DETERMINANTS OF COMMERCIAL BANKS' PROFITABILITY IN ...

DETERMINANTS OF COMMERCIAL BANKS’ PROFITABILITY IN
SUB-SAHARAN AFRICA
By: Munyambonera Ezra Francis
Abstract
This research investigated some of the key determinants of commercial banks’ profitability
in Sub-Saharan Africa. The study used data from balance sheet as well as standardized
financial accounts derived from the Bank-Scope International bank database. An
unbalanced panel data set for a sample of 224 commercial banks from 42 countries, for the
period 1999 to 2006 was utilized. A cost efficiency model was employed to generate
specify the bank profitability function. The random effects estimator was utilized to estimate
the model. Results confirm the importance of bank level factors such as; assets, capital
adequacy, operational efficiency, and liquidity; and macroeconomic factors such as growth
in GDP and inflation in explaining bank profitability in SSA. Further, the results provide
evidence that that the banking sector over the study period had persistence in profitability
behavior towards equilibrium. By studying the determinants of banks’ performance in Sub-
Saharan Africa, the study provided additional knowledge about SSA commercial banking
sector that is important for policy making.
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DETERMINANTS OF BANK S’ PROFITABILITY IN SUB-SAHARAN AFRICA
2.0 Introduction
During the last two decades, the banking sector in Africa and in the rest of the developing
world has experienced major transformation in its operating environment. In a number of
countries, financial sector reforms have been implemented. In these reforms, the role of
commercial banks has remained central in financing economic activities in the various
segments of the markets especially in Sub-Saharan Africa. Panayiotis et al.(2005), Naceur
and Goaied (2001; 2003) among others, showed that both external as well as domestic
factors have contributed to growth in performance of SSA banks in the last two decades.
These studies further suggested that, given the importance of commercial banks in Africa,
better understanding of the determinants of performance was important.
The few studies on Africa and other developing world have shown that there are many
factors that influence bank profitability. Commercial banks’ profitability for most of the
SSA countries has been about 2 percent over the last 10 years and compares significantly
with other developing world, but lower than the developed world. A major research
question is why commercial banks in SSA have remained less competitive and less
profitable, despite financial sector reforms of 1980s and 1990s, aimed at improving their
efficiency. The motivation behind this study was that there is little information about
commercial banks’ performance in SSA that would be important for policy guidance of the
sector.
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2.1 Problem Statement
Despite financial sector reforms in Africa since the 1990s with an aim of improving
profitability, efficiency and productivity, commercial banks’ performance has remained
poor with substantial gaps in service delivery to private agents. There is sufficient empirical
evidence that poor performance is manifest into low performance of bank indicators
including: high levels of credit risk to private agents, poor quality loans, limited and or
inadequate capitalization, operational inefficiencies, higher incidences of non-performing
loans, higher levels of liquidity risk; among others. Although these are mentioned as
constraint areas affecting SSA banks performance, they are based on a few studies and non-
elaborate methods to generate sufficient conclusions. This study there became an extension
of the few studies undertaken on SSA with a view of generating sufficient information on
commercial banks. The study adopts the fundamental indicators that influence banks’
performance in general and have been utilized in most studies available. These observations
are collaborated in the literature and empirical studies contained in this paper.
Nissanke and Aryeetey (1998) and Aryeetey et al. (1997) demonstrated that continuous
poor performance of banking systems in Africa could be partly explained by the high
degree of financial market fragmentation and limited access to basic payment services or
savings accounts. On the other hand, Nissanke and Aryeetey (2006); Dermerguc-Kunt and
Huizingha (2001); and Bikker and Hu (2002) revealed that African banks have not been
widely studied and was therefore difficult to inform policy on readily efficient banks in the
continent without sufficient data. A similar view was reached by Roland (1997),
Eichengreen and Gibson, (2001), Goddard et al. (2004), Gibson (2005), Bonaccorsi di Patti
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and Hardy (2005), Berger et al. (2005), and Nakane and Weintraub (2005), that more data
was required on African banking systems to inform policy. All these studies, among others
observed that more understanding on African banking sector performance was important.
World Bank (2005) also emphasized the need to undertake deeper analysis of financial
sector performance in SSA, where performance has not been impressive.
2.2 Objectives of the study
The main objective of the study was to investigate the determinants of commercial banks
‘profitability in SSA for the period 1999 to 2006. The research should help to draw some
implications for policy that improves performance of the sector in the sub-region. The
study utilized both bank level as well as macroeconomic factors to measure profitability.
2.3 Justification for the Study
Empirical evidence clearly shows that studies focusing on Sub-Saharan Africa’s financial
sector are still scanty and limited. Even those which have been carried out point to a need
for further investigation of the factors which that have continued to cause poor financial
performance in the sub-region, notwithstanding the reforms. Most of the evidence in
regard to commercial banks’ performance largely focus on the developed economies
environments and the conclusions of may not be useful for Africa financial sector
planning. According to literature, the studies on commercial banks’ profitability would
provide more elaborate and current information that is important for policy for the sector
and also scholarly literature.
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2.4 Hypotheses Testing
The major hypothesis of this study was to evaluate weather bank level as well as
macroeconomic factors are important in explaining commercial banks’ profitability in
SSA.
The report is organized as follows. Chapter one presents the background and study
motivation. In Chapter II, the determinants of banks’ profitability are explored. In chapter
three, the determinants of banks’ total factor productivity are investigated.
The analysis was based on unbalanced panel for 224 banks, operating in 42 countries for
the period 1999 to 2006. The study follows an extensive literature that focuses on bank
level as well as macroeconomic factors as main determinants of bank profitability. Bank
data was derived from the Bank-Scope and macroeconomic data from International Finance
Statistics database. A dynamic panel specification was adopted to estimate the determinants
of banks’ profitability in SSA. Return average assets and net interest margin were adopted
as alternative measures of bank profitability and considered dependent variables; while
bank-specific and macroeconomic factors; as explanatory. Bank-specific factors include:
growth in bank assets, capital adequacy, asset quality (credit risk), operational efficiency,
and liquidity ratio; while macroeconomic variables include inflation and growth in GDP.
The main conclusion of the study is that bank-specific as well as macroeconomic factors
explain the variation in SSA commercial banks’ profitability.
The report is structured as follows. Section 2.1 presents the introduction Section 2.2
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describes the literature review of some of the important studies on bank performance. In
section 2.3, empirical model and methodology are discussed. In section 2.4, empirical
findings are discussed, while in 2.5, conclusions and some implications for policy are
indicated.
2.5 Review of Literature on Bank Profitability
This section explores the empirical literature and the various methods used in studying
commercial bank’s profitability. In theory, determinants are categorized into three
indicators: bank-specific, industry-specific and macroeconomic. Bank specific indicators
include: growth in bank assets, capital adequacy, operational efficiency, and liquidity. The
common measure for industry-specific representative used in the various studies is bank-
concentration. While on the other hand, the key macroeconomic variables include: growth
in GDP, GDP-per-capita inflation expectation, interest rate and its spread. The empirical
evidence provides the various methods employed in studying bank profitability using these
determinants. Much of the empirical literature agrees that bank level as well and
macroeconomic factors largely influence bank profitability. There is however limited
evidence that industry-specific factors have any influence on bank profitability. It is against
this background that the study utilized only bank level and macroeconomic factors to
estimate profitability.
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2.5.1 Bank-specific determinants
In trying to understand commercial banks’ performance in Sub-Saharan Africa, studies on
profitability have largely focused on returns on bank asset or equity and net interest margin.
Also, traditionally, the impact on banks’ performance has been measured by bank-specific
factors such as capital adequacy, credit risk, liquidity risk, market power and regulatory
costs. More recently, research seems to have focused on the impact of macroeconomic
factors on banks’ performance. In all these studies, however, the literature reveals that Sub-
Saharan Africa is less studied and therefore would require more information on banking
sector for better planning for the sector. This study therefore, was an attempt to address the
gap of information on SSA commercial banking sector.
In investigating bank profitability,Short (1979), Bourke (1989), Molyneux and Thornton
(1992), Demirguc-Kunt and Huizinga (2000) and Goddard et al (2004), among others,
applied linear models to explain performance. Linear models have however been criticized
for employing inconsistent variables and generating inefficient results. All this evidence
points to the fact that that more data would be required to understand banks’ performance
in the developing countries.
Cornett and Tehania (1992), Mercia, et al. (2002), Toddard, et al. (2004), Guru et al.(2001)
and Panayiotis et al.(2006) show that bank profitability is a function of internal and
external factors. Internal factors include bank-specific; while external factors include both
industry-specific and macroeconomic factors. According to this literature, there are six
standard key bank-specific indicators that are widely used to study banks: profitability;
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capital risk; asset quality; operational efficiency; and growth in bank assets. Industry–
specific factors include: ownership, bank size, bank concentration index; while
macroeconomic factors include interest rate, interest rate spread, inflation and per-capita
income and growth in GDP. Most of these factors are included in this study to estimate
bank profitability for SSA banks.
Appling the General Method of Moments (GMM) technique to a panel of Greek banks for
the period 1985 to 2001 period, Panayiotis et.al. (2006) discovered that bank profitability
persist to a moderate extent. Persistence suggests that departures from perfectly competitive
market structures may not be large. The study further shows that all bank-specific
determinants, with the exception of size, influence bank performance in the anticipated
way. The study on Malaysian banks by Guru et al. (2001) also show that efficient
management is among the most important factors that explain high bank profitability.
Extending a similar study to SSA, therefore, generates comparative results.
Al-Haschimi (2007) investigates the determinants of bank net interest margin in 10 SSA
countries, and applies an accounting decomposition model as well as panel regressions.
Further found out that credit risk and operational inefficiencies explain most of the
variation in net interest margins across the region, with macroeconomic factors, having less
influence on performance
Smirlock (1985) found a positive and significant relationship between size and bank
profitability. Short (1979) discovered that size is closely related to capital adequacy of a
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ank since relatively large banks tend to raise less expensive capital and, hence, appear
more profitable. Using similar arguments, Bikker and Hu (2002) and Goddard et al. (2004),
among others; linked bank size for small to medium size banks to capital and profitability.
Molyneux and Thornton (1992), among others; found a negative and significant
relationship between the level of liquidity and profitability. In contrast, Bourke (1989)
reported an opposite result; while the effects of credit risk on profitability could be negative
(Miller and Noulas, 1997).
There is also an extensive literature based on the idea that an expense-related variable
should be included in a profit function. For example, Bourke (1989) and Molyneux and
Thornton (1992) found a positive relationship between better-quality management and
profitability. Athanasoglou, et al. (2006) study on the South Eastern European banking
industry over the period 1998 to 2002, suggested a new approach in understanding bank
profitability.
2.5.2 Industry-specific determinants
Another strand of literature emphasizes the importance of market structure and bank
specific variables in explaining performance heterogeneities across banks. This literature is
based on the structure-conduct-performance (SCP) paradigm and is also applicable to
contestable markets, firm-level efficiency, and the roles of ownership and governance in
explaining banks’ performance (Berger, 1995; Berger and Humphrey, 1997; Bikker and
Haaf, 2002; Goddard et al., 2001; Goddard et al., 2004; and Molyneux et al., 1996). It was
explained that, the extensive empirical evidence does not provide conclusive proof that
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ank performance is influenced either by concentrated market structures and collusive price
setting behavior or superior management and production techniques. Bank efficiency levels
were found to vary widely across banking sectors (Altunbaş et al., 2001; Schure et al.,
2004).
Smirlock (1985) and Berger (1995) investigated the profit structure relationship in banking,
providing tests of the aforementioned hypotheses. To some extent, the relative market
power hypothesis was verified; since there was evidence that superior management and
increased market share (especially in the case of small to medium sized banks) raise profits.
In contrast, weak evidence was found for the efficient structure hypothesis. Explained that
efficiency not only raises profits, but may lead to market share gains and, hence, increased
concentration, so that the finding of a positive relationship between concentration and
profits could be a spurious result due to correlations with other variables. Bourke (1989),
Molyneux and Thornton (992) argued, instead that increased concentration is not the result
of managerial efficiency, but rather reflects increasing deviations from competitive market
structures, which leads to monopolistic profits. Consequently, concentration should be
related to bank profitability.
These studies further questioned whether the ownership status of a bank is related to its
profitability or not. However, little evidence is found to support the theory that privately-
owned institutions will return relatively higher economic profits (Short, 1979). In contrast,
Bourke (1989) and Molyneux and Thornton (1992) revealed that ownership status could be
irrelevant for explaining profitability. Eichengreen and Gibson (2001) analyzed bank-
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specific and market-specific profitability determinants of Greek banks for the period 1993
to 1998; using a panel not restricted to commercial banks. The results revealed that
industry-specific variables such as concentration ratio and market share have positive and
significant influence on bank profitability. Recent literature also emphasizes the importance
of changes in macroeconomic conditions on bank profitability. All this evidence provides
sufficient information that bank concentration may not be a larger factor in explaining
banks’ profitability behaviour.
2.5.3 Macroeconomic determinants
The last group of profitability determinants deals with macroeconomic control variables.
The common variables include inflation rate, the long-term interest rate and rate of
economic growth (Panayiotis et al., 2005). More recently, a number of studies emphasize
the relationship between macroeconomic variables and bank risk. Allen and Saunders
(2004) provided evidence of the importance of macroeconomic factors in determining the
profitability of banks in the sampled
Rovell (1979) introduced the issue of the relationship between bank profitability and
inflation. Noted that the effect of inflation on bank profitability depends on whether bank
wages and other operating expenses increase at a faster rate than inflation. The question is
how mature an economy is so that future inflation is accurately forecasted to enable banks
manage their operating costs. Perry (1992) observed that the extent to which inflation
affects bank profitability depends on whether inflation expectations are fully anticipated.
An inflation rate fully anticipated by the bank management implies that banks can
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appropriately adjust interest rates in order to increase their revenues faster than their costs
and thus acquire higher economic profits. Bourke (1989) and Molyneux and Thornton
(1992) found a positive relationship between inflation and bank profitability.
Saunders and Schumacher (2000) apply a model of Ho and Saunders (1981) to study the
determinants of interest margin in six European Union and US banks during the period
1988 to 1995. They further established that macroeconomic volatility and regulations have
a significant impact on bank interest margin. The result pointed out an important trade off
between ensuring bank solvency, as defined by high capital to asset ratio, and lowering cost
of financial services to consumers, as measured by lower interest rate margin.
Bourke (1989), Molyneux and Thornton (1992), Demirguc-Kunt and Huizinga (2000) and
Bikker and Hu (2002) identified possible cyclical movements in bank profitability. Bikker
and Hu. (2002) established that bank profits are positively correlated with movements in
the business cycle. Afanasieff et al. (2002), Guru et al. (2002) and Naceur and Goaied
(2001; 2003), and Barajas et al. (1999) study on emerging countries (Brazil, Colombia,
Malaysia and Tunisia) documented significant effects of financial liberalization on banks’
performance.
Afanasieff et al. (2002) also made use of panel data techniques to uncover the main
determinants of bank performance in Brazil and found out that macroeconomic variables
such as GDP growth rate, inflation expectations are important in determining bank
profitability over time. Neeley and Wheelock (1997) also explored the profitability of
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sampled US commercial banks and found a positive impact of per-capita income on
profitability.
Overall, empirical review for this research provides back ground information of bank
profitability. There is ample evidence of comprehensive account of developed countries and
a few of developing ones, but less of SSA, signifying the requirement for further research
on the sub-region. Empirical findings provide support that bank profitability is influenced
by both internal, sector specific as well as macroeconomic factors. This study utilized some
of the identified key variable indicators that were identified in earlier studies to have an
influence on commercial banks’ profitability. Further, there is proof that in these studies,
more of static models than dynamic have been applied to study banks’ profitability.
Dynamic panel models therefore deem to be considered as important specifications for
providing more rigorous and efficient results on banks’ performance in the context of SSA,
which is an extension of these studies. This study on SSA commercial banks; becomes an
important step in providing more empirical information on the sub-region’s commercial
banking sector; using dynamic panel models. These methods are expected to generate more
efficient results.
2.6 Conceptual Framework
In this section, the theoretical basis of the relationship between banks’ profitability and
explanatory bank-specific as well as macroeconomic factors is presented. From the
theoretical relationship, the conceptual model that was used to estimation is presented.
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2.6.1 Theoretical basis for the model
A cost efficiency model was employed to measure bank profitability. This approach was
adopted from the work done by Marcia, et.al. (2002), Marcos (2003), Kang and William
(2005), Goddard, et. al. (2004), and Panayiotis et al. (2005; 2006) on bank efficiency in
developed and a few developing economies. These studies employed dynamic panel
specifications to estimate the determinants of bank's performance. These approaches are
understood to generate reliable estimates on larger samples (Evanoff and Israelvich, 1991;
Wheelock and Wilson, 1999). In measuring bank's performance, the key bank indicators
aggregated into as well as some macroeconomic factors, such as inputs and outputs were
utilized to measure performance.
2.6.2 Cost efficiency model for generating the profitability function
The cost efficiency frontier is a technical efficiency concept based on a production function
that is used to measure bank cost efficiency. Cost efficiency is derived from the cost
function and is a modified form of Cobb-Douglas function. This provides information on
how close (or far) bank costs are from the best practice, producing the same output under
similar conditions. Cost efficiency therefore reflects the position of particular bank relative
to the cost frontier. A stochastic cost frontier function is presented, where C (.) is a suitable
functional form;
lnci = C(yi,wi, β ) + νi+ui ; I = 1,2.................................., N....................................(2.1)
Where ci is the observed cost of production; yi is the logarithm of output quantity; wi is the
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vector of logarithms of input prices, β is a vector of unknown parameters to be established;
vi is the random error term and ui is the non-negative inefficiency effect.
Coelli, Rao and Battese (1998) showed that inefficiency factor ui is included because the
cost frontier represents minimum costs. 1
The random error vi accounts for measurement
errors and other random factors. Inefficiency factor accounts for both technical inefficiency
and allocative inefficiency. The random error and the inefficiency term are assumed to be
multiplicatively separable from the cost frontier Berger and Mester (1997). Efficiency
measurement techniques differ in how they separate the composite error term vi + ui , i.e.
how they distinguish the inefficiency term from the random error.
Battese and Coelli (1992) employed a stochastic frontier specification of the cost efficiency
model to study Central and Eastern Europe to estimate bank cost efficiency. Likewise,
Marko (2006) employed a common frontier function in analyzing efficiency gaps between
East and West Europe banks following integration. In the same vein, this methodology was
adopted to study SSA commercial banks in this study. Using the basic model specification
(2.1), a log linear generalized production function framework was utilized for estimating
bank profitability.
The structural model is presented in the form;
ln Ci = α + αi ln Σwi + αij lnΣΣwi + Σ βk ln yk z ……………. lnvi + lnui……… .(2.2)
1 The production frontier represents maximum output, and ui is subtracted from it.
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Where; C is total cost; yk is the k-th output; wi is the i-th input price; z is the equity capital;
v is measurement error term; and u is the inefficiency term. The function could further be
decomposed to;
ln (yit) = xitβ + vit - uit ...............(2.3) i= 1,2……………... N; t = 1.2………..... T
Where; yit is the cost per output of i-th firm in the t-th time period, xit is a K–vector of value
of bank variables used as explanatory associated with functional specification, β is K-vector
unknown parameter to be estimated, vit are random errors assumed to be independently
normally distributed, with uits and uit being technical inefficiency effects. Equation (2.3) is
the reduced form of the cost function that is utilized to estimate bank profitability. Different
distributions of uits are assumed for different panels (Coelli, Rao and Battese, 1998). The
model permits estimations of unbalanced panels and uis are assumed to be exponential
function of time, involving only one unknown parameter.
Estimating bank profitability, this study also adopts a similar framework as also applied by:
Wilson et.al. (2004) on European banks; Naceur (2003) on Tunisian banks; and Panayiotis
et al. (2005) on Greece banks. In these studies, bank performance measurement is
expressed in terms of profitability as follows;
Пit = α0i + α1Пi t-1 + + α2Пi t-2+ α3g t-1 + βiXit + γMacro + uit...............................(2.4)
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Where П is the profitability variable and Xi = other bank variable indicators, and git is the
growth rate variable given by logarithmic value of bank size (proxy by total bank assets).
Empirical studies identify average asset (ROAA) and net interest margin (NIM) as common
possible choices for measuring bank profitability, though at times return on average equity
have been used. The former, however are common features in this reviewed literature for
this study. In the same way, were adopted for this study.
2.6.3 Panel specification for bank profitability
Panel specification adopts a cost efficiency functional framework of equation (2.3) and
expressed as follows;
Пit = c + ΣβiXit +εit …............................................................................................(2.5)
εit = ηi + λt +vit ;which is a two way error correction component.
Where Пit is profitability of bank i at time t, with i =1,………N; t = 1, T; c is a constant
term; ηi is the unobservable bank specific effects; and λt is the time-specific effects and vit is
the remainder error term assumed to be white noise stochastic error term, α is a constant
and β is a (Kx1) vector of the coefficients of K explanatory variables as bank indicators.
Using log linear transformation, equation (2.5) decomposes to;
lnПit = c + βi lnXit + εit …………………………………………………………………(2.6)
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The explanatory variables Xits are grouped into bank specific; industry specific and macro-
economic variables. Thus, a general specification of the profit function becomes;
ln∑Пit = c + βiln∑ J Xit + βiln∑X I it + βiln∑Xit M + εit………………..….…..…......2.7)
Where; Xits with subscripts J, I and M, denote bank–specific, industry-specific and
macroeconomic determinants, respectively. The model is represented as a two way error
correction component where εit is given as:
εit = ηi + λt +vit…………………………………………………………………………….2.8)
2.6.4 Model specification and variables
Model specification and variable identification was implemented in line with Cornett and
Tehania (1992), Naceur (2003) and Panayiotis et al. (2005) classification of bank
indicators. Bank indicators are classified into six categories: profitability that measures the
overall performance of the bank; capital adequacy that measures the bank ability to meet
regulated capital standards; credit risk that measures changes in the bank loan quality and
risk; operational efficiency that measures the bank ability to generate revenue, pay,
expenses and measure of employment expense; liquidity ratio that measures the changes in
the bank cash position; and growth indicator that measures the bank change in assets. It is
on the basis of this classification that the regression analysis of estimating bank profitability
was done.
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Bank profitability is the dependent variable in this study. Bank profitability can be
efficiently represented by two alternative measures: return on average asset (ROAA) and net
interest margin (NIM) alternative measure (IMF, 2002; Yigremachew, 2008; and Weaver,
2001). Thus, depending on data availability and consistency, these measures are applicable
to study bank profitability. Return to average assets reflects the bank ability to generate
profits from bank assets although it may be biased due to off-balance sheet activities
(Panayiotis et al., 2005 and 2006). Return on assets is often referred to as the bank equity
multiplier and measures financial leverage of the bank. Depending on data availability and
consistency these variables have been extensively applied in measuring bank profitability.
In this study, these variables were adopted for estimation and their efficiency compared in
the analysis.
Other bank variable indicators taken as explanatory are also explained. Capital adequacy
expressed as equity to total asset ratio (eta), growth in bank deposits (log.td), operational
efficiency as cost to income ratio (ctir), liquidity as net loan to total asset ratio (nlta), and
growth indicator as growth in bank asset (log.ta ).
To isolate the effects of bank characteristics, it is necessary to control for other factors that
are used as determinants of bank profitability. In addition, the macroeconomic variables
that influence bank profitability; GDP growth and inflation expectation were also included
in the specification. Industry-specific variables such as bank concentration were not
included due to data limitation and lack of a clear formula to estimate the variable. Even
then, there is evidence that these variable representatives may have less significance to
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ank’s profitability. Their impact is reported largely to depend on other factors. The study
therefore concentrated more on bank-specific and macroeconomic factors.
Using the profitability function equation (2.7) and considering actual variable notations, the
specification is given by;
Пit = c + β1lnTAit + β2ETAit + β3lnTDit + β4CTIRit + β5 NLTA it+ β6 lnGDPAit
+β7 INFLit-1 + εit ………………...….......................................................................(2.9)
Where Пit: is profitability variable represented by either return to average assets (ROAA) or
net interest margin (NIM), (LnTA) is growth in bank assets, (ETA) is bank equity to total
assets, (lnTD) growth in bank deposits, (CTIR) is cost to income ratio, (NLTA) is net loans
to total assets, (lnGDPA, is GDP-growth and (INFL) is inflation expectation given by
current inflation.
To capture profit persistence over time in the panel bank data, a dynamic specification was
adopted in accordance with (Berger et al. (2000) and (Baltagi, 2001) 2
. A dynamic
specification includes a lagged dependent variable and is given as;
Пit = c + δПi,t-1 + β1lnTTAit + β2ETAit + β3lnTDit + β4CTIRit + β5NLTAit
+β6lnGDPAit +β7INFLit-1+ εit.............................................................................. (2.10)
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2 Few studies have considered profit persistence in banking as indicated by (Le vonian, 1993;
Roland, 1997; Eichengreen and Gibson, 2001; Goddard et al., 2004 and Gibson, 2005). In the
industrial organization literature and important contribution is Geroski and Zacquemin (1988).

(Пi, t-1) is the one period lagged profitability and δ is the speed of adjusting to equilibrium.
A value of δ between 0 and 1 implies that profits persist, but they will eventually return to
their normal (average) level. A value close to 0 means that the industry is fairly competitive
(high speed adjustment), while a value of δ close to 1 implies less competitive structure
(very slow adjustment) (Panayiotis et al., 2005).
2.6.5 Determinants of commercial banks’ profitability and hypotheses
In estimating bank profitability for SSA commercial banks, the profitability variable was
considered as dependent variable. Empirical literature suggests return on average assets
ROAA and net interest margin NIM, as appropriate choices for measuring bank
profitability. These have been adopted in this study to provide comparative results.
Bank–specific variables and expected impact on profitability
Bank growth indicator is given by natural logarithm of total bank assets. Boyd and Runkle
(1993) established a significant inverse relationship between size and return on assets in
U.S banks from 1971 to 1990 and positive relationship between financial leverage and size
of banks. Berger, et al. (1987) showed that banks experience some diseconomies of scale to
negatively affect performance. Goddard, et al. (2004), on five European countries, observed
that the growth in bank size could positively influence bank performance. These
observations suggest that the expected impact of bank size on bank profitability could be
mixed.
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Capital adequacy indicator measured by bank equity to total assets, refers to the amount of
own funds available to support a bank business and acts as a safety net in the case of
adverse selection. It could also measure the bank’s ability to withstand losses. Banks with
substantial capital adequacy ratio may be over cautious, passing up profitable investments
opportunities. Alternatively, a declining ratio may signal capital adequacy problems.
Capital is an important variable in determining bank profitability, although in the presence
of capital requirements, it may proxy risk and also regulatory costs. In imperfect capital
markets, well-capitalized banks may need to borrow less in order to support a given level of
assets, and tend to face lower cost of funding due to lower prospective bankruptcy costs.
Athanasoglou et al. (2005) and Berger. (1995) noted that in the presence of asymmetric
information, a well-capitalized bank could provide a signal to the market that a better-than-
average performance should be expected. Well-capitalized banks are, in this regard, less
risky and profits should be lower because they are perceived to be safer. In this case, we
would expect to observe a negative association between capital and profits. However, if
regulatory capital represents a binding restriction on banks, and is perceived as a cost, we
would expect a positive relationship to the extent that banks try to pass some of the
regulatory cost to their customers. Profits may also lead to higher capital, if the profits
earned are fully or partially reinvested. In this case, we would expect a positive causation
from profits to capital. Athanasoglou, et al. (2005b) found a positive and significant effect
of capital on Greek banks bank profitability, reflecting the sound financial condition of the
banks. Likewise, Berger (2005) established a positive causation in both direction between
capital and profitability. Hence, the expected influence of this variable could either positive
or negative.
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Credit risk indicator can be represented by different measurements including loans loss
provision to total loans ratio as well as growth in bank deposits. Higher provisions for loan
losses could signal the likelihood of possible future loan losses, though it could also
indicate a timely recognition of weak loans by prudent banks. Some researchers have used
loan loss provisions to measure credit risk. Loan loss provisions are part of the accounting
breakdown of the revenue itself, which would, apriori, induce a significant negative
correlation between the two variables. Loan loss provisions are also likely to account for
realized losses rather than risk. On the other hand, deposit-to-loan ratio could also measure
different levels of credit risk across countries if the respective practices on income
verification and collaterals are different. Al-Haschimi (2007) found a positive effect of
credit risk on Sub-Saharan Africa’s net interest margins. With perfect capital markets and
no bankruptcy costs, the capital structure (how assets are financed) does not matter and
value can only be generated by the assets. However, with asymmetric information and
bankruptcy costs, the specific way in which assets are funded could create value. So the
expected impact of this variable to bank profitability could be mixed.
Operational efficiency indicator which is also referred to as expenses by management is
given as cost to income ratio. The higher this ratio, the less efficient and bank could
adversely be affected in return on assets, depending on the degree of competition in the
market. Al-Haschimi (2007) showed that operating inefficiencies appear to be the main
determinants of high bank spreads in SSA economies. Brock and Rojas Suarez (2000) also
established that administrative and other operating costs contribute to the prevalence of
23

high spreads in Latin American countries. Some other studies (Bourke, 1989; and
Molyneux and Thornton, 1992) revealed a positive relationship between better quality
management and profitability in European banks. This variable could therefore have a
positive or negative impact on bank profitability, positive with better quality management
at reduced costs, negative at higher inefficiency levels at higher costs.
Liquidity risk indicator is measured by bank net loans to total assets or a percentage of
assets that comprise the loan portfolio. High ratios may be an indicative of better bank
performance because of possible increases in interest income. However, very high ratios
could also reduce liquidity and increase the number of marginal borrowers that default.
This is also considered as bank activity mix and also an important proxy for the overall
level of risk undertaken by banks to the extent that different sources of income are
characterized by different credit risk and volatility. Demirgüç-Kunt and Huizinga (1998)
study of banks in 80 countries found that those with relatively high non-interest earning
assets are, in general, less profitable. Banks that rely on deposits for their funding are also
less profitable, possibly due to the required extensive branch network, and other expenses
that are incurred in administering deposit accounts. Thus Again, the effect to bank
profitability of this variable could be mixed.
Macroeconomic variables and expected impact on bank profitability
Bank performance is expected to be sensitive to macroeconomic control variables. The
impact of macroeconomic variables on bank risk has recently been highlighted in the
literature. GDP growth is adopted as a control for cyclical output effects, and expected to
24

have a positive influence on bank profitability. As GDP growth slows down, and, in
particular, during recessions, credit quality deteriorates, and defaults increase, thus resulting
into reduced bank returns.
Demirgüç-Kunt and Huizinga (1998), and Bikker and Hu (2002) discovered a positive
correlation between bank profitability and the business cycle. By employing a direct
measure of business cycle, Athanasoglou, et al. (2005) found a positive, notwithstanding
asymmetric, effect on bank profitability in the Greek banking industry, with the cyclical
output being significant only in the upper phase of the cycle. Al-Haschimi (2007) further
established that the macroeconomic environment has only limited effect on net interest
margins in SSA countries. This evidence is consistent with the results of other country-
specific studies (Chirwa and Mlachila (2004) for Malawi, and Beck and Hesse (2006) for
Uganda). GDP growth is therefore expected to have mixed impact on bank profitability
depending on trend growth of the economy.
The account for macroeconomic risk is also by controlling for inflation. It is envisaged that
the extent to which inflation affects bank profitability depends on whether future
movements in inflation are fully anticipated, which, in turn, depend on the ability of firms
to accurately forecast future movements in the relevant control variables. An inflation rate
that is fully anticipated increases profits as banks can appropriately adjust interest rates in
order to increase revenues, while an unexpected change could raise costs due to imperfect
interest rate adjustment.
25

Other studies, for example, Bourke (1989), Molyneux and Thornton (1992), Demirgüç-
Kunt and Huizinga (1998), have found a positive relation between inflation and long term
interest rates with bank performance. Inflation rate is approximated by the previous
period’s actual inflation and could positively or negatively influence bank profitability,
positive due to the ability of bank management to satisfactorily, though not fully forecast
the future inflation, which in turn could be incorporated into interest rate margins to
achieve higher profits. The expected impact of this macro variable is therefore mixed.
2.6.6 Robustness and specification tests
In the estimation of panel data, both static and dynamic specifications were checked using
both fixed FE and random effects RE estimators. In addition, efficiency evaluation was
alternately analyzed by including return on average assets (ROAA) and net interest margin
(NIM) as dependent variables, representing bank’s profitability. To check for efficiency
between the feasible generalized least square FGLS and pooled least square dummy
variable estimators LSDV, the Modified Wald Statistic test was applied. Further, testing for
efficiency between the random effects and fixed effects estimators is by Hausman
Specification test. The efficiency of the Generalized Method of Moments GMM–IV was as
an estimator, was also tested.
The modified Wald Statistic result confirmed a rejection of the null hypothesis at 5 percent
and 10 percent levels of significance; suggesting that the FGLS technique was appropriate
for the study. Hausman Specification test confirmed the efficiency of the RE estimator in
the measurement of bank profitability for SSA commercial banks.
26

Regression results revealed that the random effects (RE) technique generated more efficient
results than the fixed effect (FE) .This is consistent with theory that random effects
estimator is expected to generate more efficient results where a lagged dependent variable
is included as explanatory. Efficiency is achieved in controlling for a possible endogeinty
and auto-correlation effects associated with dynamic lag models (Arrellano and Bover
(1995) and Blundell and Bond (2000).
The evaluation also looked at the efficiency of the GMM-IV estimator and proved the
technique and found it inefficient for estimating the bank equation. The findings confirmed
by Blundell and Bond (2000) that in panels of shorter time periods (T) and larger
observations (N), which was the characteristic of this study, fixed effects and random
effects models tend to generate more efficient results than the Generalized Method of
Moments GMM-IV estimator. Given the efficiency advantage over other estimators, the
results are based on the random effect estimation method.
Panel unit-root and Co-integration tests were also implemented using the generalized
Dickey-Fuller (DF) test using the Fisher-test that is appropriate for unbalanced panels
(Baltagi (1999). It is indicated that this is an appropriate choice for testing non-stationarity
in the panel as the null hypothesis. The fisher-test uses four other tests including inverse-
chi-squared test (P), inverse normal (Z), inverse logit (L*) and modified inv.chi-squared
(PM). Rejection of the null hypothesis is when the P-Values are less than the critical values
at 0.01, 0.05 and 0.10, percent levels, respectively. Baltagi (1998 &1999) concluded that
when panels are stationary, it so happens that they are integrated and could at least generate
27

at least one co-integrating equation. The specification checks also included some
interaction analysis of at least two to three paired of variables to check their combined
effect to bank’s profitability.
2.6.7 Methodology, empirical data and analysis
To construct the sample, data was drawn from financial statements of individual banks
provided in the Bank-Scope-Database. The Bank-Scope Database is a collection of data of
balance sheets, income statements and other relevant financial accounts of several banks in
the World. The data base was accessed through Bank of Uganda (BoU). To ensure
consistency, only data for commercial banks in the unconsolidated format was used. The
period of study is 1999 to 2006.
Mathieson and Roldos (2001) indicated three important characteristics of the Bank-Scope-
database. First, its comprehensive coverage as Bank Scope data on banks accounts for
around 90 percent of total bank assets in each country. Second, comparability, the data-
collection process is based on separate data templates for each country to accommodate
different reporting and accounting standards. Bank-Scope adjusts the collected data for
country variation and presents them in a so-called global format. It is a globally
standardised form for presenting bank data. Thus, Bank-Scope Databases are comparable
across banks and across countries and allows cross-country comparisons (Claessens,
Demirguc-Kunt and Huizinga, 2001). Third, Bank-Scope Databases provides information
for individual banks, which are usually not available from other sources. Other data sources
included International Monetary Fund- Financial Statistics.
28

Data was generated from 42 countries and 224 commercial banks with at least two years of
operation between 1999 to 2006. In total, there were 1316 observations. In order to account
for time trend in the data set, a dynamic specification with a lagged dependent variable used
as explanatory. The variables considered in the specification include: bank asset growth
in assets, capital adequacy, credit risk, operating efficiency, liquidity ratio. Macroeconomic
variables that measure the influence on bank profitability also to augment the explanatory
variables. These include growth in GDP and inflation. Data was downloaded in Microsoft
Office, arranged in panel sets and analyzed using STATA- 10 and II, respectively.
In order to understand the variability in banks’ profitability across the SSA, the countries
were further categorized in low income GDP-per-capita below 750 US dollars, medium
income GDP-Per-capita between 750 to 1500 US dollars and higher income with GDP per-
capita of above 1500 US dollars per year. In the analysis, although an attempt was made to
uncover the interaction effects of some related pair of variables, the results were not
efficient and therefore dropped in the analyses.
2.7 Discussion of Results
2.7.1 Introduction
This section provides a discussion of the quantitative results for the study. They focus on
data characteristics and regression relationship of the bank level as well as macroeconomic
factors to banks’ profitability for sampled banks from 42 countries. While the first part
gives a brief summary of data characteristics in terms of unit-root test, descriptive statistics
and correlation relationship in form of tables, the second part presents a detailed
presentation of the regression results. Results showed that bank-specific as well as
29

macroeconomic variables have significant influence to the bank’s profitability.
2.7.2 Data characteristics
a) Descriptive statistics of the variables
Table 2.1 presents the descriptive statistics of the variables utilized in this study. The results
confirm the adequacy of the data used in estimating commercial banks’ profitability, all
ranging above 1000 observations. Looking at the minimum, mean and maximum values,
generally, the statistics indicate a wide variation in both the bank-specific and macro
determinants of profitability of banks across the sub-regional groupings of countries within the
SSA. This had an important implication to the approach used in estimating commercial banks’
profitability in the SSA countries, by dividing the countries in low-income, medium income
and high-income categories, using panel data technique in the empirical analysis to correct for
possible variations. On the average, poor performance of the bank indicators variables, with
a wider variation across counties in the sub-region. Poorest performing countries lie in the
low income category amongst which experienced political instability including Burundi,
Gambia, among others. This category constituted about 73 percent of the banks. Better
performing countries lie in the medium and high income categories and includes Botswana,
Cameroon, and Madagascar among others, which implemented successful financial sector
reforms during the 1990s (IMF, 2005).
30

Table 2.1: Descriptive statistics of the variables
Variable Obs. Mean Min Max
Return on average asset
1297 8.29 -56.70 49.64
Net interest margin
1266 8.86 -6.57 517
banks assets
1306 81355 0.104 7628305
Bank deposits
1298 6874 -2.38 5874527
Operational efficiency
1207 62.98 1.66 974
Capital adequacy
1315 12.81 -40.72 80.27
Liquidity
1315 43.93 0.21 96.64
GDPA
Inflation rate
Source: Panel estimates: 1999 - 2006.
1138 1.27e+10 3.99e+08 25.80e+10
1091 13.87 -10.00 550
b) Correlation between variables
In table 2.2, the correlation relation between variable is described. The results confirmed
some level of correlation between dependent variable (return on average assest or net
interest margin) and independent variables (bank assets, bank deposits, operational
efficiency, capital adequacy, GDP and inflation). Overall, with the correlation relationships
between the variable in the range below 0.5, it would indicate that multicollinearity was not an
issue in these estimations, as no two variables were highly correlated.
Table 2.2 Correlation matrix between variables
Variable roaa nim lta td ctir eta nlta lgdpa inf
Return on average asset 1.0000
Net interest margin 0.2338 1.0000
Total bank assets 0.0717 0.1157 1.0000
Bank deposits 0.0727 0.1077 0.9843 1.0000
Operational efficiency -0.4073 -0.0679 -0.0423 -0.0425 1.0000
Capital adequacy 0.1518 0.2102 -0.0409 -0.0449 0.1463 1.0000
Bank liquidity -0.1255 -0.2805 -0.0521 -0.0534 -0.1468 0.0067 1.0 000
GDPA -0.0601 -0.0835 0.0396 -0.0382 0.0367 0.1383 -0.0110 1.0000
Inflation -0.0109 0.2157 0.302 -0.0283 0.0255 0.0316 -0.2263 0.0356
Source: Panel estimates: 1999 - 2006.
c) Panel unit-root and co-integration test
To check the efficiency of the variables in the model, unit-root test for stationary and co-
integration was applied. This was by the augmented Dickey-Fuller test, with Fisher-type
unit-root test for unbalanced panels (Baltagi, 1998) and other econometric literature
confirm this method as appropriate for unbalanced panels as it accommodates for any
31

number of lags. The fisher test uses four types of other tests: inverse-chi-squared (P),
inverse normal (Z), inverse logit (L*) and parametric test (PM). Table 2.1 shows the results
of the Fisher-test for stationarity. The tests were implemented in levels (zero differenced
and zero lag).
Table 2.3: Panel unit-root test results
Variable Fisher-tests for Unit-root Deduction
P Z L* PM P ≤ α ; p ≥ α
Return on average assets (roaa) pr= 0.0000 pr=0.0000 pr= .0000 pr= 0.0000 I(0)*
Net interest margin pr =0.000 pr=0.0000 pr=0.0000 pr=0.0000 I(0)*
Growth in bank size (ltta) Pr=0.0000 pr= 0.0300 pr=0.0000 pr =0.0000 I(0)**
Growth in bank deposits (ltd) pr =0.0000 pr=0.0000 pr=0.0000 pr=0.0000 I(0)*
Equity/total assets (eta) pr= 0.0000 pr= 0.0000 pr=0.0000 pr= 0.0000 I(0)*
Operational efficiency (ctir) pr= 0.0000 pr=0.0000 pr=0.0000 pr= 0.0000 I(0)*
Liquidity risk (nlta) pr= 0.0000 pr= 0.0200 pr=0.0000 pr= 0.0000 I(0)**
Growth in GDP (lgdpa) pr= 0.0000 pr= 0.002 pr=0.0000 pr= 0.000 I(0)*
Inflation (infl) pr= 0.0000 pr= 0.0000 pr=0.0000 pr= 0.0000 I(0)*
Note 1. * integrated at (1, 5, and 10)%; ** integrated at (5 and 10)%; and *** integrated at (10) %
2. P, Z, L* and PM refers to the four types of Fisher-test for unit-root; Pr=P-Value
Source: Panel Computation, 1999 - 2006.
The findings showed that all the variables were stationary at 5 and 10 levels of significance.
The expression I (0) refers to the level of integration at zero difference level, which is
characteristic of panel data sets. Integration means a rejection of the null hypothesis that
panel variables have unit root where p-value is greater than critical levels of measurement
(α) at (0.01,0,05 and 0.10) significance levels, respectively. When panel variables have not
unit root, they are stationary and therefore integrated. Econometric literature shows that
when variables are integrated, they generate at least one co-integrating equation for
32

efficiency analysis. This is confirmed by a rejection of the null hypotheses of stationarity.
The results prove that the key bank variables: growth in bank assets, capital adequacy,
credit risk, operational efficiency and bank liquidity; as well as the macro economic
variables; of growth in GDP and inflation expectation were efficient and appropriate in
measuring bank profitability in SSA over the study period 1999 to 2006 and results are
reliable.
2.7.3 Empirical regression results
Regression results are based on return on average assets (ROAA) and net interest margin
(NIM) as measures of bank’s profitability. In order to understand how commercial bank’s
profitability relates to a country’s level of economic performance, banks were grouped
according to GDP-per-capita rating into: low for GDP –per-capita in the range of up to US
dollars 750 , medium in the range if US dollars 750 to 1500 and high in the range of US
dollars above 1500 US dollars. Following these criteria, data distribution was as follows:
low income category had 959 observations, 160 group banks; medium income category had
197 observations and 33 group banks; while high income category had 160 observations
and 31 group banks. The regressions were run independently and results compared.
I: Results across all sampled commercial banks
Table 2.3 shows the regression results for all the sampled banks. Estimation was by
applying the random effects (RE) technique. Econometrics recommends the random effects
(RE) method as an efficient estimator for unbalanced panel models (Baltagi 1999). This
was confirmed by the Hausman specification test which evaluated the efficiency between
33

the random effects (RE) and fixed effects (FE) estimators for the panel regressions. This is
consistent with theory that random effects estimator is expected to generate more efficient
results after controlling for possible endogeinty and autocorrelation effects associated with
dynamic lag models (Arrellano and Bover (1995) and Blundell and Bond (2000).
The results for average assets (ROAA) and net interest margin (NIM) were compared. The
findings revealed that that ROAA was a more efficient method estimating the regression
results, though at a lower regression fit of 20.86 percent compared, but with a larger
number of explanatory variables with expected signs. This result however compares with
applying the NIM, with a regression fit of 56. 67 percent and but with a lesser number of
explanatory variables by one. Because of the preferred measure, the discussion of the
regression results was based on ROAA.
34

Table 2.3: Random effects regression results for all banks
Variable ROAA NIM
Dependent variable = roaa or nim Coeff. P-Value Coeff.- P-Value
Lagged return to average asset (Lag.roaa) -0.0811 0.014* - -
Lagged net interest margin (Lag.nim) - - 0.4636 0.000*
Growth in bank assets ( lntta) -0.7568 0.025* -0.4429 0.158
Growth in bank deposits (lntd) 0.9065 0.005* 0.5030 0.000*
Operational efficiency (ctir) -0.0375 0.000* -0.0130 0.004*
Capital adequacy (eta) 0.1530 0.000* -0.1080 0.000*
Liquidity ratio (nlta) -0.0523 0.000* -0.02473 0.002*
Growth in GDP (lnGDP) -0.3630 0.003* 0.2273 0.094***
Inflation (Infl) -0.0058 0.100*** 0.000 0.000*
Constant 13.0360 0.000 9.3410 0.003
Observations
773
775
Group banks
Wald Chi.2(8)
Prob. Chi.2
Overall regression
180
201.38
≥ 0.0000
20.86%
Source: Panel estimation, 1999-2006.
Note: *, **, ** = Significant at 0.01, 0.05 and 0.10, respectively
35
181
623.38
≥ 0.000
56.67%

Using the ROAA, the discussion of the regression results follow. The highly significant
coefficients of the lagged profitability variable confirm the dynamic character of the
banking system. The coefficient δ takes a value of approximately -0.081 which means that
there was profit persistence of about 8.1 percent across banks in most of the SSA countries.
This also suggested perfectly competitive market structure that could have been exhibited
by most of SSA commercial banking system for the period under study.
The coefficient of the variable representing capital adequacy (equity/total asset) is positive
and significant and consistent with the theory. This result shows that capital adequacy had
positive effect on bank profitability. The significance of the coefficient could probably
explain the relative growth in bank profitability achieved in most of the SSA countries
following financial sector reforms in early 1990s (IMF, 2002). The positive impact of the
variable to bank profitability in most SSA countries reveal some levels of increased
capitalization of the banks following the recent reforms in the financial sectors.
The coefficient of the variable representing credit risk (growth in bank deposit) is positive
and significant. This is consistent with theory and empirical evidence. Other things being
constant, Naceur (2003) explained that more deposits are transformed into loans for earning
interest incomes from borrowers. The higher the interest rate margins, the higher the profits
and banks are able to shield themselves against hazards of credit risk resulting from adverse
selection and moral hazard.
36

The coefficient of the variable representing operational efficiency (cost/income) is negative
and significant. This is consistent with theory that the higher costs of operation negatively
affect bank profitability. Operational efficiency indicator is the expense variable and
explains how banks could be efficient in resource allocation and utilization including
human resource and technological improvements in banking. The negative growth in
banks’ profitability that could have occurred during the period of study in a cross section of
SSA countries could be probably explained by high costs of operation.
The coefficient of the variable representing liquidity ratio (net loans/total assets) is negative
and significant. This is consistent with theory that liquidity ratio has a negative influence on
bank profitability such that high excess liquidity decreases bank profitability and low
liquidity improves bank profitability. Excess liquidity is a sign that bank lending is low and
banks are holding more money than statutory required for precautionary purposes. While,
low liquidity is a reflection that banks are holding less money in their accounts, an
indication of increased lending to the public, and thus implied growth in business and
profitability (Saxegaard, 2006) Indeed, excess liquidity of banks negatively influences bank
profitability and low levels of bank liquidity improves bank profitability.
The macroeconomic variables chosen for this study are growth rate in GDP and inflation
expectation. The coefficient of growth in GDP variable, measured at constant 2,000 US
dollars, is negative and significant at 10 percent. This finding agrees with theory and
empirical evidence that; the relationship between GDP trend growth and bank profitability
could be pro-cyclical. This would imply that when GDP trend growth is positive, the effect
37

to bank profitability is positive and when GDP trend growth in negative, the effect on
profitability is negative. An important finding from this study is that most of the economies
in SSA have of recent experienced very low and negative economic growth that could have
impacted negatively on bank profitability (Naceur; 2003 and Panayiotis et al., 2005). There
are several reasons why the effect of growth in GDP to bank profitability could be negative
or positive. Firstly, bank credit could decrease during economic down swings, since such
periods are normally associated with increased risk and vice-versa. In absence of this
variable however; it is also observed that this variable could be partly captured by bank-
specific variables.
For inflation variable, the coefficient is positive and significant, in net interest margin and
negative but insignificant in return to average asset. This is consistent with the finding by
Panayiotis et al., 2005 on Greek banks that that the effect of inflation on bank profitability
depends on the ability of inflation forecast by the bank’s management. If predictions
become correct, such adjustments in interest rates could be incorporated in inflation
expectation, to achieve higher profits. In this case, the effect to bank profitability becomes
positive A positive relationship between inflation and bank profitability would suggest that
banks are able to project the effect of inflation expectations in their operational costs to
increase profits. From this conclusion, if the forecast is incorrect, the effect of inflation on
bank’s profitability could be negative or less significant.
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II: Low income country commercial bank category
In table 2.4, the regression results for low-income commercial bank category are illustrated.
These are based on about 598 observations and 127 group banks, for return to average asset
and602 observations and 128 group banks, respectively. The overall regressions are 25.34
and 59.20 percent, respectively, for return on average assets and net interest margin,
measures of profitability considered in this study. If the conclusion is based on return on
average assets with about 25.34 percent regression fit and about 5 variables significant with
expected signs as compared regression fit of 20.86 percent and 7 explanatory variables
significant with expected signs, for the overall sample, it shows that the results for low-
income commercial bank group category were comparable with the total bank sample. This
is confirmed by a proportion of about 73 percent of the sampled banks drawn from the low
income commercial bank category. A similar argument also applies to when the analysis is
based on net interest margin measure of bank profitability.
Table 2.4: Random effects regression results for low-income category commercial banks
Variable ROAA Nim
Dependent variable = roaa & nim Coeff. P-Value Coeff.- P-Value
Lagged returned to average asset (lroaa) -0.1340 0.000 - -
Lagged net interest margin (lnim) - - 0.4311 0.000*
Growth in bank assets ( lntta) -0.9060 0.278 -0.1012 0.879
Growth in bank deposits (lntd) 1.0425 0.220 0.1624 0.811
Operational efficiency (ctir) -0.0310 0.000* -0.0173 0.000*
Capital adequacy (eta) 0.2192 0.000* -0.0850 0.000*
Liquidity ratio (nlta) -0.0620 0.000* -0.0181 0.039**
Growth in GDP (lnGDP) -0.5243 0.000* -0.2253 0.116
Inflation (Infl) -0.030 0.000* 0.0264 0.000*
Constant -0.00078 0.000 11.000 0.003*
Observations = 598
Group Banks = 127
Wald chi 2 (8) = 199.93
Prob. Chi 2 ≥ 0.0000
Overall regression =
25.34%
Source: Panel estimation, 1999-2006.
Note: *; **; *** = significant at 0.01, 0.05 and 0.10, respectively
39
Observations = 602
Group Banks = 128
Wald chi 2 (8) = 527.25
Prob. Chi 2 ≥ 0.000
Regression = 59.21%

III: Medium income country category commercial banks
An attempt was also made to compare the regression results of the medium and high
country income category commercial banks. Results for medium income category are
indicated in table 2.5. These results though will a relatively better fit, are not comparable
with the total sample results. The major reason could arise from sample and data limitation
of most of the variables. For a high income country category banks with relatively fewer
observations of about 97, the results were ambiguous to compare with the total sample.
Table 2.5: Random effects regression results for medium income category comm.bank group
Variable ROAA NIM
Dependent variable = roaa & nim Coeff. P-Value Coeff.- P-Value
Lagged returned to average asset (lnroaa) 0.2747 0.000 - -
Lagged net interest margin (lnnim) - - 0.3017 0.000*
Growth in bank assets ( lntta) -0.3035 0.258 -2.6034 0.004*
Growth in bank deposits (lntd) 0.1038 0.625 1.3945 0.058*
Operational efficiency (ctir) -0.032 0.001* -0.0350 0.326
Capital adequacy (eta) 0.0241 0.217 -0.2770 0.000*
Liquidity ratio (nlta) 0.0010 0.912 -0.0610 0.067***
Growth in GDP (lnGDP) -0.0130 0.947 -0.001 0.967
Inflation (Infl) 0.0040 0.239 -0.0005 0.967
Constant 4.4440 0.356 16.7054 0.323
Observations = 104
Group Banks = 30
Wald chi 2 (8) = 49.41
Prob. Chi 2 ≥ 0.0000
Overall regression = 40.00%
Source: Panel estimation, 1999-2006.
Note: *; **; *** = significant at 0.01, 0.05 and 0.10, respectively
40
Observations = 102
Group Banks = 30
Wald chi 2 (8) = 49.41
Prob. Chi 2 ≥ 0.000
Regression= 52.94%

The major conclusion drawn from these comparisons in that the total sample and low
country income commercial bank category results were more appropriate for discussing the
results and drawing inferences on banks’ profitability. The medium and high income
category commercial banks’ results were inadequate for drawing conclusions on banks
‘profitability. The fact that the results of the low country income commercial bank category
had comparable results with the overall sample confirmed that the large composition of the
banks were from low income countries.
2.8 Conclusions and Some Implications for Policy
The study was conducted to investigate the influence of bank-specific and macroeconomic
variables on profitability, using a sample of 224 commercial banks from 42 SSA countries.
The lack of empirical studies on the determinants of bank profitability performance in SSA
motivated this study. A dynamic specification with a lagged profitability variable was
employed to estimate the model. Dynamic panel data models enable understanding of the
dynamic relationship between performance of variables and how they impact on persistence
of dependent variable. Efficiency tests adopts random effects estimator to report the panel
regressions results.
Overall, empirical findings provide evidence that profitability of SSA commercials banks is
influenced by bank-specific factors that have a direct relationship with bank management
and macroeconomic factors that are not the direct result of a bank managerial decision.
These findings call for a number of policy interventions in SSA; given the low poor
performance in terms of profitability. Low profitability levels reflected lack of
41

competiveness and inefficiency in the SSA banking sector. Policies would probably need to
be directed at improving risk management and technology, strengthening supportive
information and bank supervision, developing inter-bank, securities and equity markets and
at maintaining macroeconomic stability.
As indicated in the introduction, the main thrust of this research was to investigate the key
determinants of bank profitability in SSA. This study is a springboard for policy
improvement in the diverse financial sectors in SSA. The governments and other concerned
financial management institutions need to take into account the main fabrics and other
policy repercussions towards commercial bank profitability that have gained considerable
importance in SSA financial sector. This could probably be achieved through undertaking
comprehensive and rigorous stress testing to avoid risks associated with market failures in
the sector.
Cross-country studies give a comprehensive assessment between the economic
performance and policy prescription (including fiscal policy instruments) with banks’
overall performance. Supervisory and related services should be geared towards optimum
utilization of resources, prudent risk management, sound competitive environment and
excellence in service. For commercial banks in SSA, there is need to be more risk vigilant
related to changing macroeconomic factors in liberalized regimes across countries. Further,
it would also be important to look into long term effects of inflation on the overall bank
performance and need to expect asymmetric effect of such uncertainties on bank’s
profitability.
42

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Annex I: List of Countries Selected for the Study
1. Angola 22. Rwanda
2. Benin 23. Senegal
3. Botswana 24. Tanzania
4. Burkina Faso 25. Uganda
5. Burundi 26. Zambia
6. Cameroon 27. Cape Verde
7. Ghana 28. Central African Republic
8. Democratic Republic of Congo 29. Chad
9. Ethiopia 30. Congo Brazzaville
10. Gabon 31. Equatorial Guinea
11. Gambia 32. Eritrea
12. Ivory Coast 33. Guinea
13. Kenya 34. Liberia
14. Lesotho 35. Niger
15. Madagascar 36. SAO.Tome
16. Mali 37. Zimbabwe
17. Mauritania 38. Togo
18. Mauritius 39. Swaziland
19. Mozambique 40. Sierraloen
20. Namibia 41. Seychelles
21. Nigeria 42. South Africa
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