Fighting consumer price inflation in Africa

Description
The purpose of this paper is to examine the effects of policy options in financial dynamics
(of money, credit, efficiency and size) on consumer prices. Soaring food prices have marked the
geopolitical landscape of African countries in the past decade.

Journal of Financial Economic Policy
Fighting consumer price inflation in Africa: What do dynamics in money, credit,
efficiency and size tell us?
Simplice A. Asongu
Article information:
To cite this document:
Simplice A. Asongu, (2013),"Fighting consumer price inflation in Africa", J ournal of Financial Economic
Policy, Vol. 5 Iss 1 pp. 39 - 60
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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. 20-38http://dx.doi.org/10.1108/17576381311317763
Wenling Lu, David A. Whidbee, (2013),"Bank structure and failure during the financial crisis", J ournal of
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Fighting consumer price
in?ation in Africa
What do dynamics in money, credit,
ef?ciency and size tell us?
Simplice A. Asongu
HEC-Management School, University of Lie `ge,
Lie `ge, Belgium
Abstract
Purpose – The purpose of this paper is to examine the effects of policy options in ?nancial dynamics
(of money, credit, ef?ciency and size) on consumer prices. Soaring food prices have marked the
geopolitical landscape of African countries in the past decade.
Design/methodology/approach – The sample is limited to a panel of African countries for which
in?ation is non-stationary. VAR models from both error correction and Granger causality perspectives
are applied. Analyses of dynamic shocks and responses are also covered and six batteries of
robustness checks are applied, to ensure consistency in the results.
Findings – First, it is found that there are signi?cant long-run equilibriums between in?ation and
each ?nancial dynamic. Second, when there is a disequilibrium, while only ?nancial depth and
?nancial size could be signi?cantly used to exert de?ationary pressures, in?ation is signi?cant in
adjusting all ?nancial dynamics. In other words, ?nancial depth and ?nancial size are more signi?cant
instruments in ?ghting in?ation than ?nancial ef?ciency and activity. Third, the ?nancial
intermediary dynamic of size appears to be more instrumental in exerting a de?ationary tendency than
?nancial intermediary depth. Fourth, the de?ationary tendency from money supply is double that
based on liquid liabilities.
Practical implications – Monetary policy aimed at ?ghting in?ation only based on bank deposits
may not be very effective until other informal and semi-formal ?nancial sectors are taken into account.
It could be inferred that, tight monetary policy targeting the ability of banks to grant credit (in relation
to central bank credits) is more effective in tackling consumer price in?ation than that, targeting the
ability of banks to receive deposits. In the same vein, adjusting the lending rate could be more effective
than adjusting the deposit rate. The insigni?cance of ?nancial allocation ef?ciency and ?nancial
activity as policy tools in the battle against in?ation could be explained by the (well documented)
surplus liquidity issues experienced by the African banking sector.
Social implications – This paper helps in providing monetary policy options in the ?ght against
soaring consumer prices. By keeping in?ationary pressures on food prices in check, sustained
campaigns involving strikes, demonstrations, marches, rallies and political crises that seriously
disrupt economic performance could be mitigated.
Originality/value – To the best of the author’s knowlege, there is yet no study that assesses
monetary policy options that could be relevant in addressing the dramatic surge in the price of
consumer commodities.
Keywords Banks, In?ation, Prices, Development, Panel, Africa, Monetary policy
Paper type Research paper
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – E31, G20, O10, O55, P50
The author is highly indebted to the editor and referees for their very useful comments.
Journal of Financial Economic Policy
Vol. 5 No. 1, 2013
pp. 39-60
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381311317772
Fighting
in?ation
in Africa
39
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1. Introduction
During the past decade, the world has seen a dramatic rise in the price of many staple
food commodities. For instance, the price of maize increased by 80 percent between 2005
and 2007 and has since increased further. Many other commodity prices have also
soared sharply over this period: milk powder by 90 percent, rice by 25 percent and wheat
by 70 percent. Such large variations in prices have had tremendous impacts on the
incomes of poor households in developing countries (FAO, 2007; The World Bank, 2008;
Ivanic and Martin, 2008). Assessing how to ?ght in?ation is particularly relevant given
its positive incidence onpoverty (Fujii, 2011), especially in a continent where povertyhas
remained stubbornly high despite ?nancial reforms and structural adjustment policies
(Asongu, 2012b). Also, while low in?ation may mitigate inequality (Bulir, 1998; Lope´z,
2004), high in?ation has been documented to have a negative income redistributive
effect (Albanesi, 2007) in recent African inequality literature (Asongu, 2012b).
The overall effect on poverty rates in African countries is contingent on whether the
gains to poor net producers outweigh the adverse impact on poor consumers. The
bearingof foodprices onthe situationof particular households also depends importantly
on the products involved, the patterns of households income and expenditure, as well as
policy responses of governments. On account of existing analyses, the impacts of higher
food prices on poverty and inequality are likely to be very diverse; depending on the
reasons for the price change and the structure of the economy (Ravallion and Lokshin,
2005; Hertel and Winters, 2006). While the effects of soaring food prices on inequality
and poverty may depend on certain circumstances, most analysts agree that,
sustained increased in food prices ultimately leads to sociopolitical unrests like those
experienced in 2008.
The World Bank (2008) has also raised concerns over the impact of high prices on
socio-political stability. Most studies con?rmthe link between rising food prices and the
recent waves of revolutions that have marked the geopolitical landscape of developing
countries over the last couple of months (The World Bank, 2008; Wodon and Zaman,
2010). The premises of the Arab Spring and hitherto unanswered questions about some
of its dynamics could be traced to poverty; owing to unemployment and rising
food prices. “We will take to the streets in demonstrations or we will steal,” a 30-year-old
Egyptian woman in 2008 vented her anger as she stood outside a bakery. Riots and
demonstrations linked to soaring consumer prices took place in over 30 countries
between 2007 and 2008. The Middle East encountered food riots in Egypt, Jordan,
Morocco and Yemen. In Ivory Coast, thousands marched to the home of President then
Laurent Gbagbo chanting: “you are going to kill us”, “ we are hungry”, “life is too
expensive”, etc. Similar demonstrations followed in many other African countries,
including, Cameroon, Senegal, Ethiopia, Burkina Faso, Mozambique, Mauritania and
Guinea. In Latin America, violent clashes and demonstrations over rising food prices
occurred in Guatemala, Peru, Nicaragua, Bolivia, Argentina, Mexico and the Haitian
Prime Minister was even toppled following food riots. In Asia, people ?ooded the streets
in Bangladesh, Cambodia, Thailand, India and the Philippines. Even North Korea
surprisingly experienced an incident in which market women gathered to protest
against restrictions on their ability to trade in food (Hendrix et al., 2009). The geopolitical
landscape in the last couple of months has also revolved around the inability of some
political regimes to implement concrete policies that ensure the livelihoods of their
citizens. Tunisia, Egypt, Morocco, Senegal, Uganda, Zambia, Mauritania, Sudan,
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Western Sahara and most recently Nigeria are some countries that have witnessed major
or minor unrests via techniques of civil resistance in sustained campaigns involving
strikes, demonstrations, marches and rallies.
Whereas the literature on the causes and impacts of the crisis in global food prices
in the developing world has mushroomed in recent years (Piesse and Thirtle, 2009;
Wodon and Zaman, 2010; Masters and Shively, 2008), we are unaware of studies that
have closely examined how ?nancial policies affected consumer prices. Remedial
policy and pragmatic choices aimed at ?ghting in?ation that have been documented
include both short and medium term responses (SIFSIA, 2011). Short-term and
immediate measures include: input vouchers and input trade fairs (seeds, fertilizer
and tools) for vulnerable farmers; reinforcement of capacity (training and equipment)
in income generating activities; safety-nets (cash transfers or food vouchers); tax
measures and government policies. Medium term measures could be clubbed into three
strands: trade and market measures; production and productivity incentives;
coordination and activation of food security plan. First, trade and market measures
include: reduction of import taxes on basic food items and grain-export bans when
needed; strengthening the food and agricultural market information system;
conducting of value chain analysis; building of ef?cient marketing institutions;
facilitation of farming contract arrangements; lowering of distribution cost; strategic
reserve support and government anticipation of price increase. Second, production and
productivity incentives include: investing in agriculture; addressing of poor harvest
and promotion of shelf-life products. Third, coordination and activation of food
security action plan involve: coordination and coherence among various agencies
engaged in price stabilization efforts; comprehensiveness of multi-sectoral responses to
price hikes and coordination (synchronization) of food insecurity plan, in a bid to
achieve the maximum impact.
According to Von Braun (2008), monetary and exchange rate policy responses were
not effective in addressing food in?ation. This revelation by the Director General of
the International Food Policy Research Institute has motivated us to peruse the
literature in search of monetary policies on soaring food prices. Finding none, the
present paper ?lls this gap in the literature by assessing how ?nancial development
dynamics in money, credit, activity, ef?ciency and size could be exploited in monetary
policy to keep food prices in check. In plainer terms, this work aims to assess the
impact of the following dynamics on food prices:
.
Money: the role of ?nancial depth (in dynamics of overall economic money
supply and ?nancial system liquid liabilities).
.
Credit: the incidence of ?nancial activity dynamics (in banking and ?nancial
system perspectives).
.
Ef?ciency: the impact of ?nancial intermediary allocation ef?ciency (from
banking and ?nancial system angles).
.
Size: the part ?nancial size plays.
Another appeal of this paper is the scarcity of literature on the effect of ?nancial
development on in?ation despite a substantial body of work on the economic and
?nancial consequences of in?ation (Barro, 1995; Bruno and Easterly, 1998; Bullard and
Keating, 1995; DeGregorio, 1992; Boyd et al., 2001).
Fighting
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The rest of the paper is organized as follows. Section 2 presents data and discusses
the methodology. Empirical analysis is outlined in Section 3. Discussion and policy
implications are covered in Section 4. Section 5 concludes.
2. Data and methodology
2.1 Data
We examine a panel of ten African countries with data from the ?nancial development
and structure database (FDSD) and African Development Indicators (ADI) of the
World Bank (WB). The ensuing balanced panel is restricted from 1980 to 2010 owing to
constraints in data availability. Information on summary statistics and correlation
analysis is detailed in Appendices 1 and 3, respectively. De?nition of the variables and
corresponding sources are presented in Appendix 2. Countries in the sample include:
Algeria, Egypt, Lesotho, Morocco, Nigeria, Sudan, Tunisia, Uganda[1], Zambia and
Tanzania[2]. The limitation to these countries is primarily based on the inability of
some African countries to exhibit a unit root in consumer price in?ation. Given the
problem statement of the study, it is interesting to have non-stationary consumer price
in?ation for consistent modeling. Hence, in accordance with recent African law-?nance
literature (Asongu, 2011f), CFA franc[3] countries of the CEMAC[4] and UEMOA[5]
zones have not been included[6]. Beside the justi?cations for eliminating CFA franc
countries provided by preliminary analysis and recent theoretical postulations
(Asongu, 2011f), the seminal work of Mundell (1972) has shown that, African countries
with ?exible exchange rates regimes have more to experience in the ?ght against
in?ation than their counterparts with ?xed exchange rate regimes[7].
In line with the literature (Bordo and Jeanne, 2002; Hendrix et al., 2009) and the
problem statement, the dependent variable is measured in terms of annual percentage
change in the consumer price index (CPI). For clarity in organization, the independent
variables are presented in terms of depth, ef?ciency, activity and size.
First, from a ?nancial intermediary depth standpoint, we are consistent with the
FDSD and recent African ?nance literature (Asongu, 2011a, b, c, d) in measuring
?nancial depth both from overall-economic and ?nancial system perspectives with
indicators of broad money supply (M2/GDP) and ?nancial system deposits (Fdgdp),
respectively. Whereas the former represents the monetary base plus demand, saving
and time deposits, the latter denotes liquid liabilities of the ?nancial system. Since
we are dealing exclusively with developing countries, we distinguish liquid liabilities
from money supply because a great chunk of the monetary base does not transit
via the banking sector (Asongu, 2011e). The two indicators are in ratios of GDP
(see Appendix 2) and can robustly check one another as either account for over
98 percent of information in the other (see Appendix 3).
Second, by ?nancial ef?ciency[8] here, we neither refer to the pro?tability-related
concept nor to the production ef?ciency of decision making units in the ?nancial sector
(throughdataenvelopment analysis (DEA)). What the paper aims to elucidate is the ability
of banks to effectively ful?ll their fundamental role of transforming mobilized deposits
into credit for economic operators. We adopt indicators of 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”). As with ?nancial depth
dynamics, these two ?nancial allocation ef?ciency proxies can check each other as either
represent more than 95 percent of variability in the other (see Appendix 3).
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Third, in accordance with the FDSD, we proxy for ?nancial intermediary
development size as the ratio of “deposit bank assets” to “total assets” (deposit bank
assets on central bank assets plus deposit bank assets: Dbacba).
Fourth, by ?nancial intermediary activity, the paper points out the ability of banks to
grant credit to economic operators. We appreciate both bank-sector-activity and
?nancial-sector-activity with “private domestic credit by deposit banks: Pcrb” and
“private credit by domestic banks and other ?nancial institutions: Pcrbof”, respectively.
The former measure checks the latter as it represents more than 98 percent of
information in the latter (see Appendix 3).
2.2 Methodology
The estimation technique typically follows mainstream literature on ?ghting in?ation
(Bernanke and Gertler, 1995; Detken and Smets, 2004; Goujon, 2006). The estimation
approach entails the following steps: unit root tests, cointegration tests, vector error
correction estimation, Granger causality modeling and impulse-response analysis.
Robustness checks are ensured by:
.
the use of alternative ?nancial indicators;
.
consideration of homogenous and heterogeneous assumptions in both unit root
and cointegration tests;
.
optimal lag selection for goodness of ?t in model speci?cation consistent with the
recommendations of Liew (2004);
.
usage of bivariate analysis to limit causality misspeci?cation issues;
.
application of vector error correction and simple Granger causality; and
.
verifying that, the signs and intervals of the error correction terms (ECTs) are
consistent with theory.
3. Empirical analysis
3.1 Unit root tests
We begin by testing for serial correlations with two types of panel unit root tests. When
the variables are not stationary in level, we proceed to test for stationarity in ?rst
difference. While short-run Granger causality presupposes the absence of unit roots, the
vector error correction model (VECM) requires that the variables have a unit root
(non-stationary) in level (series). There are two main types of panel unit root tests: ?rst
generational (that assumes cross-sectional independence); and second generational
(based on cross-sectional dependence). Aprecondition for the application of the latter is a
cross-sectional dependence test which is possible only and only if the number of
cross-sections (N) in a panel exceed the number of periods in the cross-sections (T).
Hence, we focus on the ?rst generational type. To this end, both the Levin, Lin and Chu
(LLC, 2002) and Im, Pesaran and Shin (IPS, 2003) tests are applied. Whereas the former is
a homogenous based panel unit root test (common unit as null hypothesis), the latter is a
heterogeneous oriented test (individual unit roots as null hypotheses). In case of con?ict
of interest in the results, IPS (2003) takes precedence over LLC (2002) in decision making
because, according to Maddala and Wu (1999), the alternative hypothesis of LLC (2002)
is too powerful. Consistent with Liew (2004), goodness of ?t is ensured by the
Hannan-Quinn information criterion (HQC) and the Akaike information criterion (AIC)
for the LLC (2002) and IPS (2003) tests, respectively[9].
Fighting
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Table I reports the panel unit root tests results. It can be observed that, all the
variables exhibit a unit root in level; that is, they are non-stationary. However, on
account of the IPS (2003) results, the variables are overwhelmingly stationary in ?rst
difference. These ?ndings indicate the possibility of a long-run equilibrium
(cointegration) among variables; because according to Engel-Granger theorem, two
variables that are not stationary may have a linear combination in the long-run (Engle
and Granger, 1987).
3.2 Cointegration tests
According to the cointegration theory, two or more series that have a unit root may
have a linear combination (equilibrium) in a long-run. In this equilibrium, permanent
movements of one factor (variable) affect permanent movements in the other factor.
To investigate this long-run relationship, we test for cointegration using
Engle-Granger based Pedroni and Engle-Granger Kao tests. Consistent with Camarero
and Tamarit (2002), the advantage of applying these two tests is that, while the former
(Pedroni, 1999) is heterogeneous, the latter (Kao, 1999) is homogenous based.
Implementation of both tests is in line with our earlier application of both homogenous
(LLC) and heterogeneous (IPS) unit root tests. Similar deterministic trend components
used inunit root tests are applied. However, the Pedroni (1999) test will be given priorityin
event of con?ict of interest because, it has more deterministic components[10]. Optimal lag
selection for goodness of ?t is by the AIC. The choice of bivariate statistics instead of
multivariate statistics is to avoid misspeci?cation in causality estimations[11].
Table II reports results of the cointegration tests. While Panel A reports the
long-term relationship between ?nancial depth (ef?ciency) and in?ation, Panel B
reveals ?ndings for the long-run equilibrium between ?nancial activity (size) and
in?ation. It could be observed from the Engle-Granger based Pedroni test that, there is
overwhelming evidence of a long-term relationship between each ?nancial dynamic
and in?ation. It follows that in the long-run, permanent changes in each ?nancial
dynamic affect permanent changes in in?ation. Hence, the need to investigate
short-term adjustments to this long-run equilibrium with the VECM.
3.3 Vector error correction model
Let us consider in?ation and a ?nancial dynamic with no lagged difference, such that:
Inflation
i;t
¼ bFinance
i;t
ð1Þ
The resulting VECMs are the following:
DInflation
i;t
¼ ›ðInflation
i;t21
2bFinance
i;t21
Þ þ1
1;t
ð2Þ
DFinance
i;t
¼ sðFinance
i;t21
2bInflation
i;t21
Þ þ1
2;t
ð3Þ
In equations (1) and (2), the right hand terms are the ECTs. At equilibrium, the value of the
ECT is zero. When the ETC is non-zero, it implies that in?ation and a ?nancial dynamic
have deviatedfromthe long-runequilibrium; andthe ECThelps eachvariable to adjust and
partially restore the equilibrium. The speeds of these adjustments are measured by › and
s for in?ation and a given ?nancial dynamic, respectively. Hence, equations (1) and (2) are
replicated for each “?nancial dynamic and in?ation” pair. The same deterministic trend
assumptions used in the cointegration tests are applied and optimal lag selection for
goodness of ?t in model speci?cation is in line with the AIC (Liew, 2004).
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*
*
2
2
.
1
1
5
*
*
2
3
.
2
4
*
*
*
2
1
.
3
5
7
*
c
t
2
1
.
9
1
6
*
*
2
0
.
4
1
5
2
.
6
3
7
1
.
7
9
6
2
2
.
0
3
2
*
*
2
1
.
3
6
7
*
2
2
.
0
2
6
*
*
2
0
.
9
2
4
P
a
n
e
l
B
:
?
n
a
n
c
i
a
l
a
c
t
i
v
i
t
y
,
?
n
a
n
c
i
a
l
s
i
z
e
a
n
d
i
n
?
a
t
i
o
n
F
i
n
a
n
c
i
a
l
a
c
t
i
v
i
t
y
F
i
n
.
s
i
z
e
I
n
?
a
t
i
o
n
F
i
n
a
n
c
i
a
l
a
c
t
i
v
i
t
y
F
i
n
.
s
i
z
e
I
n
?
a
t
i
o
n
P
c
r
b
P
c
r
b
o
f
D
b
a
c
b
a
I
n
?
.
P
c
r
b
P
c
r
b
o
f
D
b
a
c
b
a
I
n
?
.
L
e
v
e
l
c
1
.
5
1
9
1
.
0
5
7
2
.
1
7
5
2
0
.
2
7
1
3
.
0
9
9
2
.
2
7
9
2
.
4
5
4
0
.
6
9
4
c
t
2
.
8
8
7
2
.
6
4
4
0
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3
0
7
0
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2
6
4
3
.
2
6
6
2
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6
3
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4
9
4
0
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3
3
F
i
r
s
t
d
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f
f
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r
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n
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e
c
0
.
4
3
1
2
0
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1
6
7
2
2
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0
4
2
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3
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1
4
2
2
1
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3
6
7
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1
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7
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4
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3
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5
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5
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t
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3
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2
6
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3
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5
8
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7
.
0
0
4
6
.
8
4
8
2
1
.
2
2
3
2
1
.
9
4
7
*
*
2
2
.
3
8
*
*
*
2
3
.
6
9
*
*
*
N
o
t
e
s
:
S
i
g
n
i
?
c
a
n
t
a
t
:
*
1
0
,
*
*
5
a
n
d
*
*
*
1
p
e
r
c
e
n
t
;
“
c
”
a
n
d
“
c
t
”
:
“
c
o
n
s
t
a
n
t
”
a
n
d
“
c
o
n
s
t
a
n
t
a
n
d
t
r
e
n
d
”
,
r
e
s
p
e
c
t
i
v
e
l
y
;
m
a
x
i
m
u
m
l
a
g
i
s
8
a
n
d
o
p
t
i
m
a
l
l
a
g
s
a
r
e
c
h
o
s
e
n
v
i
a
H
Q
C
f
o
r
L
L
C
t
e
s
t
a
n
d
A
I
C
f
o
r
I
P
S
t
e
s
t
;
o
p
t
i
m
a
l
l
a
g
f
o
r
t
h
e
m
o
s
t
p
a
r
t
i
s
2
;
L
L
C
–
L
e
v
i
n
,
L
i
n
a
n
d
C
h
u
(
2
0
0
2
)
;
I
P
S
–
I
m
,
P
e
s
a
r
a
n
a
n
d
S
h
i
n
(
2
0
0
3
)
;
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
i
n
g
s
y
s
t
e
m
e
f
?
c
i
e
n
c
y
;
F
c
F
d
–
?
n
a
n
c
i
a
l
s
y
s
t
e
m
e
f
?
c
i
e
n
c
y
;
P
c
r
b
–
b
a
n
k
i
n
g
s
y
s
t
e
m
a
c
t
i
v
i
t
y
;
P
c
r
b
o
f
–
?
n
a
n
c
i
a
l
s
y
s
t
e
m
a
c
t
i
v
i
t
y
;
D
a
b
c
b
a
–
?
n
a
n
c
i
a
l
s
i
z
e
;
I
n
?
.
–
I
n
?
a
t
i
o
n
;
F
i
n
.
–
?
n
a
n
c
i
a
l
Table I.
Panel unit root tests
Fighting
in?ation
in Africa
45
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
)
P
a
n
e
l
A
:
d
e
p
t
h
,
e
f
?
c
i
e
n
c
y
a
n
d
i
n
?
a
t
i
o
n
F
i
n
a
n
c
i
a
l
d
e
p
t
h
a
n
d
i
n
?
a
t
i
o
n
F
i
n
a
n
c
i
a
l
e
f
?
c
i
e
n
c
y
a
n
d
i
n
?
a
t
i
o
n
M
2
a
n
d
i
n
?
a
t
i
o
n
F
d
g
d
p
a
n
d
i
n
?
a
t
i
o
n
B
c
B
d
a
n
d
i
n
?
a
t
i
o
n
F
c
F
d
a
n
d
i
n
?
a
t
i
o
n
c
c
t
c
c
t
c
c
t
c
c
t
E
n
g
l
e
-
G
r
a
n
g
e
r
b
a
s
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d
P
e
d
r
o
n
i
t
e
s
t
f
o
r
h
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t
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r
o
g
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n
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o
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s
p
a
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l
P
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l
v
-
s
t
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t
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s
t
i
c
s
2
0
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4
8
4
2
1
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5
9
8
2
0
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7
1
2
2
2
.
0
6
6
2
0
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8
6
1
2
2
.
4
4
7
2
1
.
1
6
0
2
2
.
8
7
1
P
a
n
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l
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h
o
-
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t
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c
s
2
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6
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1
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7
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2
2
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4
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*
*
2
2
.
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5
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*
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2
2
.
6
2
2
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*
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2
1
.
8
9
6
*
*
P
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n
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l
P
P
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s
t
a
t
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s
t
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s
2
1
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8
2
8
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2
3
.
7
0
2
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*
*
2
2
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0
8
3
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2
3
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4
7
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2
3
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1
0
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2
4
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2
7
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*
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2
3
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1
9
3
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3
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6
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P
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n
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l
A
D
F
-
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c
s
2
0
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1
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1
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2
6
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1
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1
3
1
2
1
.
6
8
1
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0
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6
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2
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6
2
6
0
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1
1
1
G
r
o
u
p
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o
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t
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s
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c
s
2
0
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3
7
3
2
0
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3
4
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2
0
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7
9
7
2
0
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2
8
7
2
1
.
6
7
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*
2
1
.
5
2
5
*
2
1
.
2
0
8
2
0
.
7
4
2
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r
o
u
p
P
P
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c
s
2
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5
3
4
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4
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9
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2
2
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3
6
2
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2
4
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3
3
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*
2
1
.
9
1
1
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2
2
.
6
6
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2
2
.
7
5
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2
6
.
4
7
*
*
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r
o
u
p
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c
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2
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3
0
0
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1
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9
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1
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3
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1
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0
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0
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1
0
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1
8
3
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2
4
7
0
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5
0
8
E
n
g
l
e
-
G
r
a
n
g
e
r
b
a
s
e
d
K
a
o
t
e
s
t
f
o
r
h
o
m
o
g
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n
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u
s
p
a
n
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l
A
D
F
t
-
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a
t
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s
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c
s
0
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0
3
6
n
a
2
0
.
5
9
2
n
a
2
0
.
6
9
6
n
a
2
1
.
7
5
2
*
*
n
a
P
a
n
e
l
B
:
a
c
t
i
v
i
t
y
,
s
i
z
e
a
n
d
i
n
?
a
t
i
o
n
F
i
n
a
n
c
i
a
l
a
c
t
i
v
i
t
y
a
n
d
i
n
?
a
t
i
o
n
F
i
n
a
n
c
i
a
l
s
i
z
e
a
n
d
i
n
?
a
t
i
o
n
P
c
r
b
a
n
d
i
n
?
a
t
i
o
n
P
c
r
b
o
f
a
n
d
i
n
?
a
t
i
o
n
D
b
a
c
b
a
a
n
d
i
n
?
a
t
i
o
n
c
c
t
c
c
t
c
c
t
E
n
g
l
e
-
G
r
a
n
g
e
r
b
a
s
e
d
P
e
d
r
o
n
i
t
e
s
t
f
o
r
h
e
t
e
r
o
g
e
n
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o
u
s
p
a
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l
P
a
n
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l
v
-
s
t
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t
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s
t
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c
s
2
0
.
8
8
5
2
2
.
6
0
8
2
0
.
6
3
9
2
2
.
3
7
7
0
.
3
3
0
2
1
.
6
5
3
P
a
n
e
l
r
h
o
-
s
t
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t
i
s
t
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c
s
2
2
.
4
4
*
*
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2
2
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1
2
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2
2
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7
1
9
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2
2
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0
9
7
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2
2
.
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7
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9
6
4
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P
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l
P
P
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s
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c
s
2
2
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7
1
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2
3
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6
9
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2
2
.
9
4
9
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*
2
3
.
7
2
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*
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2
3
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0
3
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2
3
.
1
7
*
*
*
P
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n
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l
A
D
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c
s
0
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2
0
2
0
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7
9
5
2
0
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3
9
9
2
0
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0
7
4
2
3
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0
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2
2
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1
5
4
*
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r
o
u
p
r
h
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s
2
1
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1
2
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6
1
2
1
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7
6
4
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2
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3
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5
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2
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1
8
7
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r
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u
p
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c
s
2
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6
0
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2
4
.
5
1
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2
3
.
2
1
5
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*
*
2
5
.
6
0
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2
2
.
1
9
1
*
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2
2
.
3
5
8
*
*
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G
r
o
u
p
A
D
F
-
s
t
a
t
i
s
t
i
c
s
0
.
7
0
3
0
.
6
9
7
2
0
.
1
4
0
2
0
.
5
8
7
2
0
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4
3
9
2
0
.
2
1
6
E
n
g
l
e
-
G
r
a
n
g
e
r
b
a
s
e
d
K
a
o
t
e
s
t
f
o
r
h
o
m
o
g
e
n
o
u
s
p
a
n
e
l
2
0
.
3
1
7
n
a
2
0
.
0
6
9
n
a
2
0
.
3
8
9
n
a
N
o
t
e
s
:
S
i
g
n
i
?
c
a
n
t
a
t
:
*
1
0
,
*
*
5
a
n
d
*
*
*
1
p
e
r
c
e
n
t
;
“
c
”
a
n
d
“
c
t
”
–
“
c
o
n
s
t
a
n
t
”
a
n
d
“
c
o
n
s
t
a
n
t
a
n
d
t
r
e
n
d
”
,
r
e
s
p
e
c
t
i
v
e
l
y
;
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
i
n
g
s
y
s
t
e
m
e
f
?
c
i
e
n
c
y
;
F
c
F
d
–
?
n
a
n
c
i
a
l
s
y
s
t
e
m
e
f
?
c
i
e
n
c
y
;
P
c
r
b
–
b
a
n
k
i
n
g
s
y
s
t
e
m
a
c
t
i
v
i
t
y
;
P
c
r
b
o
f
–
?
n
a
n
c
i
a
l
s
y
s
t
e
m
a
c
t
i
v
i
t
y
;
D
a
b
c
b
a
–
?
n
a
n
c
i
a
l
s
i
z
e
;
P
P
–
P
h
i
l
l
i
p
s
-
P
e
r
o
n
;
A
D
F
–
a
u
g
m
e
n
t
e
d
D
i
c
k
e
y
F
u
l
l
e
r
;
n
o
d
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t
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m
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n
i
s
t
i
c
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p
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o
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;
m
a
x
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m
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m
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a
g
s
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s
8
a
n
d
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p
t
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m
a
l
l
a
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s
a
r
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c
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e
n
v
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a
A
I
C
;
o
p
t
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m
a
l
l
a
g
s
f
o
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m
o
s
t
p
a
r
t
i
s
1
,
w
i
t
h
e
x
c
e
p
t
i
o
n
s
o
f
t
e
s
t
s
f
o
r
?
n
a
n
c
i
a
l
s
y
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t
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m
e
f
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c
i
e
n
c
y
a
n
d
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n
a
n
c
i
a
l
s
y
s
t
e
m
a
c
t
i
v
i
t
y
w
h
e
r
e
3
a
n
d
2
l
a
g
s
a
r
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u
s
e
d
,
r
e
s
p
e
c
t
i
v
e
l
y
Table II.
Bivariate panel
cointegration tests
(Pedroni and
Kao Engle-Granger
based tests)
JFEP
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U
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A
t

2
1
:
4
6

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
Based on results reported in Table III, while only ?nancial depth and ?nancial size are
exogenous to de?ationary pressures, in?ation is exogenous to all ?nancial intermediary
dynamics under consideration. In other words, when there is a disequilibrium, while
only ?nancial depth and ?nancial size could be signi?cantly used to exert in?ationary
pressures, in?ation is signi?cant in adjusting all ?nancial dynamics. Panels Aand Bare
based on equations (2) and (3), respectively. The ECTs have the expected signs and are in
the right interval (See Section 3.5 on robustness checks for discussion below). In event of
a shock, short-run adjustments of ?nance to the equilibrium (Panel B) are faster than
short-term adjustments of in?ation (Panel A). Hence, ?nance is more endogenous to
in?ation than ?nance is exogenous to in?ation. Since some models (?nancial ef?ciency
and activity in Panel A for the most part) are cointegrated with in?ation but have no
signi?cant corresponding short-term adjustments to long-run equilibrium, we proceed
to analyze the relationship of the variables under consideration by simple Granger
causality.
3.4 Granger causality
Considering a basic bivariate ?nite-order VAR model, simple Granger causality is
based on the assessment of how past values of a ?nancial dynamic could help
past values of in?ation in explaining the present value of in?ation. In mainstream
literature, this model is applied on variables that are not cointegrated (that is, pairs
that are stationary in levels). However, within our framework we are applying this
test to all pairs in “?rst difference” for two reasons: ensure comparability and;
the model can be applied only when variables are stationary and ours are stationary
only in “?rst difference”. In light of the above, the resulting VAR models are the
following:
DInflation
i;t
¼
X
p
j¼1
l
ij
DInflation
i;t2j
þ
X
q
j¼0
d
ij
DFinance
i;t2j
þm
i
þ1
i;t
ð4Þ
DFinance
i;t
¼
X
p
j¼1
l
ij
DFinance
i;t2j
þ
X
q
j¼0
d
ij
DInflation
i;t2j
þm
i
þ1
i;t
ð5Þ
The null hypothesis of equation (4) is the position that, Finance does not Granger cause
In?ation. Hence, a rejection of the null hypothesis is captured by the signi?cant
F-statistics; which is the Wald statistics for the joint hypothesis that estimated
parameters of lagged values equal zero. Optimal lag selection for goodness of ?t is in
accordance with the AIC (Liew, 2004). Based on the results reported in Table III, while
?nancial size causes in?ation, the latter causes ?nancial depth (money supply and
liquidity liabilities).
3.5 Robustness checks
In order to ensure that our results are robust, we have performed the following:
(1) With the exception of ?nancial size (for every ?nancial dynamic) two indicators
have been employed.
Hence, the ?ndings have encapsulated measures of ?nancial intermediary
performance both from banking and ?nancial system perspectives.
Fighting
in?ation
in Africa
47
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P
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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
)
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;
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2
–
m
o
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;
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d
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d
p
–
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c
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d
–
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d
–
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f
?
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c
y
;
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c
r
b
–
b
a
n
k
i
n
g
s
y
s
t
e
m
a
c
t
i
v
i
t
y
;
P
c
r
b
o
f
–
?
n
a
n
c
i
a
l
s
y
s
t
e
m
a
c
t
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v
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a
b
c
b
a
–
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a
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e
;
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C
M
–
v
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c
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o
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;
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C
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–
e
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;
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)
;
f
o
r
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r
a
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c
a
u
s
a
l
i
t
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p
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m
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o
n
i
s
b
a
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d
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h
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A
I
C
;
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(
t
)
–
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s
h
e
r
(
s
t
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–
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Table III.
Vector error correction
model and Granger
causality estimations
JFEP
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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
)
(2) Both homogenous and heterogeneous assumptions are applied in the unit root
and cointegration tests.
(3) Optimal lag selection for goodness of ?t in model speci?cations is in line with
the recommendations of Liew (2004).
(4) By using bivariate analysis in cointegration tests and corresponding VECM
estimations, we have limited causality misspeci?cation issues.
(5) Both VECM and simple Granger VAR speci?cations for, respectively, long-run
and short-term causality have been applied.
(6) The signs and intervals of the ECTs conform to theory.
While the ?rst ?ve points have already been elucidated above, the sixth has only been
highlighted. Hence, the need to discuss its relevance to the results. In principle, the
speed of adjustment of the parameters should be between zero and “minus one” (0, 21).
If the ECTs are not within this interval, then either the model is misspeci?ed (and
needs adjustment) or the data is inadequate (perhaps owing to issues with degrees of
freedom)[12].
4. Dynamic responses to shocks and policy implications
4.1 Dynamic responses
Using a Choleski decomposition on a VAR with ordering:
.
in?ation; and
.
a ?nancial dynamic.
we compute impulse response functions (IRFs) for in?ation and ?nancial dynamics.
However, given the character of the problemstatement in this study, policy implications
will be based on the responses of in?ation to shocks in ?nancial dynamics. In other
words, how one standard deviation in ?nancial dynamic innovations affect
in?ation. A negative response of in?ation to a (positive) shock in a ?nancial dynamic
will imply a de?ationary tendency in the CPI. Hence, an effective shock in the ?ght
against in?ation. Appendices 4-10 present graphical representations corresponding to
the IRFs.
The dotted lines shown around the IRFs in Appendices 4-10 are the two standard
deviation bands, which are used as a measure of signi?cance (Age´nor et al., 1997, p. 19).
A number of results are noteworthy. First, the results obtained for dynamics of each
?nancial dimension are broadly similar, indicating robustness of our results to the choice
of corresponding ?nancial dynamics within each ?nancial dimension[13]. Second,
shocks in ?nancial dynamics have a signi?cant impact on the temporary component of
in?ation. Broadly across the IRFs, a decrease in a ?nancial intermediary performance
dynamic leads to a (temporary) decrease in in?ation (de?ation)[14]. This effect is
consistent with the theoretical predictions and illustrate the contraction of ?nancial
intermediary activities as a measure of ?ghting in?ation. Though all ?nancial
adjustments from a VEC framework are signi?cant with the right signs, from a
VAR-based IRFs framework (owing to the problem statement), policy implications will
only be based on de?ationary adjustments (shocks in ?nancial dynamics of depth and
size) because, these have signi?cant adjustment terms from a VECM-based framework
(see Panel A of Table III).
Fighting
in?ation
in Africa
49
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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
)
Hence, the following ?ndings have been established:
.
There are signi?cant long-run equilibriums between in?ation and each ?nancial
dynamic.
.
When there is a disequilibrium, while only ?nancial depth and ?nancial size
could be signi?cantly used to exert de?ationary pressures, in?ation is signi?cant
in adjusting all ?nancial dynamics.
In other words, ?nancial depth and ?nancial size are more signi?cant
instruments in ?ghting in?ation than ?nancial ef?ciency and activity.
.
The ?nancial intermediary dynamic of size appears to be more instrumental in
exerting a de?ationary tendency than ?nancial intermediary depth.
.
The de?ationary tendency from money supply is double that based on liquid
liabilities.
4.2 Policy implications, caveats and future directions
Four main policy implications could be derived fromthe ?ndings established above. First,
the fact that the effectiveness of money supply as an instrumental tool in ?ghting in?ation
almost doubles that of liquid liabilities (bank deposits) is consistent with theoretical
postulations that, a great chunk of the monetary base in developing countries does not
transit throughthe bankingsector. Hence, monetarypolicyaimedat ?ghtingin?ationonly
based on bank deposits may not be very effective until other informal and semi-formal
?nancial sectors are taken into account. An eloquent example is the growing phenomenon
of mobile banking in African countries (that constitute the monetary base but) not
captured by mainstream monetary policies based on formal ?nancial activities (Asongu,
2012f). Second, ?nancial intermediary size[15] appears to be more effective than ?nancial
intermediary dynamics of depth (money supply and bank deposits). In other words,
decreasing ?nancial intermediary assets (in relation to central bank assets) more
substantially exerts de?ationary pressures on consumer prices. It could therefore be
inferredthat, tight monetarypolicytargetingthe abilityof banks togrant credit (inrelation
to central bank credits) is more effective in ?ghting consumer price in?ation, than that
targeting the ability of banks to receive deposits. In the same vein, adjusting the lending
rate could be more effective than adjusting the deposit rate. Third, we have seen that
?nancial depth and ?nancial size are more signi?cant instruments in ?ghting in?ation
than?nancial ef?ciency[16] andactivity[17]. The de?ationary effects of reducing ?nancial
allocation ef?ciency and credit allocation have had the rights signs but not signi?cant.
While inherent surplus liquidityissues inAfricanbanks couldexplainthe insigni?cance of
the ef?ciency dimension (Saxegaard, 2006), we expected the in?ation-mitigation effect of
?nancial activity to be signi?cant. The insigni?cant character of ?nancial activity as an
effective instrument in ?ghting in?ation may be sample-speci?c. Hence, the result should
not be treated with caution and not generalized to all African countries[18].
To the best of our knowledge, the absence of literature dedicated to examining the
bearing of ?nancial dynamics on in?ation makes our results less comparable. In this
paper, we have onlyconsidered?nancial intermediarydeterminants of in?ation. But inthe
real world, in?ation is endogenous to a complex set of variables: exchange rates, wages,
price controls, etc. Thus, the interaction of money, credit, ef?ciency and size elasticities of
in?ation with other determinants of in?ation could result in other dynamics of consumer
price variations.
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Hence, it would be interesting to replicate the analysis in a multivariate VAR
context. Another interesting future research direction could be to assess whether the
?ndings apply to other developing countries. Also, since a substantial chunk of the
monetary base is now captured by the burgeoning phenomenon of mobile banking,
investigating how mobile-banking oriented in?ation could be managed is a
particularly relevant future research focus.
5. Conclusion
In recent years, the African geopolitical landscape has been marked with political strife
and social unrests due to increases in consumer prices. This paper had assessed how
?nancial intermediary development dynamics could be exploited in monetary policy to
keep food prices in check. We have investigated the impact by examining the roles of
money, credit, ef?ciency and ?nancial size on in?ationary pressures. Four main
?ndings have been established:
(1) There are signi?cant long-run equilibriums between in?ation and each ?nancial
dynamic.
(2) When there is a disequilibrium, while only ?nancial depth and ?nancial size
could be signi?cantly used to exert de?ationary pressures, in?ation is
signi?cant in adjusting all ?nancial dynamics.
In other words, ?nancial depth and ?nancial size are more signi?cant
instruments in ?ghting in?ation than ?nancial ef?ciency and activity.
(3) The ?nancial intermediary dynamic of size appears to be more instrumental in
exerting a de?ationary tendency than ?nancial intermediary depth.
(4) The de?ationary tendency from money supply is double that based on liquid
liabilities.
(5) Policy implications and future research directions have been discussed.
Notes
1. “Despite decelerating to 27.0 percent in December 2011 from a high of 30.4 percent in
October, in?ation in Uganda is still far higher than expected, given the 3 percent rate at the
end of 2010. Year-on-year food in?ation spiked to 45.6 percent in October 2011, while
non-food in?ation has been increasing steadily, moving to 22.8 percent from 5.5 percent in
December 2010” (Simpasa et al., 2011, p. 3).
2. “Tanzania in?ation reached 19.8 percent in December 2011, well above the 10 percent
average for the last few years. However, in 2010, in?ationary pressures started to build,
fuelled by soaring food and energy prices, while the government’s ?scal outlays added to the
in?ationary pressure. Since October 2010, in?ation has more than tripled, reaching
17.9 percent in October 2011. Although food in?ation has slowed recently, it is unlikely to
offset other in?ationary pressures” (Simpasa et al., 2011, p. 3).
3. The CFA franc is the name of two currencies used in Africa (by some former French colonies)
which are guaranteed by the French treasury. The two CFA franc currencies are the
West African CFA franc (used in the UEMOA zone) and Central African CFA franc (used
in the CEMAC zone). The two currencies though theoretically separate are effectively
interchangeable.
4. Economic and Monetary Community of Central African States.
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5. Economic and Monetary Community of West African States.
6. The need for in?ation to exhibit a unit root in order to accommodate the problem statement
draws from an “in?ation uncertainty” theory in recent African ?nance literature.
“The dominance of English common-law countries in prospects for ?nancial development in
the legal-origins debate has been debunked by recent ?ndings. Using exchange rate regimes
and economic/monetary integration oriented hypotheses, this paper proposes an ‘in?ation
uncertainty theory’ in providing theoretical justi?cation and empirical validity as to why
French civil-law countries have higher levels of ?nancial allocation ef?ciency. In?ation
uncertainty, typical of ?oating exchange rate regimes accounts for the allocation inef?ciency
of ?nancial intermediary institutions in English common-law countries. As a policy
implication, results support the bene?ts of ?xed exchange rate regimes in ?nancial
intermediary allocation ef?ciency” Asongu (2011a, p. 1). Also, before restricting the dataset,
we have found from preliminary analysis that, African CFA franc countries have a relatively
very stable in?ation rate.
7. “The French and English traditions in monetary theory and history have been different [. . .].
The French tradition has stressed the passive nature of monetary policy and the importance
of exchange stability with convertibility; stability has been achieved at the expense of
institutional development and monetary experience. The British countries by opting for
monetary independence have sacri?ced stability, but gained monetary experience and better
developed monetary institutions” (Mundell, 1972, pp. 42-3).
8. “It is widely acknowledged that money growth must be seen as more dangerous for price
stability when accompanied by strong credit. On the contrary, robust money growth not
associated with sustained credit expansion and strong dynamics in asset prices seems to be
less likely to have in?ationary consequences” (Anonymous Referee). This is consistent with
a recent strand of empirical literature (Bordo and Jeanne, 2002; Borio and Lowe, 2002, 2004;
Detken and Smets, 2004; Van den Noord, 2006; Rof?a and Zaghini, 2008; Bhaduri and Durai,
2012). These comment and fact have been incorporated into the analysis from an ef?ciency
standpoint. Financial intermediary allocation ef?ciency re?ects how money growth (through
bank deposits) is accompanied by credit facilities.
9. “The major ?ndings in the current simulation study are previewed as follows. First, these
criteria managed to pick up the correct lag length at least half of the time in small sample.
Second, this performance increases substantially as sample size grows. Third, with
relatively large sample (120 or more observations), HQC is found to outdo the rest in
correctly identifying the true lag length. In contrast, AIC and FPE should be a better choice
for smaller sample. Fourth, AIC and FPE are found to produce the least probability of under
estimation among all criteria under study. Finally, the problem of over estimation, however,
is negligible in all cases. The ?ndings in this simulation study, besides providing formal
groundwork supportive of the popular choice of AIC in previous empirical researches, may
as well serve as useful guiding principles for future economic researches in the
determination of autoregressive lag length” (Liew, 2004, p. 2).
10. Pedroni (1999) is applied in the presence of both “constant” and “constant and trend” while
Kao (1999) is based only on the former (constant).
11. For example, multivariate cointegration may involve variables that are stationary in levels
(Gries et al., 2009).
12. “The error correction term tells us the speed with which our model returns to equilibrium
following an exogenous shock. It should be negatively signed, indicating a move back
towards equilibrium, a positive sign indicates movement away from equilibrium.
The coef?cient should lie between 0 and 1, 0 suggesting no adjustment one time period
later, 1 indicates full adjustment. The error correction term can be either the difference
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between the dependent and explanatory variable (lagged once) or the error term (lagged
once), they are in effect the same thing” (Babazadeh and Farrokhnejad, 2012, p. 73).
13. For example, from a ?nancial depth perspective, the response to a money supply shock is
similar to that of a liquid liability shock. In the same vein, the response of a banking
ef?ciency shock is similar to that of a ?nancial ef?ciency shock. This same analogy applies
to ?nancial intermediary activity (from banking and ?nancial system perspectives).
14. In Appendix 4, a one standard deviation negative shock to money supply sharply decreases
in?ation within the ?rst year, then slightly decreases it again the next year before a slightly
steady in?ationary effect after the second year (see response of INFLATION to M2). The
de?ationary effect in the ?rst year of the shock is consistent with the liquid liabilities
perspective of ?nancial depth in Appendix 5. Here again, a one standard deviation negative
shock of liquidity liabilities has a de?ationary pressure on consumer prices within the ?rst
year (see response of INFLATION to FDGDP).
15. Financial size as de?ned by our paper is also in relative terms (bank assets on total assets).
Total assets here refer to bank assets plus central bank assets. Bank assets refer to credit
granted to economic operators.
16. Financial allocation ef?ciency in the context of this paper refers to the probability of deposits
being transformed into credit for economic operators. In other words, ?nancial
intermediation ef?ciency is the ability of ?nancial depth to allocate credit for ?nancial
activity. Thus, ?nancial ef?ciency is a relative measure (Beck et al., 1999).
17. Financial activity in the context of this paper refers to the ability of ?nancial institutions to
grant credit to economic operators.
18. The insigni?cance of ?nancial allocation ef?ciency and ?nancial activity as policy tools in
the battle against in?ation could be explained by the well documented surplus liquidity
issues experienced by the African banking sector (Saxegaard, 2006). Thus, allocation
inef?ciency (due to low transformation of mobilized funds into credit) and slow ?nancial
activity (limited granting of credit to economic operators) could partly elucidate this ?nding.
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Appendix 1
Appendix 2
Variables Sign Variable de?nitions Sources
In?ation In?. Consumer prices (annual %) World Bank
(WDI)
Economic ?nancial depth
(money supply)
M2 Monetary base plus demand, saving and
time deposits (% of GDP)
World Bank
(FDSD)
Financial system depth
(liquid liabilities)
Fdgdp Financial system deposits (% of GDP) World Bank
(FDSD)
Banking system allocation
ef?ciency
BcBd Bank credit on bank deposits World Bank
(FDSD)
Financial system allocation
ef?ciency
FcFd Financial system credit on Financial
system deposits
World Bank
(FDSD)
Banking system activity Pcrb Private credit by deposit banks (% of
GDP)
World Bank
(FDSD)
Financial system activity Pcrbof Private credit by deposit banks and other
?nancial institutions (% of GDP)
World Bank
(FDSD)
Financial size Dbacba Deposit bank assets on Central banks
assets plus deposit bank assets
World Bank
(FDSD)
Notes: In?. – In?ation; M2 – money supply; Fdgdp – liquid liabilities; BcBd – bank credit on bank
deposits; FcFd – ?nancial system credit on ?nancial system deposits; Pcrb – private domestic credit
by deposit banks; Pcrbof – private domestic credit by deposit banks and other ?nancial institutions,
Dbacba – deposit bank assets on Central bank assets plus deposit bank assets; WDI – world
development indicators; FDSD – ?nancial development and structure database
Table AII.
Variable de?nitions
Variables Mean SD Min. Max. Obser.
Financial
development
Financial depth Money supply 0.397 0.246 0.001 1.141 267
Liquid liabilities 0.312 0.206 0.001 0.948 270
Financial
ef?ciency
Banking system
ef?ciency
0.638 0.349 0.070 2.103 296
Financial system
ef?ciency
0.645 0.337 0.139 1.669 270
Financial
activity
Banking system
activity
0.203 0.190 0.001 0.825 265
Financial system
activity
0.214 0.200 0.001 0.796 270
Fin. size Financial system
size
0.661 0.272 0.017 1.609 293
Dependent
variable
Consumer price
index
20.524 32.416 2100.00 200.03 297
Notes: SD – standard deviation; min. – minimum; max. – maximum; obser. – observations;
?n. – ?nancial
Table AI.
Summary statistics
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Appendix 3
Appendix 4
Financial depth
Financial
ef?ciency
Financial
activity Fin. size In?ation
M2 Fdgdp BcBd FcFd Pcrb Pcrbof Dbacba In?.
1.000 0.987 0.172 0.199 0.776 0.758 0.503 20.357 M2
1.000 0.171 0.193 0.779 0.762 0.543 20.380 Fdgdp
1.00 0.955 0.674 0.684 0.408 20.205 BcBd
1.00 0.697 0.736 0.368 20.211 FcFd
1.00 0.985 0.541 20.335 Pcrb
1.000 0.552 20.339 Pcrbof
1.000 20.566 Dbacba
1.000 In?ation
Notes: M2 – money supply; Fdgdp – liquid liabilities; BcBd – bank credit on bank deposit (banking
intermediary system ef?ciency); FcFd – ?nancial credit on ?nancial deposits (?nancial intermediary
system ef?ciency); Pcrb – private domestic credit (banking intermediary activity); Pcrbof – private
credit from domestic banks and other ?nancial institutions (?nancial intermediary activity); Dbacba –
deposit bank assets on deposits banks plus central bank assets (?nancial size); In?. – in?ation
Table AIII.
Correlation analysis
Figure A1.
In?ation and money
supply (M2)
–5
0
5
10
15
20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Response to Cholesky One S.D.Innovations ± 2 S.E.
Response of INFLATION to M2
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Appendix 5
Appendix 6
Figure A2.
In?ation and liquid
liabilities (FDGDP)
–10
–5
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Response to Cholesky One S.D. Innovations ± 2S.E.
Response of INFLATION to FDGDP
Figure A3.
In?ation and banking
system ef?ciency (BCBD)
–10
–5
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of INFLATION to BCBD
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Appendix 7
Appendix 8
Figure A4.
In?ation and ?nancial
system ef?ciency (FCFD)
–10
–5
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of INFLATION to FCFD
Figure A5.
In?ation and banking
system activity
(PCRDBGDP)
–10
–5
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of INFLATION to PCRDBGDP
Fighting
in?ation
in Africa
59
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Appendix 9
Appendix 10
Corresponding author
Simplice A. Asongu can be contacted at: [email protected]
Figure A6.
In?ation and ?nancial
system activity
(PCRDBOFGDP)
–10
–5
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of INFLATION to PCRDB OF GDP
Figure A7.
In?ation and ?nancial
size (DBACBA)
–10
–5
0
5
10
15
20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of INFLATION to DBACBA
JFEP
5,1
60
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