How can economic and political liberalisation improve financial development in African

Description
The objective of this paper is to study the interactions between economic liberalisation,
political liberalisation, and financial development in African countries. More specifically, the paper
seeks to establish the impact of economic, political, and institutional openness on financial deepening

Journal of Financial Economic Policy
How can economic and political liberalisation improve financial development in African
countries?
Michael Enowbi-Batuo Mlambo Kupukile
Article information:
To cite this document:
Michael Enowbi-Batuo Mlambo Kupukile, (2010),"How can economic and political liberalisation improve
financial development in African countries?", J ournal of Financial Economic Policy, Vol. 2 Iss 1 pp. 35 - 59
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Dogga Satyanarayana Murthy, Suresh Kumar Patra, Amaresh Samantaraya, (2014),"Trade Openness,
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Financial Economic Policy, Vol. 6 Iss 4 pp. 362-375http://dx.doi.org/10.1108/J FEP-10-2013-0056
Boopen Seetanah, Shalini T. Ramessur, Sawkut Rojid, (2009),"Financial development and economic
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Dilip K. Das, (2010),"Financial globalization: a macroeconomic angle", J ournal of Financial Economic
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How can economic and political
liberalisation improve
?nancial development
in African countries?
Michael Enowbi-Batuo
Facolta` di Economia “Giorgio Fua` ”, Universita` Politecnica delle Marche,
Ancona, Italy, and
Mlambo Kupukile
African Development Bank, Tunis, Tunisia
Abstract
Purpose – The objective of this paper is to study the interactions between economic liberalisation,
political liberalisation, and ?nancial development in African countries. More speci?cally, the paper
seeks to establish the impact of economic, political, and institutional openness on ?nancial deepening.
Design/methodology/approach – In the empirical part, the paper proposes a two-step procedure
which involves the treatment effect and the new panel studies technique of recently updated data for
economic and political reform.
Findings – The results show to what extent political liberalisation, economic liberalisation, and the
stability of the political system have a statistically signi?cant effect on the ?nancial development of
the continent, showing that reform, stability, and democratic rule seem to be favourable for
development of the ?nancial sector in the continent.
Originality/value – There are few studies that directly explore the link between political and
economic liberalisation on ?nancial development. The difference between this paper and other studies
is that ?rst, it is restricted to African countries, those that have been mostly undergoing the two types
of reform (political and economics). The second reason is that most of the previous papers always took
into consideration the effects of ?nancial development on one of the reforms either political or
economic, meanwhile in this paper, the author considers the various aspects of reform: political,
economical, and the stability of the environment.
Keywords Economic conditions, Politics, Economic development, Africa
Paper type Research paper
1. Introduction
Economic theory and experience suggests that ?nancial development has a positive
impact on long-termeconomic growth (Barth et al., 2000; Levine, 2003; Bekaert et al., 2001;
Minier, 2003; Christopoulos and Tsionas, 2004; Demetriades and Andrianova, 2005). The
argument goes back to Schumpeter (1912/1934) who argued that the services provided
by ?nancial intermediaries were essential for innovation and development. Levine (1997)
list of ?ve functions of the ?nancial system by which it enhances economic growth:
(1) reducing risk;
(2) allocating resources;
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – G20, O16, O17, O55
Financial
development in
African countries
35
Journal of Financial Economic Policy
Vol. 2 No. 1, 2010
pp. 35-59
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381011055334
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(3) monitoring managers and exerting corporate control;
(4) mobilizing savings; and
(5) facilitating exchange of goods and services.
The better the ?nancial system performs these functions, the more it contributes to
overall economic growth. However, while all ?nancial systems provide these ?nancial
functions, there are large differences in how well they are provided.
In the 1970s, Shaw (1973) and Mckinnon (1973) emphasized the problem of ?nancial
repression in developing countries, arguing that in an economy in which the government
directly in?uences the credit policy of banks and sets the ceilings on interest rates, the
result is a fall in aggregate savings and investments and inef?cient distribution of
?nancial resources. They pointed out that ?nancial repression resulted in sub-optimal
macroeconomic performance and a choice for less favourable development prospects.
On the other hand, ?nancial liberalisation would raise the level of aggregate savings
and foster a more ef?cient distribution and use of ?nancial resources as preconditions
for creating a sustainable basis for the economic growth and development.
On the advice of international ?nancial and development agencies many
African countries undertook ?nancial liberalization as part of overall macroeconomic
reforms inthe 1980s and 1990s (Aryeetey, 1994; Collier, 1993; Ekpenyong, 1994; Oshikoya,
1992; Kasekende and Atingi Ego, 1999; Khan and Reinhart, 1990). Overall, while the
reforms succeeded in easing ?nancial repression, the impact on increasing growth, and
investment has been patchy while African ?nancial systems remain shallow and
relatively underdeveloped. Instead liberalization appeared to engender greater instability
and crises, particularly in the banking sector (Demirguc-Kunt and Detragiache, 1999).
In this paper, we return to these questions by examining the impact of economic and
?nancial liberalization on ?nancial development in Africa. In particular, we examine
whether democratization can induce a country to develop or liberalize the ?nancial
sector, and whether political stability improves a country’s ?nancial sector. In addition,
we examine whether economic liberalization is signi?cant for the development of the
?nancial sector. The questions are not trivial, considering the fact that liberalization
increases the opportunity for banks to take on greater or more risks. This has led
Demirguc-Kunt and Detragiache (1999) to conclude that in countries with weak legal
and institutional frameworks, such as lack of rule of law, high levels of corruption and
weak contract enforcement, ?nancial liberalization should proceed cautiously.
These issues are more relevant given the current global ?nancial crisis, which has
directly affected the key drivers of the continent recent growth performance due to the
fragility of their economies and vulnerability to external shock (Kasekende and
Atingi Ego, 1999). Despite the fact that most of African countries implemented
signi?cant economic reforms over the last two decades, which the crisis threatens to
unravel. Examining the role of economic and political liberalisation on ?nancial sector
development will help in shaping appropriate strategies for developing the ?nancial
sector African countries.
The paper adopts a two-step procedure, the ?rst model is a difference in difference
method to illustrate the various aspect of ?nancial liberalisation on economic and
political freedom, and then we use panel data techniques from period 1990 to 2005 for a
sample of 50 African countries to examine the effect of economic and political reform.
Our results show that political liberalisation, economic liberalisation, and the stability
JFEP
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of the political system have a statistically signi?cant effect on ?nancial development in
Africa. That is, we ?nd reforms, stability, and democratic rule to be favourable for
development of the ?nancial sector in the continent.
There are a few studies that directly explore the link between political and economic
liberalisation on ?nancial development (Yongfu, 2005; Olson, 1993; Clague et al., 1996).
In this paper, we restrict our focus to African countries only, concentrating on those
where both political and economic reforms have taken place. Second, compared with
previous papers, we consider various aspects of reform, including the political and
economic environment as well as stability.
The paper proceeds as follows. Section 2 is an outline of the literature review, while
in Section 3, we describe the methodology and data used. Section 4 presents the results,
and Section 5, summarizes the main conclusions.
2. Literature review
Many reasons have been put forward to explain why some countries have less
sophisticated ?nancial system than others. These explanations can be divided into
three interrelated strands of literature. According to the ?rst group, ?nancial
institutions do not succeed in an institutional vacuum, but need a legal and regulatory
environment in which contracts can be enforced and bankers are given strong
incentives to behave honestly (Kaufmann et al., 1999; Demirguc-Kunt and Levine, 1999;
Andrianova, 2004). The second school examines the link between ?nance and law,
showing that speci?c types of legal systems are more conducive to protecting investor
rights and adapting the law to take into account ?nancial innovation (La Porta et al.,
1997, 2000; Beck, 2001). For example, by comparing different broad legal traditions,
namely, civil law versus common law, La Porte et al. ?nd that the latter provides
stronger shareholder and creditor protection on which liquid capital markets depend,
and have a stronger enforcement tradition.
The third strand argues that ?nancial underdevelopment may be the outcome of
political circumstances, protecting the interests of narrow political-industrial elite
(Rajan and Zingales, 2003). Such an elite may have little interest in developing a well
functioning capital market, as they are well served by a relationship with the bankers.
In such an environment, there is an absence of arms length ?nance, thus restricting the
potential competitors’ access to ?nance. The more power is held by the elite groups,
the more autocratic the system, and the more obstacles to ?nancial development. This
means that political freedom, political rights, and civil liberties could be crucial for
?nancial development because they widen the suffrage in the political system, and
limit the in?uence of an elite group’s governing policy-making. Within this approach,
Pagano and Volpin (2001) regard regulation and its enforcement as a result of the
balance of power between social and economic constituencies. An important dynamic
implication of the political economy approach is that the scope of ?nancial
intermediation should increase as a broader section of the population achieves political
representation, leading to increase access to ?nance, and more competition (Perotti and
Volpin, 2007).
Further, as has been discussed by Clague et al. (1996), Olson (1993), and recently
Rajan and Zingales (2003) dominant interest groups, especially incumbent ?rms and
incumbent ?nancial intermediaries, have strong incentives to prevent new companies
from entering, potentially blocking the development of a more advanced ?nancial
Financial
development in
African countries
37
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market. Beck et al. (2003) applied the settler mortality hypothesis of Acemoglu et al.
(2001) to ?nancial development and suggested that while the institutions established
by extractive colonizers were likely to be detrimental to ?nancial development, those
created by the settler colonizers tended to favour ?nancial development. When the
colonisers left, the post colonial elite took over the same institution and continued
enjoying extractive surpluses. Once a political system had been set up, it brought
advantages to the interest groups bene?ting from the system within the political
process. Hence, even inef?cient systems were perpetuated, suggesting there is path
dependence (Bebchuk and Roe, 1999; Zanella et al., 2003).
However, a number of these studies have reported controversial outcomes. One
group of studies found that the democratic process enhances fundamental civil rights
liberties, stable politics, and an open society, it promotes property rights protection and
contract enforcement, discourages corruption and lawlessness, and fosters economic
growth (Olson, 1993; Clague et al., 1996; Minier, 1998; Persson, 2005). Other studies
have concluded that when the different interest groups are under pressure, the
democratic structures may suffer from inef?ciency in decision making and a dif?culty
in implementing viable policies for rapid growth. Less democratic nations tend to lower
their economic growth rate, even resulting in economic disorder, political instability,
and ethnic con?ict (Blanchard and Shleifer, 2000; Persson and Tabellini, 1992). Such
processes are likely to be typical in African countries. In this context, it is critical to
explore furthers on the relationship between political liberalisation or political stability
and economic liberalization on ?nancial development in African countries during a
period of a strong wave of democratic and economic reform changes taking place.
3. Methodology and data
In this section, we would describe the various indexes use in the empirical analysis as
the ?nancial development index constructed by using the principle component analysis,
the political liberalisation index, and political stability indicator obtained from the
polity IV dataset (Marshall and Taeggers, 2003) and the economic freedom index
collected from Fraser Institute (Gwartney et al., 2006). We illustrate the two different
econometric models employ in the study that is the different in different model and
panel dynamics model. Our sample consist of annual observation of about 50 African
countries selected on the basic of data availability during the period 1990-2005 (Table I).
Variable Observations Mean SD Min Max
GDP growth 718 0.034 0.078 20.968 0.724
Log GDP per capita 766 7.296 1.001 4.916 10.258
Real openness 650 0.251 0.154 0.040 1.068
Financial development index 735 4.681 1.560 21.272 7.947
Economic liberalisation 516 5.321 0.914 2.93 7.43
Political freedom 721 20.468 5.535 210 10
Capital account openness 751 20.607 1.063 21.766 2.602
Log domestic credit to private sector percent of GDP 744 2.523 0.942 20.381 5.193
Log bank loans percent of GDP 741 2.382 1.055 28.008 6.248
Log liquid liabilities (M3) percent of GDP 749 3.256 0.702 20.185 6.625
Log domestic credit provide by bank sector 647 3.142 1.055 21.685 7.135
Table I.
Summary statistics
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The ?nancial development index
Measuring ?nancial development is a very complex and complicated process because
there is no clear-cut de?nition as to what constitutes ?nancial development. Bandiera
et al. (2000) argued that an ideal index of ?nancial sector development should include
various aspect of regulatory and institutional reform. However, measuring this aspect
of government policy is a very dif?cult if not impossible task (Kelly and Mavrotas,
2003). The inclusion of all the policy variables separately in the same model also causes
serious estimation problems such as, multicolinearity, among others.
We construct the aggregate index of ?nancial development using the principal
component analysis from the standard ?nancial development indicators. In Africa
these are mainly from the banking system, and they include liquid liabilities as percent
of gross domestic product (GDP), private sector credit as percent of GDP and domestic
credit to banking sector as a percent of GDP. Each of the indicators above captures a
different aspect of ?nancial development and has its own strengths and weaknesses.
In the case of the banking sector development indicators, private sector credit is
probably the most relevant for measuring opportunities for new ?rms or determining
the ease with which any entrepreneur or company with a sound project can obtain
?nance. Liquid liabilities measures the ability of banks to mobilize funds or the size of
the banking system relative to the economy, but the funds are not always used to
?nance new entrepreneurs, so it may not be a suitable indicator of ?nancial
development in the Rajan and Zingales sense. Domestic credit comprises private credit
as well as credit to the public sector, thus it is probably the least well suited to
capturing the ?nancial development index.
The principal reason for building a composite index is to avoid the problem of
multi-collinearity[1] that occurs when introducing simultaneously several ?nancial
variables that are highly correlated among them. The principal component and factor
analysis which are methods for data reduction are ways that can be considered when
dealing with multi-collinearity, even though there is the econometric theory suggesting
many other procedures[2] to solve the problem. For this paper, we preferred using
the principal components method because it provides many advantages. Apart from
helping to reduce multi-collinearity, improving parsimony, and improving the
measurement of indirectly observed concepts, it makes economic sense by aiding
the re-conceptualization of the meaning of the predictor in our regression model
(Table II).
Using these three indicators of the banking system together, namely, liquid
liabilities as percent of GDP, private sector credit as percent of GDP and domestic
credit to banking sector as a percent of GDP allows us mainly to capture the size of
bank-based intermediation. The ?nancial development index is the ?rst principal
component of these three indicators and account for 77 percent of their variation. The
weights from this procedure are 0.60 for liquid liabilities, 0.57 for private credit, and
0.55 for domestic credit. The data of the various variable were collected from the World
Bank (2008) Development Indicator and the African Development Bank Statistics
Department.
The political liberalisation
To assess whether becoming more democratic deepens ?nancial development or
whether ?nancial development makes a country more democratic, two indicators were
Financial
development in
African countries
39
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r
c
e
n
t
l
e
v
e
l
s
;
r
o
b
u
s
t
s
t
a
n
d
a
r
d
e
r
r
o
r
s
a
r
e
i
n
p
a
r
e
n
t
h
e
s
e
s
Table II.
Pair wise correlation
coef?cient
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used. The ?rst one is the “combined polity score” – polity2 index, which varies from 10
(strongly democratic) to 210 (strongly autocratic), and is obtained the polity IV
database (Marshall et al., 2003)[3]. The polity variable was designed to record the
regime institutionalized authority characteristics. First, the dataset recorded a
democracy score (ranging from 0 to 10) for each country, based on the openness of the
political process, de?ned as the extent to which citizens can effectively express
preferences about policies and leaders through elections and the degree of restraints on
the power of the chief executive. Second, each country has an autocracy (again ranging
from 0 to 210) based on how political leaders are selected (by designation or chosen
from closed lists), the constraints on their power and the regulation and
competitiveness of political participation. In this paper, we consider political
liberalisation as change from a non-democratic to a democratic regime, which means
that only democratization is considered rather than an improvement in the regime.
A regime change is then taken to be a change from a non-positive to a positive polity2
value. Countries that have improved their democratic process are assigned a 1 starting
in the year they became a democracy and 0 otherwise; all other countries that have not
change their process are assigned a 0 (Figure 1).
The second indicator is that of political instability, whose application is based on
the premise that ?nancial development requires a certain level of social development,
trust, and reputation. The political system that is unstable results in a loss of social and
human capital, uncertainty and the breakdown of long-term economic relationships.
Fear of con?scation due to the frequent changes leads people to hold physical assets
instead of ?nancial assets. Following the annual historical events in each country, we
were able to determine if a country had political stability (assigned a 1 and a 0 for
instability).
The economic liberalisation index
To measure the quality of economic liberalisation, this paper employs the aggregate
index of economic freedom of the Fraser Institute (Gwartney et al., 2006). This
Figure 1.
Financial development
and economic
liberalisation index
AGO
BDI
BEN
BFA
BWA
CAF
CIV
CMR
COG
DZA
EGY
ETH
GAB
GHA
KEN
LSO
MAR
MDG
MLI
MOZ
MRT
MUS
MWI
NAM
NER
NGA
RWA
SEN
SLE
TCD
TUN
TZA
UGA
ZAF
ZAR
ZMB
F
i
n
a
n
c
i
a
l

d
e
v
e
l
o
p
m
e
n
t
Economic liberalisation
3
0
2
4
6
8
4 5 6 7
(Mean) fd1 Fitted values
Financial
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composite indicator, which draws on survey data from the Global Competitiveness
Report and the International Country Risk Guide, measures the extent to which
institutions in a country provides secure protection of property rights, assures fair
enforcement of contracts and a stable monetary environment, allows free exchange
with foreigners, and lifts restrictions on entry into occupation and business activities.
It was computed for 123 countries in the base years of 1980-2005, and, by construction
ranging from 0 to 10 implies the highest economic freedom index. In a recent paper,
Berger et al. (2006) compared the different measures of economic liberalisation which
appeared in the literature. They argued that the economic freedom index periodically
compiled by the Fraser Institute has been extensively applied in empirical papers and
has been proved to be the best at capturing the essence of market-oriented institutions
(Figure 2).
The other index that we took into consideration was the index of capital account
openness that was develops by Chinn and Ito (2007). They used the data reported in the
Annual Report on Exchange Arrangements and Exchange Restrictions published by
the IMF on the existence of multiple exchange rates, restrictions on current and capital
accounts (where the latter is measured as the proportion of the last ?ve years without
control) and requirement to surrender export proceeds in order to capture the intensity
of controls on capital account transactions. Their index of openness is the ?rst
standardized principal component of these variables, and in practice it ranges from
22.0 in the case of the most control to 2.5 in the case of the most liberalisation. This
data is available for 108 countries for 1970-2000. The control variables are real
openness, which is export plus import divided by the GDP purchasing power parity,
the GDP per capita, and the growth rate of GDP, all of which are taken from the World
Bank (2008) Development Indicator (Figure 3).
Empirical speci?cation
In this paper, we used two approaches to estimate the causal effect of economic and
political reform on ?nancial development. The micro econometric approaches known
as the difference in difference estimation will be ?rst approach while the second
approach is to estimate the panel regression.
Figure 2.
Financial development
and political liberalisation
index
AGO
BDI
BEN BFA
BWA
CAF
CIV
CMR
COG
COM
DZA
EGY
ERI
ETH
GAB
GHA
GIN
GMB
GNB
GNQ
KEN
LBR
LSO
MAR
MDG
MLI
MOZ
MRT
MUS
MWI
NAM
NER
NGA
RWA
SDN
SEN
SLE
SWZ
SYC
TCD
TGO
TUN
TZA
UGA
ZAF
ZAR
0
2
4
6
8
F
i
n
a
n
i
c
a
l

d
e
v
e
l
o
p
m
e
n
t
–10 –5 0 5 10
Political liberalisation
(Mean) fd1 Fitted values
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Basic treatment effect model. We followed the method used by Persson (2005), then
after Giavazzi and Tabellini (2005) in which they divided the sample of country into
two groups. Those countries that have experience of some reforms during the period of
observation were called “treated” while those that had not implemented reforms during
the same period of observation were known as “controls”. For this paper, we looked at
the pre- and post-treatment effect on the liberalizing countries relative to the entire
group. To identify the effect of economic or political liberalization as the estimated
difference to difference of the ?nancial development between the two groups of
countries, we implemented the following equation:
y
it
¼ a
i
þh
t
þb reform
it
þ1
it
ð1Þ
where y
it
is the outcome of ?nancial development of country i at time t; a
i
and h
t
are
country and year ?xed effect, respectively, reform are economic and political freedom
variables which are given the value of 1 in the year after the reform in the treated
countries and 0 otherwise and 1
it
is an unobserved error term. The coef?cient of b
measures the effect of the reform on the variable of interest y, which is the index
?nancial development. This method allowed us to take advantage of both the time
series and the cross sectional variation in the data. It also useful, when studying the
effect of economic or political liberalization to differentiate the results of the treated
countries from others and also consider the pre- and post-reform consequences,
exploiting both the within country variation as well as the comparison between
countries.
Dynamic analysis. Assessing the relationship between ?nancial development and
political or economic liberalization in our panel data set poses some econometric issues
that can be described in the context of a simple dynamic equation. Consider the
following equation:
y
it
¼ ay
it21
þb
0
x
it
þg
0
Z
it
þh
i
þm
t
þ1
it
ð2Þ
where y
it
is the dependent variable ?nancial development index, y
it21
is the lagged
variable of the ?nancial development index which has to capture the adjustment
Figure 3.
Financial development
and index of capital
account openness
AGO
BDI
BEN
BFA
CAF
CIV
CMR
COG
COM
CPV
DZA
EGY
ERI
ETH
GAB
GHA GMB
GNB
GNQ
KEN
LSO
MAR
MDG
MLI
MOZ
MRT
MUS
MWI
NAM
NER
NGA
RWA
SDN
SEN
SLE
SWZ
SYC
TCD
TGO
TUN
TZA
UGA
ZAF
ZMB
3
4
5
6
7
8
–1 0 1 2
(Mean) kaopen
(Mean) fin_index Fitted values
Financial
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African countries
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process of the dependent variable to the desired level, while x
it
represents the
explanatory variable which is the political or economic liberalization variable, and Z
it
is
a vector of controlling variables which comprise real openness, the logarithm of real
GDP per capita, and the growth rate of GDP. The terms h
i
and m
i
, respectively, denote
the unobserved common factor affecting all countries, and a country effect capturing
unobserved country characteristics.
Using the panel data methods for the estimation allows us to control the omitted
variables bias and endogeneity, which are better than in the case of the cross-section
approach (Caselli et al., 1996). To solve the potential problem of endogeneity of the
regressors (Griliches and Mairesse, 1991), suitable instruments are needed. We relied
primarily on internal instruments, along the lines described by Arellano and Bond
(1991). Also, when the ordinary least squares (OLS) model is applied, the estimator of a
is inconsistent and likely to be biased up ward since the lagged value of y
it21
is
positively corrected with the omitted ?xed effect even if the idiosyncratic component of
the error term is serially uncorrelated.
The problem of the country – speci?c effects, cannot be solved by taking the ?rst
difference of the equation since the ?rst difference transformation introduces
correlation between the lagged dependent variable and the differenced errors:
y
it
2y
it21
¼ ða þ 1Þð y
it
2y
it22
Þ þb
0
ðx
it
2x
it21
Þ þg
0
ðZ
it
2Z
it21
Þð1
it
21
it
Þ ð3Þ
The use of instruments is required to deal with the likely endogeneity of the
explanatory variables, and the problem of constructing the new error term, 1
it
21
i;t21
,
which is correlated with the lagged dependent variable. Assuming that the time
varying disturbance 1 is not serially correlated, and the explanatory variable x is
weakly exogenous (they are uncorrelated with future realization of the time varying
error term), lagged values of the endogenous and exogenous variables provide valid
instruments. In other words, we assume that:
1by
i;t2S
· ð1
i;t
21
i;t21
Þc ¼ 0 for s $ 2; t ¼ 3; . . . ; T ð4Þ
1½x
I ;T2S
· ð1
I ;T
21
I ;T¼1
Þ? ¼ 0 for s $ 2; t ¼ 3; . . . ; T ð5Þ
We refer to the generalized method of moment (GMM) estimator based on these
conditions as the difference estimator. There is however, conceptual and statistical
shortcoming with this difference estimator. When the explanatory variables are
persistent over time, their lagged levels are weak instruments for the regression
equation in differences (Alonso-Borrego and Arellano, 1999; Blundell and Bond, 1998).
This raises the asymptotic variance of the estimator and creates a small sample bias.
To avoid these problems, below we use the estimation that combines the regression in
difference and in levels (Arellano and Bover, 1995; Blundell and Bond, 1998).
The instrument for the regression in differences is the same as the above. The
instruments for the regression in levels are the lagged differences of the corresponding
variables. These are appropriate instruments under the following additional
assumption. Although there may be a correlation between the level of the right
hand side variable and the country-speci?c effect in equation (3), there is no correlation
between the difference of these variables and the country speci?c effect. This
assumption results from the following stationary property:
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1by
i;tþp
· h
i
c ¼ 1by
i;tþq
· h
i
c
1½X
I ;TþP
· h
I
? ¼ 1bX
I ;Tþq
· h
i
c for all p and q ð6Þ
The additional moment conditions are:
1½ð y
i;t2s
2y
i;t2s21
Þ · ðh
i
þ1
i;t
Þ? ¼ 0 for s ¼ 1 ð7Þ
1½ðX
I ;T2S
2X
I ;T2S21
Þ · ðh
I
þ1
I ;T
Þ? ¼ 0 for s ¼ 1 ð8Þ
Based on the conditions in equations (4)-(8), we employ the GMM procedure to generate
consistent and ef?cient estimates of the parameters of interest and their asymptotic
variance-covariance (Arellano and Bond, 1991; Arellano and Bover, 1995).
These are given by the following formulas:
^
u ¼ ð

X
0
W
^
V
0

XÞ
21

X
0
W
^
V
21
W
0
y ð9Þ
AVARð
^
uÞ ¼ ð

X
0
W
^
V
21
W
0

XÞ
21
ð10Þ
where u is the vector of parameters of interest (a, b), y is the dependent variable
stacked ?rst in differences and then in levels, X is the explanatory-variable matrix
including the lagged dependent variable ( y
it
, x) stacked ?rst in differences and then
in levels, Z is the matrix of instruments derived from the moment conditions, and
^
V is a consistent estimate of the variance covariance matrix of the moment conditions.
The consistency of the GMM estimators depends on whether lagged values of the
explanatory variables are valid instruments in the growth regression. We addressed
this issue by considering two speci?cation tests suggested by Arellano and Bond
(1991) and Arellano and Bover (1995). The ?rst is a Sargan or Hensen test of
over-identifying restrictions, which tests the overall validity of the instruments by
analyzing the sample analogy of the moment conditions used in the estimation process.
Failure to reject the null hypothesis gives support to the model. The second test
examines the null hypothesis that the error term 1
it
is not serially correlated. As in the
case of the Sargan test, the model speci?cation is supported when the null hypothesis is
not rejected. In the system speci?cation, we test whether the differenced error term
(that is, the residual of the regression in differences) is second-order serially correlated.
First-order serial correlation of the differenced error term is expected even if the
original error term (in levels) is uncorrelated, unless the latter follows a random walk.
The second-order serial correlation of the differenced residual indicates that the
original error term is serially correlated and follows a moving average process at least
of order one. This would reject the appropriateness of the proposed instruments (and
would call for higher-order lags to be used as instruments).
The aim of using different methods of panel estimation (OLS, less square dummy
variables (LSDV), and system general method of moment (SYS-GMM)), is because the
dynamic panel data approach suffers from serial autocorrelation and a business cycle
effect which are inevitably introduced when more than one observation for each
economy is added (Mankiw, 1995). It is, therefore, essential to discuss different
methods of panel data model that we used before looking at the results. The OLS
estimation of the panel data does not consider the unobserved time and country effects.
As a result, the OLS estimation suffers from a positive correlation between the lagged
Financial
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dependent variable and the error term which affects the OLS estimation to be biased
upwards and which can be inconsistent (Roodman, 2007). The LSDV estimation tends
to be biased downward due to the fact the lagged dependent variable is negatively
corrected with the error term. Generally, the OLS and LSDV estimators can provide a
bound for the turn value of the coef?cient of the lagged dependent variable. Good
estimates of the true parameter should therefore lie in the range between these values
or at least close to it.
It is a well-known concern in the literature that some of the regressors may be
potentially endogenous or predetermined in determining ?nancial development.
For example, ?nancial development of a country might improve due to a more liberalized
economic system or political system but at the same time political and economical
liberalization may be enhanced by a developed ?nancial sector. If we were to run the
OLS regression, the estimate would be biased as the error term is correlated with Xs.
To address the potential endogeneity of regressors and to incorporate ?xed effects,
we employ the system-GMM estimator from Blundell and Bond (1998). The
Blundell-Bond estimator is arguably a superior approach to the Arellano-Bond
difference-GMM as adding lagged differenced variables as instruments in the level
equations may generate substantial ef?ciency gains when the time window is
relatively short. Another advantage of the system-GMM estimation is its ability to
identify the coef?cients of time-invariant variables in the level equation.
4. Results
Interaction between economic and political liberalisation on ?nancial development
We present the results of the treatment effect estimation between economic
liberalisation, political liberalisation, and ?nancial development. Table III reports the
outcome in which the dependent variable is the index of ?nancial development while
the explanatory variable for economic liberalisation is the aggregate index of economic
freedom of the Fraser Institute (Gwartney et al., 2006), giving the value of 1 for those
countries that were considered treated and otherwise 0. The ?rst two columns show all
the countries in the sample including the treated countries while the last two columns
represent only the treated countries, that is, those countries that experienced some
years of reform during the period under observations. Table III shows a positive
relationship between ?nancial development and economic liberalisation. The effect is
more consistent with treated groups than the entire sample of countries. In columns 2
Dependent variable: ?nancial development index
1 2 3 4
Economic liberalisation 0.94 (0.22)
* * *
0.79 (0.27)
* * *
3year_pre_lib. 0.024 (0.037) 0.57 (0.039)
3year_post_lib. 0.038 (0.027) 0.57 (0.029)
*
4year_post_lib. 0.065 (0.027)
* * *
0.59 (0.026)
* *
Observations 445 322 337 322
R
2
0.32 0.05 0.24 0.05
Sample ALL ALL Treated Treated
Notes: Signi?cant at
*
10,
* *
5, and
* * *
1 percent levels; robust standard errors are in parentheses
Table III.
Financial development
and economic
liberalisation
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and 4, we examine whether the timing of the reforms matters. This is accomplished by
considering the liberalisation process in the three years preceding the reform
(3year_pre_lib), three year following the reform (3year_post_lib), and from four year
and onward from the reform. Economic liberalisation seems to produce a positive effect
on ?nancial development from the four-year period onward after the liberalisation for
the all the countries in the sample, while for the treated group, from the three-year
period onward after reform they have a positive and signi?cant effect on ?nancial
development.
Table IV documents the effect of political liberalisation on ?nancial development.
We ?nd that political freedom appears to be positive and signi?cant on ?nancial
development when considering all the countries in the sample but it is not the case for
all democratic countries, in which the coef?cient is positive but not signi?cant. The
results listed in columns 2 and 4 show that the timing effect is negative and signi?cant
after the four-year period of political reform while for the treated group it is positive
and signi?cant after the four-year period of political reform. The timing effect can be
interpreted as re?ecting transition from the early to a mature stage democratisation.
Table V shows the relationship between ?nancial liberalisation and ?nancial.
Columns 1 and 2 show that ?nancial openness is consistently positive and signi?cant,
suggesting that ?nancial reform do have a long-run effect on the ?nancial system. For
the timing effect, the outcome for the treated countries is positive from the three-year
Dependent variable: ?nancial development index
1 2 3 4
Political liberalisation 0.026 (0.011)
* *
0.015 (0.011)
3year_pre_lib. 0.045 (0.034) 20.03 (0.023)
3year_post_lib. 0.031 (0.023) 0.0004 (0.017)
4year_post_lib. 20.106 (0.056)
*
0.45 (0.019)
* *
Observations 603 569 307 377
R
2
0.02 0.06 0.08 0.10
Sample ALL ALL Treated Treated
Notes: Signi?cant at
*
10,
* *
5, and
* * *
1 percent levels; robust standard errors, clustered at the
country level, in parentheses; all the regressions include yearly ?xed effect
Table IV.
Financial development
and political freedom
Dependent variable: ?nancial development index
1 2 3 4
Financial liberalisation 0.23 (0.047)
* * *
0.29 (0.102)
* * *
3year_pre_lib. 1.24 (1.01) 21.53 (0.46)
* *
3year_post_lib. 21.75 (1.01)
*
0.24 (0.087)
* *
4year_post_lib. 0.17 (0.203) 0.56 (0.22)
*
Observations 634 376 309 322
R
2
0.04 0.12 0.08 0.05
Sample ALL ALL Treated Treated
Notes: Signi?cant at
*
10,
* *
5, and
* * *
1 percent levels; robust standard errors are in parentheses
Table V.
Financial development
and capital account
openness
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period onward while for all the countries in the sample the are some negative effects
after three years of ?nancial openness.
When political freedom is measured by the polity2 variable (10 for strongly
democratic and 210 for autocratic), column 1 in Table VI shows that political
liberalisation has a negative and an insigni?cant effect on ?nancial development, while
democratization undertaken ?ve years earlier improves ?nancial development. As can
be seen in column 2, we also ?nd the same effect when the political freedom is
measured by a dummy variable (10 ¼ 1 for strongly democratic and 210 ¼ 0 for
autocratic). On the other hand, the effect of economic liberalisation on ?nancial
development is positive and signi?cant effect. The effect of earlier ?ve years of
economic reform is also positive though not signi?cant.
Dynamic panel results
In Tables VII and VIII, we present the results for the full sample of African countries
obtained by OLS, LSDV, and SYS-GMM estimation methods. The ?rst three columns
show the baseline speci?cation (OLS) in which both political liberalisation variables
(dummy polity2 and polity2) are positive but not signi?cant. The political stability
dummy (following the political and social events of each country reported by polity IV
dataset), however, has a strong positive and signi?cant effect on ?nancial
development.
In columns 4-6, we present the estimates obtained using the LSDV approach, which
relies on the variability of data within-country. In this context, the in?uence of various
independent variables has to be understood to be taking place over time within a
country, rather than across countries. The use of an LSDV estimator allows us to wipe
out all time-invariant country-speci?c characteristics that are likely to affect the
?nancial development patterns. Moreover, the use of the LSDV estimator overcomes
the possible problems in data comparability across countries. The result shows that the
effect of political freedom on ?nancial development is positive but not signi?cant while
political stability is positive and signi?cant at a 5 percent level.
We have to remember that in AR(1) models, the OLS level estimate of the
autoregressive parameter is biased upward in the presence of a ?xed effect, and the
LSDV estimate is biased downward in a short panel. A consistent estimate of
1 2 3
Real openness 0.665 (1.396) 0.732 (1.434) 21.475 (1.791)
GDP growth 23.981
*
(2.257) 23.855
*
(2.248) 20.965 (1.785)
Log GDP per capita 0.630
*
(0.320) 0.637
* *
(0.312) 1.004
* *
(0.371)
Political freedom 20.290 (0.373) 20.035 (0.036)
Political freedom_5 1.045
*
(0.530) 1.118
* *
(0.512)
Economic liberalisation 0.587 (0.242)
* *
Economic liberalisation_5 0.319 (0.464)
Observations 554 553 395
Number of countries 40 40 30
Adjusted R
2
0.266 0.274 0.414
Notes: Signi?cant at
*
10,
* *
5, and
* * *
1 percent levels; robust standard errors, clustered at the
country level, in parentheses; all the regressions include yearly ?xed effect
Table VI.
Financial development,
political freedom, and
economic liberalisation
JFEP
2,1
48
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2
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Table VII.
Effect of economic
liberalisation on ?nancial
development in African
countries
Financial
development in
African countries
49
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A
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2
4

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5
4
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s
:
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1
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1
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n
p
a
r
e
n
t
h
e
s
e
s
Table VIII.
Effect of political freedom
and stability on ?nancial
development in African
countries
JFEP
2,1
50
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Y

U
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R
S
I
T
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2
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(
P
T
)
the autoregressive parameter can be expected to lie in between the OLS levels and
LSDV estimates. It is a simple indication of the presence of serious ?nite sample biases
when particular estimates fail to fall within this interval or are very close to the bound.
In columns 7-9, the SYS-GMM estimate provides strong evidence that the
improvement of political freedom is associated with ?nancial developments, all the
variables of political liberalisation and political stability are positive and signi?cant
and the diagnostic tests, including ?rst and second order serial correlation tests, the
Sargan test and the different Sargan tests are supportive. In general, the coef?cient of
log GDP per capita has a positive sign, real trade openness has a positive sign almost
in all the estimations except with the SYS-GMM model where it has a negative sign in
all the estimates (see columns 7-9, Table VIII).
Table VIII looks at the effect of economic liberalisation on ?nancial development in
African countries. It appears that the effect of economic reform on ?nancial
development was strongly signi?cant during this period (1990-2005), contributing to
the improvement of ?nancial development. It is positive in the entire methods (OLS,
LSDV, and SYS-GMM) but evidence is clearer in the SYS-GMM model (columns 5
and 6), where we ?nd that the two variable representing economic liberalisation (the
index of economic freedom of the Fraser Institute and the index of capital account
openness) having both positive and highly signi?cant effects on the speed of ?nancial
development. In general, we ?nd that log of GDP per capita and real trade openness
have a positive sign and in some cases is signi?cant while the growth rate of GDP is
always negative and signi?cant.
The lagged level of the ?nancial development index as an explanatory variable is
included in all the regressions. The coef?cient is a highly signi?cant explanatory
variable in all of the outlier robust regression. The positive coef?cient indicates that the
lagged level is picking up the unobserved country effect, which raises both present and
past ?nancial development. While the signs and coef?cients of economic liberalisation
variables are mostly relatively robust, the signi?cance level tends to decline. An
explanation for the decline in signi?cance levels is the correlation between the level of
?nancial development and economic liberalisation. Multicolinearity would tend to
increase the standard errors of the coef?cient and hence decrease the reported
signi?cance levels. In sum, the main ?nding in this paper is in accordance with the
literature (Olson, 1993; Clague et al., 1996; Huang and Temple, 2005; Giavazzi and
Tabellini, 2005; Carmignani, 2008), showing that improving the democratic process
and economic liberalisation reform leads to a greater ?nancial development sector.
5. Conclusions
The purpose of this paper has been to explain the effect of political and economic
liberalisation on ?nancial development in Africa using a panel of 50 African countries,
over the period spanning 1990-2005. The effect of economic and political liberalisation
on ?nancial development is ?rst examined using the difference-in-difference approach.
Finding that both ?nancial and economic liberalisation improves ?nancial
development while the association between political freedom and ?nancial
development is not consistent. Even when considering the effect of timing, it shows
that after three years onward of economic liberalisation, the outcome is positive for the
performance of the ?nancial sector.
Financial
development in
African countries
51
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U
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2
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6

(
P
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)
Furthermore, using the panel data techniques, including LSDV and SYS-GMM
estimators, the paper points out a number of issues. First, there is a positive
relationship between political freedom and ?nancial development in Africa, but the
evidence seems quantitatively stronger for political stability than political freedom.
Second, the relationship between economic liberalisation and ?nancial development is
signi?cantly positive, and the effect is expected to persist over a long period. What
political and economic liberalisation has appeared to deliver in the continent is greater
access to the international capital market, dynamic change within the ?nancial system
in most countries. It is, however, yet to transform the institutional setting for resource
mobilisation suf?cient to produce dynamic indigenous growth.
In summing up, economic reformis a necessary condition for democratic development
because it is an instrument capable of delivering the desired transformation for an
economy. It opens up the market and unleashes popular participation in society, and can
easily facilitate the convergence of free society and healthy ?nancial system.
The study therefore recommends two suggestions to enhance ?nancial deepening
through the liberalisation process as a means of resource mobilization for the private
sector. First, taking advantage of ?nancial openness to diversify the ?nancial
instruments being offered in the ?nancial market and channelling it to the private
sector in these economies in order to increase competitiveness will enhance innovation,
hence increase ef?ciency. Second, the reform policies, mostly implemented under
structure adjustment programs could work in a similar way as structural reforms
which can encourage the private sector so as to boost corporate governance, improve
investment climate, and reduce corruption.
Notes
1. Multi-collinearity refers to a situation in which two or more explanatory variables in a
multiple regression model are highly correlated.
2. The procedure mentioned to solve multi-collinearity were the instrumental variables. The
Centring method, Omitting the variable with the least statistical signi?cance, etc.
3. See polity IV project data set user’s manual.
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Corresponding author
Michael Enowbi-Batuo can be contacted at: [email protected]
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Appendix
Figure A1.
Financial development
index in various African
countries, 1990-2005
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AGO BDI BEN BFA BWA CAF CIV
CMR COG COM CPV DZA EGY ERI
ETH GAB GHA GIN GMB GNB GNQ
KEN LBR LSO MAR MDG MLI MOZ
MRT MUS MWI NAM NER NGA RWA
SDN SEN SLE SWZ SYC TCD TGO
TUN TZA UGA ZAF ZAR ZMB
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Figure A2.
The average domestic
credits to private sector
(percent of GDP) and
liquid liabilities (percent of
GDP) 1990-2005
0 50 100 150 200
Liquid liabilities (M3) % GDP
ZMB
ZAR
ZAF
UGA
TZA
TUN
TGO
TCD
SYC
SWZ
SLE
SEN
SDN
RWA
NGA
NER
NAM
MWI
MUS
MRT
MOZ
MLI
MDG
MAR
LSO
LBR
KEN
GNQ
GNB
GMB
GIN
GHA
GAB
ETH
ERI
EGY
DZA
CPV
COM
COG
CMR
CIV
CAF
BWA
BFA
BEN
BDI
AGO
0 50 100 150
Domestic credits to pirvate sector (% GDP)
ZMB
ZAR
ZAF
UGA
TZA
TUN
TGO
TCD
SYC
SWZ
SLE
SEN
SDN
RWA
NGA
NER
NAM
MWI
MUS
MRT
MOZ
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MAR
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GIN
GHA
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Economic
liberalisation Political freedom Financial openness
Countries Control Treated Control Treated Control Treated
Angola Yes No Yes No Yes No
Benin No Yes No Yes Yes No
Botswana No Yes No Yes Yes No
Burkina Faso No Yes No Yes Yes No
Burundi No Yes No Yes Yes No
Cameroon No Yes Yes No Yes No
Cape Verde No Yes Yes No Yes No
Central Africa No Yes Yes No Yes No
Chad Yes Yes No Yes No
Comoros – – No Yes Yes No
Congo Democratic Yes Yes No Yes No
Congo Republic Yes Yes No Yes No
Cote d’lvoire No Yes Yes No No Yes
Equatorial Guinea – – Yes No Yes No
Eritrea – – Yes No Yes No
Ethiopia No Yes No Yes Yes No
Gabon No Yes Yes No Yes No
Gambia – – No Yes Yes No
Ghana No Yes No Yes No Yes
Guinea – – Yes No Yes No
Guinea Bissau – – No Yes Yes No
Kenya No Yes No Yes No Yes
Lesotho No Yes No Yes No Yes
Liberia – – No Yes Yes No
Madagascar No Yes Yes No Yes No
Malawi Yes No Yes Yes No
Mali No Yes No Yes Yes No
Mauritania No Yes Yes No Yes No
Mauritius No Yes No Yes Yes No
Mozambique No Yes No Yes No Yes
Namibia No Yes No Yes Yes No
Niger No Yes No Yes Yes No
Nigeria No Yes No Yes Yes No
Rwanda No Yes Yes No Yes No
Senegal No Yes No Yes Yes No
Seychelles – – Yes No Yes No
Sierra Leone No Yes No Yes Yes No
South Africa No Yes No Yes Yes No
Sudan – – Yes No Yes No
Swaziland – – Yes No No Yes
Tanzania No Yes No Yes Yes No
Togo – – Yes No Yes No
Uganda No Yes Yes No Yes No
Zambia No Yes Yes No Yes No
Algeria Yes Yes No Yes No
Egypt No Yes Yes No No Yes
Morocco No Yes Yes No Yes No
Tunisia No Yes Yes No No Yes Table AI.
Financial
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