;t2t
Þ
ð3Þ
Even with this individual ?xed effect elimination, estimation by ordinary least square
(OLS) is still biased because there remains a correlation between the lagged endogenous
independent variable and the disturbance term. Arellano and Bond (1991) proposed an
application of the generalized method of moments (GMM) that exploits all the
orthogonality conditions between the dependent lagged variables and the error term. This
GMM approach has been extensively used in the convergence literature and recently
applied by Narayan et al. (2011). While Narayan et al. (2011) use equation (1) without ?xed
effects, this paper applies equation (3) instead; as de?ned by Fung (2009). We prefer the
second-step GMMbecause it corrects the residuals for heteroscedasticity. In the ?rst-step
the residuals are considered to be homoscedastic. The hypothesis of no auto-correlation in
residuals is crucial as past lagged variables are to be used as instruments for the
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dependent variables. Also the estimation depends on the assumption that the lagged
values of the outcome variable and other explaining variables are valid instruments in the
regression. When the error terms of the level equation are not auto-correlated, the
?rst-order auto-correlation of the differenced residuals should be signi?cant while their
second-order auto-correlation should not be the case. The validity of the instruments is
investigated by virtue of the Sargan over-identifying restrictions test (OIR).
As emphasized by Islam (1995, p. 14), annual time spans are too short to be
convenient for studying convergence, as short run disturbances may loomsubstantially
in such brief time spans. Therefore, with respect to our data span of 28 years (yrs), we
borrow from Narayan et al. (2011) in using a four-year non-overlapping interval such
that we have seven time intervals: 1982-1985; 1986-1989 and so on. This implies in the
analysis, the autoregressive order t is set to 4.
We also compute the impliedrate of convergence bycalculating(s/4) whichbyvirtue of
equations (1) and (2) is the equivalent of the Narayan et al. (2011) computation with
(1 þ b)/4. Thus, the paper divides the estimated coef?cient of the lagged differenced
endogenous variable by 4 because we have used a four-year non-overlapping span in a
bid to absorb short-term disturbances. When the absolute value of the estimated
autoregressive coef?cient is greater than zero but less than one (0,jsj,1), we conclude
the presence of convergence. A broader interpretation suggests, past differences have a
less proportionate impact onfuture differences, meaningthe variationonthe left handside
of equation (3) is diminishing overtime as the country is converging to a stable state.
4. Empirical analysis
This section addresses three main issues:
(1) assessment of the presence of convergence;
(2) determination of the speed of convergence; and
(3) computation of the time needed for full (100 percent) convergence.
Table I presents a summary of overall ?ndings and addresses the ?rst two issues,
while Tables II and III, respectively, present results for unconditional and conditional
convergence.
Unconditional (absolute) convergence is estimated with only the lagged difference of
the endogenous variable as exogenous variable while conditional convergence is in
respect of equation (3). Thus, unconditional convergence is estimated without W
i,t
: vector
of determinants (openness, public investment and population growth) of per capita
?nance (or real sector per capita). To investigate the validity of the model and indeed the
convergence hypothesis, we carry-out two tests, namely the Sargan test which examines
the over-identi?cation restrictions, and the Arellano and Bond test for autocorrelation
which assesses the null hypothesis of no autocorrelation. The Sargan test investigates
whether the instruments are uncorrelated with the error term in the estimated equation.
Its null hypothesis is the stance that the instruments as a group are strictly exogenous
(do not suffer from endogeneity), which is needed for the validity of the GMM estimates.
We also report the Wald statistics for the joint signi?cance of estimated coef?cients.
The autocorrelation, Sargan and Wald tests statistics with associated p-values for each of
the panels are reported in the tables. The Sargan test statistics often appear with a p-value
.0.10, hence its null hypothesis is not rejected for the most part. We onlyreport the AR(2)
test in?rst difference because it is more important thanAR(1) as it detects autocorrelation
Real and
monetary
policy
25
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in levels. For most estimated models we are unable to reject the AR (2) null hypothesis of
no autocorrelation. There is therefore robust evidence that most of the models are free
from autocorrelation at the 1 percent signi?cance level.
4.1 Synthesis of results
In Table I, we provide a summary of our results. This summary results is based on
details presented in Tables II and III. AC, CC, SAC, SCC; represent absolute convergence,
conditional convergence, speed of absolute convergence and speed of conditional
convergence, respectively. We notice that for the three panels, with respect to all
dynamics only ?nancial size within the CFA zone re?ects a conditional convergence.
There is no form of convergence within the CEMAC zone. The UEMOA and CFA zones
both re?ect absolute convergence in liquid liabilities, banking activity, ?nancial activity
and ?nancial size. There is the absence of any form of convergence within any zone for
in?ation and GDP growth regressions. On a speci?c note, we observe absolute
convergence within the UEMOA (CFA zone) in money supply (banking and ?nancial
ef?ciency).
Financial depth
Money supply Liquid liabilities
AC CC SAC SCC AC CC SAC SCC
CEMAC zone No No – – No No – –
UEMOA zone Yes (5%) No 7.87% – Yes (10%) No 10.82% –
Franc CFA zone No No – – Yes (10%) No 7.5% –
Financial ef?ciency
Banking system ef?ciency Financial system ef?ciency
AC CC SAC SCC AC CC SAC SCC
CEMAC zone No No – – No No – –
UEMOA zone No No – – No No – –
Franc CFA zone Yes (1%) No 12.32% – Yes (1%) No 14.72% –
Financial activity
Banking system activity Financial system activity
AC CC SAC SCC AC CC SAC SCC
CEMAC zone No No – – No No – –
UEMOA zone Yes (1%) No 11.02% – Yes (1%) No 11.02% –
Franc CFA zone Yes (1%) No 12.67% – Yes (1%) No 12.75% –
Financial size
AC CC SAC SCC
CEMAC zone No No – –
UEMOA zone Yes (5%) No 16.75% –
Franc CFA zone Yes (5%) Yes (1%) 7.2% 18.62%
In?ation and GDP growth
In?ation (CPI) GDP growth
AC CC SAC SCC AC CC SAC SCC
CEMAC zone No No – – No No – –
UEMOA zone No No – – No No – –
Franc CFA zone No No – – No No – –
Notes: AC – absolute convergence; CC – conditional convergence; SAC – speed of absolute
convergence; SCC – speed of conditional convergence; CEMAC – Economic and Monetary
Community of Central African States; UEMOA – Economic and Monetary Community of Western
African States; CFA – Franc of African French Colonies; CPI – consumer price index
Table I.
Summary of results
on convergence
JFEP
5,1
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,
i
n
i
t
i
a
l
–
l
a
g
g
e
d
a
u
t
o
r
e
g
r
e
s
s
i
v
e
e
s
t
i
m
a
t
e
d
c
o
e
f
?
c
i
e
n
t
,
2
n
d
a
u
t
o
–
s
e
c
o
n
d
-
o
r
d
e
r
a
u
t
o
c
o
r
r
e
l
a
t
i
o
n
t
e
s
t
;
O
I
R
–
o
v
e
r
i
d
e
n
t
i
f
y
i
n
g
r
e
s
t
r
i
c
t
i
o
n
s
S
a
r
g
a
n
t
e
s
t
;
W
a
l
d
–
s
t
a
t
i
s
t
i
c
s
f
o
r
j
o
i
n
t
s
i
g
n
i
?
c
a
n
c
e
o
f
e
s
t
i
m
a
t
e
s
,
o
b
s
e
r
.
–
o
b
s
e
r
v
a
t
i
o
n
s
;
C
P
I
–
c
o
n
s
u
m
e
r
p
r
i
c
e
i
n
d
e
x
;
G
D
P
–
g
r
o
s
s
d
o
m
e
s
t
i
c
p
r
o
d
u
c
t
Table II.
Absolute convergence
Real and
monetary
policy
27
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
i
n
a
n
c
i
a
l
d
e
p
t
h
F
i
n
a
n
c
i
a
l
e
f
?
c
i
e
n
c
y
M
o
n
e
y
s
u
p
p
l
y
L
i
q
u
i
d
l
i
a
b
i
l
i
t
i
e
s
B
a
n
k
i
n
g
s
y
s
t
.
e
f
?
c
i
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n
c
y
F
i
n
a
n
c
i
a
l
s
y
s
t
.
e
f
?
c
i
e
n
c
y
C
E
M
A
C
U
E
M
O
A
C
F
A
C
E
M
A
C
U
E
M
O
A
C
F
A
C
E
M
A
C
U
E
M
O
A
C
F
A
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M
A
C
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l
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2
F
i
n
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c
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a
c
t
i
v
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t
y
F
i
n
a
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c
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B
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k
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c
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v
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t
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C
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C
F
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C
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A
C
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Table III.
Conditional convergence
JFEP
5,1
28
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Table III.
Real and
monetary
policy
29
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A
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2
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:
4
6
2
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J
a
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a
r
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1
6
(
P
T
)
4.2 Results of absolute convergence
Table II reports results of absolute convergence regressions. First and foremost, we
notice that for all models, the instruments are valid as the null hypotheses of the AR (2)
and Sargan OIR tests are not rejected. Where the lagged endogenous estimated
coef?cient is signi?cant, the Wald statistics is also signi?cant. We expected this result
for the Wald statistic because only one endogenous regressor is used in the absolute
convergence regressions.
For ?nancial depth, with respect to money supply we notice convergence only within
the UEMOA zone (at a 5 percent signi?cance level) with a speed of 7.78 percent per
annum (p.a). This implies that a 100 percent convergence will be achieved in about
51.41 yrs. With regard to the liquid liability dimension of ?nancial depth, there is
evidence of convergence within the UEMOAand CFAzones at a 10 percent signi?cance
level, with speeds (time) of (for full) convergence: 10.82 percent p.a (36.96 yrs) and
7.5 percent p.a (53.33 yrs), respectively.
Looking at results for ?nancial ef?ciency, only those of the CFA zone are signi?cant
at the 1 percent level from both banking and ?nancial system perspectives. Banking
(?nancial) system ef?ciency has a convergence rate of 12.32 percent (14.72 percent)
p.a and a 100 percent convergence time of 32.46 (27.17) years.
We also notice evidence of AC in banking (?nancial) system activity within the
UEMOA and CFA zones at a 1 percent signi?cance level. For UEMOA, the speeds
(time) of (for full) convergence in banking and ?nancial system activity are (is) equal
at (in) 11.02 percent p.a (36.29 yrs). With regard to the CFA zone the speeds (time)
of (for full) convergence in banking and ?nancial system activity are, respectively,
12.67 percent p.a (31.57 yrs) and 12.75 percent p.a (31.37 yrs).
In the convergence analysis of ?nancial size we also ?nd evidence of AC within the
UEMOA and CFA zones at a 5 percent signi?cance level with speeds (time) of (for full)
convergence: 16.75 percent p.a (23.88 yrs) and 7.2 percent p.a (55.55 yrs), respectively.
We ?nd no support for convergence in in?ation and GDP growth. The highest and
lowest rates of AC are found in the ?nancial size analysis. While the highest rate of AC
convergence is within UEMOA(23.88 yrs at a convergence rate of 16.75 percent p.a), the
lowest rate is within the CFA zone (55.55 yrs at a convergence rate of 7.2 percent p.a).
4.3 Results of conditional convergence
Table III reports results of conditional convergence. First and foremost, we notice that
but for the CFA zone (in ?nancial system ef?ciency and ?nancial size: with respect to
AR (2) at a 10 percent level) in all models the instruments are valid at 5 and 1 percent
signi?cance levels as the null hypotheses of the AR (2) and Sargan OIR tests are not
rejected. Of all combinations of panels with elements of real and monetary policy, we
?nd the presence of conditional convergence only in ?nancial size within the CFA zone
with a speed of 18.62 percent p.a and a time for 100 percent convergence of 21.48 yrs.
4.4 Discussion and policy implications
Before we delve into the discussion of results, it is important at the outset to
understand the economic intuition motivating absolute and conditional convergence in
real and monetary policies within the Franc CFA zone. The EMU crisis has sent a
strong signal to other common currency regions on the goals of real and monetary
convergence. A major lesson of the EMU crisis is that serious disequilibria result from
JFEP
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V
E
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Y
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t
2
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:
4
6
2
4
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a
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u
a
r
y
2
0
1
6
(
P
T
)
regional monetary arrangements not designed to be robust to a variety of shocks
(Willett, 2011; Willett and Srisorn, 2011). In designing the euro zone, institutions’
almost exclusive concern was placed on limiting crises caused by ?nancial sectors. The
of?cial position of the German Government today appears to maintain that failure of
these safeguards is the predominant cause of the crisis. The present analysis has been
based on two hypotheses:
H1. Real economic sector policies are designed to achieve macroeconomic
performance through growth in GDP.
H2. Monetary policies are designed to keep in?ation in check and improve ?nancial
intermediary dynamics of depth (money supply and liquid liabilities),
ef?ciency (at banking and ?nancial levels), activity (from banking and
?nancial perspectives) and size.
4.4.1 Absolute convergence. Absolute convergence is the result from factors such as
monetary unions and the adoption of a single currency, among others (Narayan et al.,
2011). Absolute convergence in real and monetary policy implies countries share the
same fundamental characteristics with respect to the ?nancial intermediary market
(or monetary policy) such that the only difference across countries is in initial levels
of ?nancial intermediary market development. In the CFA zone, the CEMAC and
UEMOA regions that constitute it have distinct central banks which have independent
monetary policies. This explains the large disparity in signi?cance between their
absolute and conditional convergence estimates.
Since the mid-1980s countries of the CFA Franc zone have undertaken structural
reform programs engineered by the International Monetary Fund (IMF) which include
?nancial liberalization for the most part. The vested objective has been to reduce barriers
to trade and increase foreign investment. Unlike other African countries without a single
currency, CFA member states are expected to bene?t more in the reforms by virtue of
reduced risk and low cross-border currency conversion costs in the ?ows of trade and
investment among member countries. Holding all other things constant (such as political
instability, market isolation and macroeconomic conditions), ?nancial liberalization
reduces barriers to trade and improves investment as it obviates the need for investor
preference for one over the other. Owing to this ?nancial liberalization, capital controls
and control on exchange rate transactions have been substantially eased in the CFAzone;
together with advances in computer and communication technologies which have
rendered the banking industry increasingly synchronized. This synchronization has also
increased the speed of shock adjustment; implying the rate at which one bank in the
monetary zone adjusts when there is a shock in the other has increased. All these factors
have resulted in absolute convergence.
4.4.2 Conditional convergence. According to the economic growth literature
(Barro, 1991), conditional convergence depicts convergence whereby one’s own long-term
steady state (equilibrium) is contingent on the different structural characteristics or
fundamentals of each economy or market (Narayan et al., 2011). Borrowing from
Narayan et al. (2011) still, when ?nancial intermediary markets across countries differ in
terms of factors relating to the performance of their markets, there could be conditional
convergence. The convergence in dynamics of the banking sector is contingent on
variables which we observe and empirically test. Our results are thus conditional on
Real and
monetary
policy
31
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(
P
T
)
the macroeconomic variables we have used. Note should be taken of the fact that, owing
to constraints in data availability and degrees of freedom required for the OIR test, we
conditioned our analysis on three macroeconomic variables: consistent with the
convergence literature (Prichett, 1997; Bruno et al., 2012; Narayan et al., 2011). But for
the case of ?nancial size within the CFA zone, our results do not ?nd convergence in
other dimensions of monetary and real policy for the UEMOA and CEMAC zones. It
follows that small-sized ?nancial intermediary countries within the CFA zone are
catching up with the large ?nancial sized markets. If evidence is considered based on
both absolute and conditional convergence, then we ?nd strong backing for
convergence only in ?nancial size within the CFA zone. This broadly con?rms the
position of Benassy-Quere and Coupet (2005) that CEMAC and UEMOA countries do
not share macroeconomic similarities and hence the CFA zone cannot be viewed as an
optimal currency area.
4.4.3 Policy implications. In spite of homogenous monetary policies for countries in
the CEMAC, UEMOA and CFA zones, we have only found strong evidence of some
unconditional convergence. The absence of evidence for this form of convergence (AC)
in certain dynamics could be understood from dissimilar initial conditions of ?nancial
development (among member states) and poor implementation of monetary policies
(by member states). But for ?nancial size in the CFAzone analysis, similar fundamental
characteristics (policies) in member states exhibited for unconditional convergence fail
to play, in ?ne-tuning conditional convergence. It follows that despite homogenous
fundamental conditions, structural characteristics and institutional differences
(including varying levels of democracy, political strife and quality of government) are
playing a crucial role in deterring convergence in real and monetary policies.
As a policy implication monetary policies which are often homogenous for all member
states are thwarted by heterogeneous structural and institutional characteristics which
give rise to different levels and patterns of ?nancial intermediary development. Therefore,
member states should work towards harmonizing cross-country differences in structural
and institutional characteristics that hamper the effectiveness of monetary policies.
5. Conclusion
The purpose of this paper has been to assess convergence within the CFA zone. It is
motivated by major lessons from the EMU crisis which suggest that the serious
disequilibria among EMU member states has resulted from regional monetary
arrangements not designed to be robust to a variety of shocks. In the investigations we
have distinguished the CEMAC from the UEMOA region, before assessing their
combined effect in the CFAzone. In the examinations we have assumed monetary policy
targets in?ation and ?nancial dynamics of depth, ef?ciency, activity and size, while real
sector policy targets economic performance in terms of GDP growth. We have also
provided the speed (time) of (for full) convergence.
From the ?ndings we notice that for the three panels (with respect all dynamics)
only ?nancial size within the CFA zone re?ects conditional convergence. There is no
form of convergence within the CEMAC zone. The UEMOA and CFA zones both re?ect
absolute convergence in liquid liabilities, banking activity, ?nancial activity and
?nancial size. There is absence of any form of convergence within any zone for
in?ation and GDP growth. On a speci?c note, we observe absolute convergence within
the UEMOA (CFA zone) for money supply (banking and ?nancial ef?ciency).
JFEP
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U
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V
E
R
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A
t
2
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:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
As a policy implication monetary policies which are often homogenous for all
member states are thwarted by heterogeneous structural and institutional characteristics
which give rise to different levels and patterns of ?nancial intermediary development.
Therefore, member states should work towards harmonizing cross-country differences
in structural and institutional characteristics that hamper the effectiveness of monetary
policies.
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Omega, Vol. 38, pp. 260-7.
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America”, Journal of Banking and Finance, Vol. 26, pp. 1113-41.
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Real and
monetary
policy
33
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l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Kim, S.J., Moshirian, F. and Wu, E. (2005), “Evolution of international stock and bond market
integration: in?uence of the European Monetary Union”, Journal of Banking and Finance,
Vol. 30, pp. 1507-34.
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Economic Review, Vol. 60, pp. 668-75.
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Prichett, L. (1997), “Divergence, big time”, Journal of Economic Perspectives, Vol. 11, pp. 3-17.
Scholtens, B. and Naaborg, I. (2005), Convergence of Financial Intermediary Activities Within
Europe: A Comparison of Banks’ Balance Sheets in the EU and the New EU Member
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aggregated stock-return volatility in emerging markets”, Journal of Banking and Finance,
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Further reading
Brada, J.C., Kutan, A.M. and Zhou, S. (2005), “Real and monetary convergence between the
European Union’s core and recent member countries: a rolling cointegration approach”,
Journal of Banking and Finance, Vol. 29, pp. 249-70.
JFEP
5,1
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a
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d
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P
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R
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2
1
:
4
6
2
4
J
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a
r
y
2
0
1
6
(
P
T
)
Appendix 1
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Table AI.
Summary statistics
Real and
monetary
policy
35
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d
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Appendix 2
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Table AII.
Correlation analysis
JFEP
5,1
36
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6
(
P
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Appendix 3
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f
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c
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F
d
F
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a
n
c
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s
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m
c
r
e
d
i
t
o
n
?
n
a
n
c
i
a
l
s
y
s
t
e
m
d
e
p
o
s
i
t
s
W
o
r
l
d
B
a
n
k
(
F
D
S
D
)
B
a
n
k
i
n
g
s
y
s
t
e
m
a
c
t
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y
P
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r
b
P
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c
r
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d
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t
b
y
d
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p
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t
b
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k
s
(
%
o
f
G
D
P
)
W
o
r
l
d
B
a
n
k
(
F
D
S
D
)
F
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a
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r
?
n
a
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c
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l
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n
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t
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t
u
t
i
o
n
s
(
%
o
f
G
D
P
)
W
o
r
l
d
B
a
n
k
(
F
D
S
D
)
F
i
n
a
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c
i
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l
s
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z
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b
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p
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W
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l
d
B
a
n
k
(
F
D
S
D
)
N
o
t
e
s
:
M
2
–
m
o
n
e
y
s
u
p
p
l
y
;
F
d
g
d
p
–
l
i
q
u
i
d
l
i
a
b
i
l
i
t
i
e
s
;
B
c
B
d
–
b
a
n
k
c
r
e
d
i
t
o
n
b
a
n
k
d
e
p
o
s
i
t
s
;
F
c
F
d
–
?
n
a
n
c
i
a
l
s
y
s
t
e
m
c
r
e
d
i
t
o
n
?
n
a
n
c
i
a
l
s
y
s
t
e
m
d
e
p
o
s
i
t
s
;
P
c
r
b
–
p
r
i
v
a
t
e
d
o
m
e
s
t
i
c
c
r
e
d
i
t
b
y
d
e
p
o
s
i
t
b
a
n
k
s
,
P
c
r
b
o
f
–
p
r
i
v
a
t
e
d
o
m
e
s
t
i
c
c
r
e
d
i
t
b
y
d
e
p
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s
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t
b
a
n
k
s
a
n
d
o
t
h
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r
?
n
a
n
c
i
a
l
i
n
s
t
i
t
u
t
i
o
n
s
;
D
b
a
c
b
a
–
d
e
p
o
s
i
t
b
a
n
k
a
s
s
e
t
s
o
n
c
e
n
t
r
a
l
b
a
n
k
a
s
s
e
t
s
p
l
u
s
d
e
p
o
s
i
t
b
a
n
k
a
s
s
e
t
s
;
W
D
I
–
w
o
r
l
d
d
e
v
e
l
o
p
m
e
n
t
i
n
d
i
c
a
t
o
r
s
;
F
D
S
D
–
?
n
a
n
c
i
a
l
d
e
v
e
l
o
p
m
e
n
t
a
n
d
s
t
r
u
c
t
u
r
e
d
a
t
a
b
a
s
e
Table AIII.
Variable de?nitions
Real and
monetary
policy
37
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o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
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V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
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a
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u
a
r
y
2
0
1
6
(
P
T
)
Appendix 4
Corresponding author
Simplice A. Asongu can be contacted at: [email protected]
Zones De?nitions Countries Number
CEMAC Economic and Monetary Community
of Central African States
Cameroon, Central African Republic,
Chad, Equatorial Guinea, Gabon
5
UEMOA Economic and Monetary Community
of West African States
Burkina Faso, Ivory Coast, Mali,
Niger, Senegal, Togo
6
Franc CFA Franc of African French Colonies Cameroon, Central African Republic,
Chad, Equatorial Guinea, Gabon,
Burkina Faso, Ivory Coast, Mali,
Niger, Senegal, Togo
11
Table AIV.
Presentation of countries
JFEP
5,1
38
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