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Bank Competition in Sub-Saharan African Countries: Has Anything Changed in the Light of 2007-2008 Global Financial Crisis?

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This paper investigated the changes in competitive behaviour of banks in sub-Saharan Africa, following the 2007/2008 global financial crisis. Using 481 bank-year observations from an unbalanced panel of 83 banks from six countries over the period 2008-2013. We employed the Panzar-Rosse model of firm competition, and found that the degree of competition among banks in Sub-Saharan Africa increased. This increase is due to the effect of reform/liberalisation policies, largely initiated in the pre-crisis era. The success that followed via the development of banking systems, nonetheless moderated at the onset of the 2007/2008 financial crisis. System instabilities, which were characteristic of a post-crisis period, exposed deficiencies in regulation and asymmetric incentives for bank management. A significant recalibration of prudential policies followed, as regulators sought to restore system stability, which again had an impact in altering competitive conduct of banks. Policymakers should continue to develop and promote policies geared towards the development of financial intermediation and improved competitive conduct of banks in sub-Saharan Africa.
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Journal of Economics and Business
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Bank Competition in Sub-Saharan African Countries: Has
Anything Changed in the Light of 2007-2008 Global Financial
Crisis?
Steve Motsia, Oluseye Samuel Ajuwonb & Collins Ntimc
aDepartment of Development Finance
University of Stellenbosch Business School
South Africa. Email: steve.motsi@ariseinvest.com
bDevelopment Finance, University of Stellenbosch Business School, South Africa.
E-mail: aajuwon@unilag.edu.ng
cProfessor and Head of Department, Accounting,
University of Southampton Business School, United Kingdom
Email: c.g.ntim@soton.ac.uk
Abstract
This paper investigated the changes in competitive behaviour of banks in sub-Saharan Africa, following
the 2007/2008 global financial crisis. Using 481 bank-year observations from an unbalanced panel of
83 banks from six countries over the period 2008–2013. We employed the Panzar-Rosse model of firm
competition, and found that the degree of competition among banks in Sub-Saharan Africa increased.
This increase is due to the effect of reform/liberalisation policies, largely initiated in the pre-crisis era.
The success that followed via the development of banking systems, nonetheless moderated at the onset
of the 2007/2008 financial crisis. System instabilities, which were characteristic of a post-crisis period,
exposed deficiencies in regulation and asymmetric incentives for bank management. A significant
recalibration of prudential policies followed, as regulators sought to restore system stability, which
again had an impact in altering competitive conduct of banks. Policymakers should continue to develop
and promote policies geared towards the development of financial intermediation and improved
competitive conduct of banks in sub-Saharan Africa.
Keywords: Bank competition, competitive behaviour, Panzar-Rosse model, H-Statistics, E-Statistics,
sub-Sahara Africa.
JEL Classifications: D41, D42, D43, E32, E44, F36
1. Introduction
In the light of 2007/2008 financial crisis, this study investigated bank competition using the
Panzar-Rosse model, a non-structural approach that determines the pricing behaviour of firms.
The analysis contained sample banks in Sub-Saharan Africa between 2008 and 2013, a period
in which substantial macroeconomic challenges and increased systemic risk materialised
(Brambila-Macias & Massa, 2009; Arieff, 2010; Allen, Otchere & Senbet, 2011) because of
the financial crisis. However, in the pre-crisis era, significant economic reforms and financial
liberalisation policies (Bikker, Spierdijk & Finnie, 2007; Claessens, Laeven, Igan &
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Dell'Ariccia, 2010; Sanya & Gaertner, 2012; Fosu, 2013), were implemented, setting the tone
for increased competition among banks in Sub-Saharan Africa.
Increasing competitiveness among banks matters1 for a number of reasons. First, it promotes
effective financial intermediation, and explains the structure, stability, efficiency and
performance of the industry (Casu & Girardone, 2006; Fosu, 2013; Schaeck & Čihák, 2014).
Second, it lowers interest rates and improves the production, quality and distribution of banking
products. Third, it explains the level of access to financial services and extent of external
financing at household and firm level (Rajan & Zingales, 1996; Allen & Gale, 2004; Claessens
& Laeven, 2005; Bikker et al, 2007; Simbanegavi, Greenberg & Gwatidzo, 2012). Finally, it
improves effectiveness of monetary policy transmission, supports real economy production
efficiency and promotes overall growth and development (Claessens & Laeven, 2003, 2005;
Kot, 2004; Casu & Girardone, 2006; Bikker et al., 2007; Claessens et al., 2010; Fosu, 2013).
The primary motivation for this study stemmed from the sheer diversity of economies in Sub-
Saharan Africa, and their influence on competitiveness in banking systems. With an estimated
total GDP of approximately USD 1.60 trillion in 2013, and estimated 650 banks on the sub-
content (as obtained from each country’s central bank). Historically linked to World Bank and
IMF2 reform that commenced in the early 1980s (Senbet & Otchere, 2006), the structure and
size of economies and their banking systems reflect the extent of deregulation of markets and
trade, promotion of private enterprise and innovation, as well as ease of entry of foreign
participants. The Nigerian and South African economies, for example, comprise approximately
50 percent of total sub-continental GDP (WDI, 2013), and have large and diversified banking
systems that are competitive and active compare to most countries in the region. Medium-sized
economies such as Angola, Kenya and Ghana also have competitive banking systems with
significant foreign participants, while smaller island economies such as Mauritius and The
Seychelles have, by comparison, first-class leading banks and sophisticated financial products
and services. As such, the diversity, size and sophistication of economies and banking systems
in Sub-Saharan Africa triggered an investigation into competitiveness and performance of
banks.
The second motivating factor was a desire to investigate how the 2007/2008 financial crisis
triggered poor performance of banks (Fosu, 2013). In turn, poor performance led to increased
systemic risk to the financial architecture and the exposure of structural weakness in
supervision and prudential policies (Dahou, Omar & Pfister, 2009; Allen & Giovannetti, 2011;
Čihák, Demirgüç-Kunt, Pería & Mohseni-Cheraghlou, 2012). Following from the concept of
information asymmetry (Mishkin, 2013), in which banks faced problems of adverse selection
and moral hazard in their lending activity, the risk to small depositors, for instance, increased
in the wake of financial crises (Stiglitz, 1994; Mishkin, 2013). This risk materialised in the
form of sequential service constraints (Andolfatto & Nosal, 2008), which created an incentive
for depositors to seek verification of the solvency of banks (Diamond & Dybvig, 1983). In the
interests of depositors, global recalibration of prudential policies followed with enactment of
the Volcker Rule3 (Whitehead, 2011; Thakor, 2012).
1 Notwithstanding the benefits of competition, aggressive conduct by banks or excessive risk-taking can have
several negative implications, such as: (1) creation of asset-price bubbles, (2) artificial credit growth and
proliferation of opaque financial products, (3) deterioration of asset quality, (4) excess leverage and build-up of
systemic risk to the financial architecture, and (5) triggering de-marketing campaigns (Boot & Thakor, 1997;
Claessens, Laeven, Igan & Dell'Ariccia, 2010; Sanusi, 2012).
2 A blueprint of economic structural adjustment programmes.
3 Volcker Rule was enacted in 2010 to counter the negative effects universal banking introduced by the Gramm-
Leach-Bliley Act.
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Lastly, a motivating factor for this study was that the existing literature on bank competition in
developing countries remains crucially limited, thus presenting an opportunity to provide
additional insights. Moreover, a significant limitation of the literature is that it largely covers
single country analyses (Buchs & Mathisen, 2005; Hauner & Peiris, 2005; Greenberg &
Simbanegavi, 2009; Biekpe, 2011; Simpasa, 2011; Simbanegavi et al., 2012, Osuagwu &
Nwokoma, 2017), as opposed to cross-country evidence, which remains limited (Sanya &
Gaertner, 2012; Fosu, 2013). This limitation results in an omission of the effect of regional
integration, trade, interdependence of intermediation and harmonisation of regulation and
monetary policy (Claessens & Laeven, 2003; Bikker, Shaffer & Spierdijk, 2009; Sanya &
Gaertner, 2012; Fosu, 2013). However, authors have often indicated that insufficient data
availability is a critical reason for the scant literature (Claessens & Laeven, 2003, 2005; Sanya
& Gaertner, 2012; Fosu, 2013).
The objectives of this study were in two parts as follows:
i. Measuring banking competition
The first objective was to examine changes in banking competition in Sub-Saharan Africa, in
a period of significant banking and economic reform that also coincided with the onset of the
2007/2008 global financial crisis. Specifically, the study sought to examine, from a non-
structural view, pricing behaviour of banks by applying the Panzar-Rosse model to compute a
continuous measure of a static H-statistic. The computed value would therefore suggest the
extent of contestability of markets. Further, the research assignment sought to validate the H-
statistic by computing an E-statistic, which explains a state of general market equilibrium as a
pre-condition to measuring banking competition.
ii. Adding insights to existing literature
Apart from empirical testing, another objective of this study is to contribute new insights to the
current debate on competition and its impact on financial sector development strategy
(Claessens & Laeven, 2003). To the best of the authors’ knowledge, prior studies on banking
competition remain limited (Claessens & Laeven, 2003; Buchs & Mathisen, 2005; Hauner &
Peiris, 2005; Bikker et al., 2009; Greenberg & Simbanegavi, 2009; Schaeck, Čihák & Wolfe,
2009; Simpasa, 2011; Sanya & Gaertner, 2012; Simbanegavi et al., 2012; Fosu, 2013, and
Osuagwu and Nwokoma 2017), by comparison with developed economies. Moreover,
reviewed studies do not adequately capture the post 2007/2008 financial crisis in which
significant implementation of reform/liberalisation and prudential policies influenced
competitive behaviour.
2.1 Literature Review
This section provides a theoretical background to the various pricing strategies of firms that
influence competitive behaviour. A review of relevant empirical literature, was also
undertaken.
2.2.1 Structural approach to measuring competition
The structural approach assumes a causal relationship between market structure and
performance (Bikker & Haaf, 2002) and consists of two competing hypotheses—Structure-
Conduct-Performance (SCP) paradigm and Efficient Structure Hypothesis. The SCP paradigm,
according to Bain and Qualls (1968: 381), posits that high concentration in an industry weakens
the degree of competition and encourages collusive behaviour by organisations. In turn,
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collusive behaviour leads to abnormal profit at the expense of efficiency. Popular empirical
tests that support the SCP paradigm are the Herfindahl-Hirschman Index (HHI) and firm
concentration ratio (CRn).
The Efficient Structure Hypothesis (ESH), originally proposed by Demsetz (1973), indicates
that a larger market share for an individual bank that leads to high industry concentration is the
result of efficiency and lower input costs, as opposed to a low degree of competition. The
hypothesis further argues that the SCP paradigm ignores barriers to entry and exit in an industry
such as economic, legal and technological barriers. In addition, it argues that the SCP paradigm
does not account for firm efficiency, such as superior staff, technology and production
efficiency. Therefore, an efficient bank will, over time, increase market share, market power
and ultimately drive superior performance relative to competitors.
2.2.2 Non-structural approach to measuring competition
The non-structural approach, on the other hand, is a modern view, in the manner of the New
Empirical Industrial Organisation (NEIO) theories. The approach suggests that changes in
input costs influence pricing behaviour and ultimately performance of banks. This ultimately
leads to prices being set equal to marginal costs (Iwata, 1974; Bresnahan, 1982; Panzar &
Rosse, 1987; Bikker & Haaf, 2002). In addition, the non-structural approach argues against the
notion of a causal relationship between market structure and performance. Popular empirical
tests are the Panzar-Rosse model (1982, 1987) and Bresnahan model (1982).
2.2.2.1 The Panzar-Rosse Model
The Panzar-Rosse model, primarily assumes that the conduct of competing banks influences
performance of any individual bank. The idea is that banks employ dissimilar pricing strategies
as they respond to changes in factor input prices; and competitiveness therefore is the extent to
which changes in input prices reflect in revenues in a state of equilibrium (Panzar-Rosse, 1987;
Bikker & Haaf, 2002). Assumptions of the model include a single product output and profit
maximisation, where marginal revenue is set equal to marginal cost. Using data at firm level,
the test derives a measure called an H-statistic, which is a summation of the elasticities of
revenue with respect to changes in input prices. In other words, an H-statistic is a continuous
measure of the level of competition (Bikker & Haaf, 2002).
At one end of the measurement, an H-statistic is less than or equal to zero, which implies
monopoly or oligopoly pricing behaviour. At the other end, it is equal to unity, which implies
perfect competition. Results for an H-statistic which lie between zero and unity imply varying
degrees of monopolistic pricing behaviour. Panzar and Rosse (1987) show that for a profit-
maximising monopolist, an H-statistic cannot be positive, since an increase in input raises
marginal cost. This they say lead to an output restriction and therefore lower revenues. For
perfect competition where an H-statistic is equal to unity, individual firms incur an increase in
marginal and average costs without altering equilibrium output.
Therefore, deriving an H-statistic follows:
0t,w,xCzn,,xR iii
'
iii
'
i (3.1)
where '
i
R and '
i
C are marginal revenue and marginal cost of bank i. Output is xi. The number
of banks is n. A vector of input prices is wi. Vectors of exogenous variables that shift the
revenue and cost functions are zi and ti. Long-run equilibrium imposes a zero-profit constraint
at market level.
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0t,w,xCzn,,xR iii
*
iii
*
i (3.2)
where variables with an asterisk are in long-run equilibrium.
*
i
ki
m
1k ki
*
i
R
w
δw
δR
H
(3.3)
where the derivative of total revenue is
ki
*
i
δw
δR , based on the price of the th
input.
In forming comparative static properties of a reduced-form revenue equation, the Panzar-Rosse
model initially utilised cross-sectional data, although in later studies panel data estimations
became prevalent as cross-country evidence emerged. Despite popularity among authors,
criticism levelled against the Panzar-Rosse model suggests overestimation of results in static
equilibrium form, due to the use of price equations, as opposed to unscaled revenue (Bikker et
al., 2009). Employing a gradual or dynamic approach to equilibrium therefore tackles this
limitation and provides a model robustness check (Goddard & Wilson, 2009).
2.2.2.2 The Bresnahan Model
The Bresnahan model was not applied in this study, but primarily differs from the Panzar-Rosse
model in that it assumes a two-product bank, for example loans and deposits; whereas the
Panzar-Rosse approach considers one. Other assumptions, nonetheless, are consistent with the
Panzar-Rosse model, such as general market equilibrium condition and profit maximisation.
Notably, banks set price P and quantity Q, such that marginal cost MC is equal to ‘perceived’
marginal revenue MRP. In a competitive market, MRP = P and in a monopoly, MRP = MR.
Where there is monopolistic competition, MRP < P. Importantly, bank customers are
considered price takers, where P = MC. Therefore, the model estimates a parameter that ranges
from perfect competition to monopoly power. Further, the model typically applies aggregate
data as opposed to firm-level data.
2.3 Studies on Sub-Saharan Africa
In sub-Saharan Africa, a few country-specific studies (Buchs & Mathisen, 2005; Hauner &
Peiris, 2005; Greenberg & Simbanegavi, 2009; Biekpe, 2011; Simpasa, 2011; Simbanegavi et
al., 2012, Osuagwu and Nwokoma, 2017), in varying periods between 1998 and 2014, were
conducted in Ghana, South Africa, Tanzania, Uganda and Nigeria. The various empirical
studies tested for changes in competitive conduct by applying the Panzar-Rosse model, and all
of them determined monopolistic competition. Tests for general market equilibrium validated
the outcomes. Importantly, the studies highlighted the impact of reform/liberalisation and
prudential policies on the conduct of banks following periods of financial repression.
Sanya and Gaertner (2012) examined the determinants of competition in the East African
region by conducting an empirical test using the Panzar-Rosse model in a sample of four out
of the five East African Community (EAC) members in the years 2001-2008. Their measured
H-statistic indicated monopolistic competition, but they found Kenyan banks to be the most
competitive and Rwandan banks the least.
Findings from their empirical test suggested that low levels of intermediation, a lack of access
to financial services and inefficiencies in banking systems in East Africa were associated with
limited competition. However, reform/liberalisation and prudential policies introduced in the
early 2000s contributed to a rapid increase in credit to the private sector. Policy implementation
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included restructuring and privatisation of state-owned banks, write-off of non-performing
loans, fostering good governance, easing of barriers to entry and tightening of supervision.
Importantly, the entry of foreign banks into the region was significant, and helped to improve
bank sophistication, but state-owned banks retained a very large market share.
Lastly, Fosu (2013) completed a first-ever attempt at broadly assessing bank competition in all
the sub-regions of Africa. The study employed the Panzar-Rosse model in a sample of 38
African countries in the period 2002-2009. A test for general market equilibrium validated the
results. Overall, the study measured the prevalence of monopolistic competition in Africa,
linked to the implementation of reform/liberalisation and prudential policies undertaken to
undo critical underperformance of banking systems.
Fosu (2013) described a continent in which banking systems had been previously plagued by
controls on interest rates and credit allocation, poor asset quality, as well as dominance by
state-run banks. However, deregulation had resulted in an increase in regional integration,
recapitalisation, entry of foreign banks, cross-border competition, advances in technology,
restructuring and privatisation of state-run banks, and write-off of non-performing loans. This
resulted in an improvement in the allocative efficiency of credit to the private sector.
Importantly, Fosu (2013) highlighted that generating interest income was an influencing factor
in competitive conduct, since most banks in Africa relied on lending activity as a key source
of revenue. This study build on Fosu (2013) to look at a period that sufficiently encapsulated
the effect of changes to the bank operating environment in sub-Saharan Africa since the onset
of the 2007/2008 global financial crisis.
2.4 Development of hypotheses
Bikker and Haaf (2002) clearly indicated that the competitive structure of a banking system
could change over time due to the process of reform/liberalisation and deregulation. From that
view, a calculated H-statistic explains pricing behaviour of firms. By measuring the sum of
elasticities of revenue to factor input prices, an H-statistic equates to unity where there is perfect
competition/contestability, implying that an increase in factor prices would not alter bank
output. A calculated result, which is less than unity but above zero, would suggest an alternative
view of monopolistic competition or partial contestability, which implies that changes in factor
input prices affect bank pricing of output. Therefore, the hypothesis follows:
H1. There is a statistically significant positive association between unit factor input prices
(market power) for a bank and the extent of competitive behaviour.
Following from the empirical theory on banking competition, in the manner of the NEIO
framework (Iwata, 1974; Bresnahan, 1982; Lau, 1982; Panzar & Rosse, 1987; Roeger, 1995),
the validity of the competition parameter is sufficient only when the banking system is
observed in general market equilibrium (Bikker & Haaf, 2002; Brooks, 2013: 495). As such,
an E-statistic, calculated to explain that general market equilibrium exists where the factor
input prices of funds, labour and capital expenditure do not influence banking returns, is
statistically equivalent to zero. The hypothesis therefore follows that:
H2. Market equilibrium exists where returns on bank assets are not associated with factor input
prices.
3.1 Research Methodology
This study applied the Panzar-Rosse approach to measuring banking competition in sub-
Saharan Africa, in light of the 2007/2008 global financial crisis.
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3.2 The Population and Sample
This study drew a sample of banks from a selection of six countries, which represented three
sub-regions in Sub-Saharan Africa, namely East Africa (Kenya and Uganda), Southern African
(Mauritius and South Africa) and West Africa (Ghana and Nigeria). Specifically, the selection
criteria for the sample anchored on data availability, economic and financial development,
advanced legal frameworks, good corporate governance and disclosure, and use of IFRS
reporting standards. From the selected countries, 83 banks (cross-sections) were included in
the sample out of a possible 152 (see Table 1), which was most representative of banks in Sub-
Saharan Africa.
Table 1: Banks population and sample
Country Population Sample Representation
Ghana 27 16 59
Kenya 42 23 55
Mauritius 21 10 48
Nigeria 21 14 67
South Africa 16 11 69
Uganda 25 9 36
Total 152 83 55
Notes: Representation refers to sample as a percentage of population.
Source: Computed by the Authors
Excluded from the sample were banks with missing data in at least three of the time series
periods. This was mainly because data for several banks was not available from data sources.
The selected years (2008-2013) represented a period that sufficiently encapsulated the effect
of changes to the operating environment in sub-Saharan Africa since the onset of the 2007/2008
global financial crisis. As a result, the data panel was unbalanced. Consequently, possible
observations were 498, but 481 were included in the empirical estimation.
3.3 Data Sources and Collection
Banks data were mainly sourced from a public database called African Financials. A significant
number of annual financial statements for banks in Sub-Saharan Africa were contained on the
platform. For a few other banks, the financial statements pages of bank-specific websites were
used. We utilised only parent or in-country bank financial statements, as opposed to
consolidated accounts. This satisfied a common-effect specification and reduced bias from
multi-country representation (Bikker & Haaf, 2002; Sanya & Gaertner, 2012). Countries with
significant multi-country bias were Kenya, Nigeria and South Africa (Claessens & Van Horen,
2011; Sanya & Gaertner, 2012). Data on macroeconomic variables were sourced from the
World Bank.
3.4 Definition of Variables and Model Specification
Table 2 below defines the variables used in the analysis.
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Table 2: Summary of definitions and operationalisation of variables (All in natural log)
Dependent variables – revenue and general market equilibrium functions
REV Ratio of interest income to total assets. It represents a scaled revenue function, which is explained
by changes in unit input prices.
ROA Ratio of net income to total assets. It is regressed to validate the Panzar-Rosse model in general
market equilibrium.
Independent variables – unit factor input prices that influenced the output pricing behaviour of banks
PF Unit price of funds or average cost of funds is the ratio of interest expense to total customer
deposits.
PL Unit price of labour is the ratio of staff expenses to total assets.
PK Unit price of capital is the ratio of other operating expenses to fixed assets. It represents the level
of capital expenditure.
Bank-specific control variables – controlled for differences in business mix, activity and scale
RISKASS Ratio of loan loss provisions to total assets. It represents the level of conservatism of
management in lending activity.
ASSET USD value of balance sheet. It controls for scale.
CREDIT Ratio of total loans to total assets. This is a representation of lending activity, which is a key
source of income for banks.
EQUITY Ratio of shareholder equity to total assets. It controls for leverage.
Country-specific variables – macroeconomic controls which were time variant but fixed per country
GROWTH Annual GDP growth rate. It captures changes in national income, which is related to performance
of banks.
INFL Annual rate of inflation controls for the effect of changes in prices.
Source: Authors compilation.
3.4.1 Specifying the Panzar-Rosse equation for H1
In a sense, the Panzar-Rosse model was a contestability regression (Brooks, 2013: 495), which
tested whether a change in input prices influenced the output of banks or not. Under such
influence, the observation indicated a state of monopolistic competition. Without influence, the
observation indicated perfect competition. Therefore, assuming the hypothesised relations
were linear, the main fixed-effects regression estimated in this study are:
lnREVit= α+ β1lnPFit+ β2lnPLit+ β3lnPKit+ γ1lnRISKASSit2lnASSETit+ γ3lnCREDITit+
γ4lnEQUITYit+ δ1lnGROWTHt+ δ2lnINFLt+ φD2+ φD3 ++φD6it , (i)
Alternatively,
lnREVit= α+ β1lnPFit+ β2lnPLit+ β3lnPKit+ γ1lnRISKASSit2lnASSETit+ γ3lnCREDITit+
γ4lnEQUITYit+ δ1lnGROWTHt+ δ2lnINFLtitit , (ii)
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where subscripts i and t, denoted bank i at year t.
This study therefore estimated a static version of an H-statistic for the specified equation i,
using a panel fixed-effects4 approach or least squares dummy variables (LSDV) method. In
essence, the fixed-effects approach controlled for heterogeneity at bank-specific level (Fosu,
2013). Further, according to the literature, a fixed-effects model allowed the intercept in the
equation to vary cross-sectionally, but not over time (Brooks, 2013: 490), where α =αi
(Claessens & Laeven, 2003). Appendix C provides details of the diagnostic tests (Hausman
test and Wald test) for the chosen model. An H-statistic, therefore, equated to the sum of the
coefficients of the unit factor prices of funds, labour and capital expenditure (Molyneux et al.,
1994; Bikker & Haaf, 2002; Claessens & Laeven, 2003). This was denoted as follows:
H = β1+ β2 + β3 (iii)
where β1 was the coefficient of price of funds, β2 was the coefficient of price of labour and β3
was the coefficient of capital expenditure.
3.4.2 Specifying the Panzar-Rosse equation for H2
Since the literature specified that conditions observed in general market equilibrium validated
the Panzar-Rosse model (Panzar & Rosse, 1987, Molyneux et al., 1994; Bikker & Haaf, 2002;
Claessens & Laeven, 2003), some minor re-specification of the model to represent the natural
log of the dependent variable ROA altered the equation as follows:
lnROAit= α+ β1lnPFit+ β2lnPLit+ β3lnPKit+ γ1lnRISKASSit2lnASSETit+ γ3lnCREDITit+
γ4lnEQUITYit+ δ1lnGROWTHt+ δ2lnINFLt+ φD2+ φD3 ++φD6it , (iv)
Alternatively,
lnROAit= α+ β1lnPFit+ β2lnPLit+ β3lnPKit+ γ1lnRISKASSit2lnASSETit+ γ3lnCREDITit+
γ4lnEQUITYit+ δ1lnGROWTHt+ δ2lnINFLtitit , (v)
where the dependent variable was specified as ROAˊ = ln(1+ROA), in order to adjust for negative values
of net income (Claessens & Laeven, 2003).
3.4.3 Defining parameters of an E-statistic
This study computed an E-statistic to test for equilibrium using a Wald test F-statistic. In this
case, the assumption was that the sum of the coefficients of the factor prices of funds, labour
and capital expenditure were statistically equivalent to zero. As such, the null was formulated
as E = zero; and it followed that if not rejected, the market was sufficiently in a state of long-
run equilibrium. Notation of an E-statistic followed:
β1+ β2 + β3 = 0 (vi)
4 This study applied the Hausman test to choose a specification between random- and fixed effects. The null
hypothesis of random-effect was rejected at the five percent level of significance. A further specification test using
the Wald test rejected null that pooled OLS is appropriate, and did not reject the fixed-effect or least squared
dummy variables (LSDV) specification.
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4.1 Findings
This section presents a review of descriptive statistics and an analysis of the correlation matrix.
A detailed presentation on findings from hypotheses testing then follows.
4.2 Review of Descriptive Statistics
Table 3 presents a summary of descriptive statistics of all dependent and independent variables
for the period 2008-2013. Importantly, the impact of the 2007/2008 financial crisis was
captured during this period, and a number of interesting findings emerged. First, in all periods
observed, 2009 appeared to have been a relatively more challenging year as the effects of the
crisis were still evolving.
4.2.1 Dependent variables
In 2009, REV increased to its highest level of 14.53 percent from 11.82 percent in 2008,
intuitively suggesting that banks’ pricing behaviour altered to reflect increasing risk to lending
activity in light of the financial crisis. Countries that experienced trade vulnerabilities (Berman
& Martin, 2012), mainly due to declining commodity prices (Allen et al., 2011), would have
likely reacted to a deterioration in loan quality by raising interest rates. As expected, bank
performance was negatively affected by changes in the operating environment (IMF, 2011),
resulting in a mean ROA of 1.41 percent for 2009, which was the lowest over the observed
period, as some banks reported losses.
4.2.2 Independent variables
Not surprisingly, PF increased to its maximum of 10.74 percent in 2009, reflecting rising cost
of funds associated with the general loss of depositor confidence, capital flight, low liquidity
and increased leverage (Arieff, 2010; Allen et al., 2011). Years later, however, PF declined as
regulators tightened prudential policy and directed banks to increase capitalisation levels. In
the wake of the financial crisis, banks sought to improve the quality of risk management and
general operations, which resulted in an increase in the wage rate as skilled talent was acquired.
For instance, the median wage rate in 2009 was the highest ever for the observed period,
highlighting the short-term pressures faced by banks. Nevertheless, as alternative methods of
distribution such as branchless banking (Klein & Mayer, 2011) evolved over the past few years,
pressure on the wage rate diminished. Banks, instead, focused on upgrading information
technology infrastructure, driving PK to its highest levels in 2010.
4.2.3 Bank-specific control variables
Despite the negative effect of the financial crisis on asset quality in many banking systems in
Sub-Saharan Africa, a bank-specific control, RISKASS, did not rise significantly. The average
was 3.41 percent in 2008, but declined to 1.89 percent in 2011. Intuitively, this implied that
some banking systems where insulated from the impact of the crisis, or had sufficiently strong
risk management infrastructure to avert a banking crisis. Further, other banking systems
resorted to either writing off or selling non-performing loans. For example, regulatory
intervention in Nigeria led to the establishment of AMCON in 2010, and a subsequent disposal
of over NGN1.5 trillion of non-performing loans by the banking sector (AMCON, 2012). As
expected, the bulk of non-performing loans purchased where related to the oil and gas sector
(27%), trade finance (19%) and capital markets (18%). Nevertheless, the sell-off resulted in
most banks reducing their NPL ratios to below the regulatory stipulated five percent (Sanusi,
2012), which was a key driver in mitigating against a rise in RISKASS and improving liquidity.
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Interestingly, lending activity as measured by CREDIT remained relatively stable, with a mean
of 50.60 percent over the observed period. The extent of financial intermediation in banking
systems in Sub-Saharan Africa pre- and post-crisis was constrained due to a number of reasons,
such as poor credit information, a lack of collateral and financial illiteracy, such that banks
traditionally preferred to lend to larger and less-risky corporate customers or alternatively held
cash and government securities. Therefore, and as expected, there was no significant level of
deleveraging that occurred post-crisis.
However, in view of the crisis regulators took precaution by instigating a recapitalisation of
banking systems across the sub-continent (Čihák et al., 2012). In essence, average EQUITY
increased to 15.01 percent in 2010, and again increased to 15.11 percent in 2013, as profitability
of banks improved. For un-scaled balance sheets, the standard deviation of ASSET was
substantially high, owing to the enormous scale of South African banks relative to other banks
in the representative sample. However, average ASSET reflected a steady increase over the
observed period, from USD 4.50 trillion to USD 6.32 trillion.
4.2.4 Country-specific control variables
GROWTH also followed a positive trend, from 4.24 percent in 2009 to 6.40 percent in 2011,
due to a recovery in commodity prices, additional resource discoveries and exploitation, as well
as rising domestic demand (IMF, 2012). However, growth rates tapered off in 2012 and 2013
as uncertainties over stability in the global economy increased, and risks of faltering demand
from emerging economies such as China materialised (IMF, 2014). Average inflation,
however, did not indicate any trend, owing to differences in pass-through to domestic prices
from associated local currency depreciation. In 2008, as commodity prices peaked (IMF, 2009;
Allen et al., 2011), average inflation for the representative sample reached a high of 14.61
percent, but subsequently declined to 6.59 percent in 2010.
Table 3: Summary descriptive statistics (percentage, unless otherwise stated)
Variables Measur
e 2008 2009 2010 2011 2012 2013 All
REV Mean 9.829 10.323 8.866 8.696 9.836 9.593 9.524
Median 10.525 10.182 8.775 8.740 10.362 9.977 10.079
STD 0.020 0.027 0.025 0.025 0.031 0.028 0.026
Min 6.439 5.413 4.352 4.573 4.356 4.703 4.352
Max 11.824 14.532 12.789 12.266 13.616 13.106 14.532
ROA Mean 2.041 1.408 2.216 1.712 2.176 2.187 1.957
Median 1.791 1.198 2.403 1.724 2.053 1.973 1.882
STD 0.011 0.012 0.008 0.013 0.008 0.010 0.010
Min 0.665 -0.642 1.043 -0.663 1.216 1.258 -0.663
Max 4.016 3.435 3.032 3.517 3.303 3.711 4.016
PF Mean 5.930 6.210 4.826 4.359 5.387 5.139 5.309
Median 5.535 5.879 4.475 4.224 5.278 5.331 5.305
STD 0.014 0.023 0.017 0.010 0.018 0.016 0.016
Min 4.237 3.538 2.632 2.761 2.572 2.630 2.572
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Max 7.763 10.735 8.338 5.776 8.045 7.559 10.735
PL Mean 2.692 2.859 2.571 2.485 2.475 2.395 2.579
Median 2.900 3.107 2.763 2.651 2.596 2.521 2.707
STD 0.008 0.009 0.007 0.007 0.008 0.008 0.008
Min 1.121 1.041 0.989 1.002 0.886 0.934 0.886
Max 3.415 3.821 3.247 3.373 3.416 3.407 3.821
PK Mean 142.024 152.060 216.405 187.613 183.472 178.887 176.743
Median 133.189 134.596 151.517 145.338 169.733 157.370 148.428
STD 0.517 0.553 1.317 1.065 0.776 0.604 0.805
Min 78.562 90.574 93.407 88.962 103.179 118.372 78.562
Max 240.921 264.995 452.526 408.562 341.490 285.799 452.526
RISKASS Mean 3.406 3.201 2.385 1.880 1.965 2.063 2.484
Median 2.112 2.219 2.052 1.780 1.792 1.734 1.922
STD 0.031 0.027 0.014 0.006 0.007 0.008 0.015
Min 1.424 1.265 0.780 1.120 1.427 1.453 0.780
Max 10.145 9.065 5.181 3.040 3.360 3.918 10.145
ASSET Mean 4 674 4 502 4 666 5 214 5 744 6 319 5 186
Median 998 1 011 1 088 1 140 1 312 1 488 1 114
STD 7 585 7 207 7 382 8 008 8 552 9 126 7 977
Min 163 205 253 302 347 526 163
Max 21 325 20 312 20 854 22 619 24 196 25 888 25 888
Total 321 419 313 092 328 038 370 604 410 640 452 011 365 967
CREDIT Mean 48.685 50.448 50.068 51.487 51.320 51.586 50.599
Median 49.692 49.860 49.744 54.044 51.510 49.722 49.802
STD 0.073 0.052 0.061 0.083 0.072 0.064 0.068
Min 33.877 42.734 43.062 38.362 38.573 41.468 33.877
Max 57.127 58.138 61.118 62.014 61.890 60.947 62.014
Source: Computed by the Authors.
Summary descriptive statistics (continued)
Variables Measur
e 2008 2009 2010 2011 2012 2013 All
EQUITY Mean 14.911 14.445 15.013 14.269 14.675 15.112 14.737
Median 15.228 15.354 14.933 14.340 14.997 15.119 15.058
STD 0.034 0.035 0.020 0.017 0.019 0.027 0.025
Min 9.805 8.895 11.805 11.458 11.605 11.112 8.895
Max 18.878 18.126 18.010 17.155 17.530 19.606 19.606
GROWTH Mean 5.678 4.241 5.794 6.397 4.454 4.683 5.208
Median 5.891 3.499 5.832 4.654 3.845 5.041 4.847
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STD 0.025 0.021 0.018 0.040 0.021 0.017 0.024
Min 1.527 1.526 3.140 3.599 2.467 1.891 1.526
Max 8.709 7.251 8.007 15.007 8.790 7.132 15.007
INFL Mean 14.610 10.453 6.587 10.682 9.006 6.753 9.682
Median 11.814 10.386 4.119 9.784 9.270 5.712 9.527
STD 0.056 0.052 0.041 0.046 0.035 0.026 0.043
Min 9.733 2.550 2.893 5.280 3.852 3.543 2.550
Max 26.240 19.251 13.720 18.693 14.016 11.608 26.240
Source: Computed by the Authors.
4.3 Analysis of Correlation Matrix
The correlation matrix presented in Table 4 mainly indicates positive signs for the coefficients.
There was a statistically significant positive association between REV and the main
independent variables PF and PL, with correlation coefficients of 0.62 and 0.64 respectively.
PK, on the other hand, had a negative sign, but the relationship with REV was statistically weak
as the coefficient was only 0.10. RISKASS and CREDIT had similar correlation coefficients
with REV, of approximately 0.40 respectively, indicating a positive relationship between
lending activity and risk management in the determination of interest rates. This can also be
explained by the notion that the higher the risk associated with granting each loan, the higher
the lending rate (Brooks, 2013: 498).
Likewise, the association of RISKASS and CREDIT to PF was also relatively strong, indicating
that cost of funds played a role in determining the quantity of risk and extent of lending activity
assumed by each individual bank. Further, the correlation coefficient between RISKASS and
CREDIT was positive with a correlation coefficient of 0.60. Notably, the correlation coefficient
between PL and RISKASS of 0.41 indicated that bank wage rates were partly influenced by the
acquisition of specialised talent in risk management in the wake of the financial crisis. PL and
INFL had a coefficient of 0.39, but the positive sign highlighted the effect of inflationary
pressures on the wage rate.
Lastly, the relationship between ASSET and GROWTH had a correlation coefficient of 0.49
and, significantly, the positive sign was associated with the notion of a causal link between
financial sector development and economic growth (King & Levine, 1993; Claessens &
Laeven, 2003).
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Table 4: Correlation matrix for the variables for all 481 observations
REV PF PL PK RISKAS
S ASSET CREDIT EQUITY GROWTH INFL
REV 0.624 0.638 -0.095 0.394 0.134 0.406 0.237 0.248 0.443
PF 0.624 0.164 -0.228 0.471 0.195 0.597 -0.109 0.162 0.278
PL 0.638 0.164 0.056 0.405 0.115 0.185 0.248 0.180 0.389
PK -0.095 -0.228 0.056 -0.308 -0.166 -0.325 -0.003 -0.158 -0.050
RISKAS
S 0.394 0.471 0.405 -0.308 0.288 0.600 -0.075 0.170 0.197
ASSET 0.134 0.195 0.115 -0.166 0.288 0.021 -0.114 0.490 0.300
CREDIT 0.406 0.597 0.185 -0.325 0.600 0.021 -0.220 -0.033 0.077
EQUITY 0.237 -0.109 0.248 -0.003 -0.075 -0.114 -0.220 0.105 0.158
GROWT
H 0.248 0.162 0.180 -0.158 0.170 0.490 -0.033 0.105 0.118
INFL 0.443 0.278 0.389 -0.050 0.197 0.300 0.077 0.158 0.118
Note: Variables taken in natural log.
Source: Authors Compilation.
4.4 Empirical Results and Discussion
This section provides detailed findings of the empirical tests of the H1 and H2. In the first
instance, the outcome of testing for H2 indicated sufficient conditions of market equilibrium.
Thereafter, H1 was regressed and the conditions of monopolistic competition identified were
consistent with previous studies.
4.4.1 General market equilibrium analysis
H2 was tested for a sample of 83 banks in Sub-Saharan Africa, that banking competition exists
under conditions of general or long run market equilibrium. Following literature, this could
also be stated as ROA is not influenced by factor input prices in the long run (Molyneux et al.,
1996), such that a computed E-statistic = zero. The empirical test, presented in detail in
Appendix D and summarised in Table 5 below, was conducted using a Wald test. The outcome
was that the computed value of the E-statistic was 0.003, with a p-value of 0.407. Therefore,
the null hypothesis that unit factor input prices were equivalent to zero, was not rejected. The
implication, therefore, was that banking competition was observed in a state of general
equilibrium.
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Table 5: Test of banking market equilibrium with LSDV model
Independent variables Dependent variable: lnROA Probability
lnPF -0.001 0.470
lnPL -0.005* 0.059
lnPK 0.003* 0.069
lnRISKASS -0.001 0.324
lnASSET 0.005*** 0.000
lnCREDIT 0.003 0.130
lnEQUITY 0.013*** 0.000
lnGROWTH 0.008** 0.018
lnINFL 0.004 0.164
D2 -0.053*** 0.000
D3 -0.065*** 0.000
D4 -0.007* 0.063
D5 -0.017*** 0.004
D6 -0.020*** 0.000
Intercept 0.031** 0.048
Number of observations / period 481 2008-2013
R2 (adjusted) - % 22.3
H0: E=0 0.003 0.407
Decision Do not reject null hypothesis; Evidence of long-run equilibrium
exists
Note: *, ** and *** denote significance at 10, 5, and 1 percent respectively.
Source: Authors Compilation.
The results of the empirical test for H2 were consistent with findings from previous studies,
where, in the long run, factor input prices were not having any influence on bank returns. These
findings were also consistent with the theoretical literature, which states that, in equilibrium,
the zero profit constraint holds constant at market level (Shaffer, 1982; Molyneux et al., 1996;
Bikker & Haaf, 2002).
4.4.2 Test of competition using the Panzar-Rosse approach
Having satisfied conditions of general market equilibrium, H1 was tested for the same sample
of 83 banks in sub-Saharan Africa, that there is statistically significant relationship between
changes in market conditions/power and the extent of competitive conduct. Specifically, a
regression of the Panzar-Rosse model, using a fixed-effects method, was used to compute a
continuous measure of an H-statistic that had a value of 0.57 (see Table 6 below). The results
were validated by a Wald test, which confirmed that the H-statistic was significantly different
from both unity and zero at the one percent level of significance. The findings therefore
suggested that the banking system is characterised by monopolistic competition, as opposed to
perfect competition or pure monopoly.
Also, the use of a fixed-effects method was validated by conducting a Hausman test (random-
effects versus fixed-effects) and also a Wald test that dummy variables were jointly zero. Both
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null hypotheses that the random-effects model was appropriate and dummy variables were
equal to zero, were strongly rejected, allowing for heterogeneity among the banks (Bikker &
Haaf, 2002; Claessens & Laeven, 2003; Brooks, 2013: 496; Fosu, 2013).
Table 6: Test of banking competition with LSDV model
Independent variables Dependent variable: lnREV Probability
lnPF 0.289*** 0.000
lnPL 0.295*** 0.000
lnPK -0.014 0.392
lnRISKASS -0.020 0.120
lnASSET 0.008 0.299
lnCREDIT 0.020 0.295
lnEQUITY 0.118*** 0.000
lnGROWTH 0.019 0.555
lnINFL 0.034 0.190
D2 -0.133* 0.092
D3 -0.299*** 0.000
D4 -0.111*** 0.008
D5 -0.354*** 0.000
D6 -0.183*** 0.002
Intercept 0.028 0.866
Number of observations / period 481 2008 - 2013
R2 (adjusted) - % 22.3 75.1
H-statistic 0.570
H0: H=0 (pure monopoly) -0.430 0.000
H0: H=1 (perfect competition) 0.570 0.000
Decision Reject both null hypotheses; evidence of monopolistic competition
Note: *, ** and *** denote significance at 10, 5, and 1 percent respectively.
Source: Authors Compilation.
Similarly, the results of monopolistic competition were consistent with outcomes of previous
studies, and consistent with theoretical literature, since changes in factor input prices (market
power) incurred by a specific bank influenced changes to its revenue. Under conditions of
perfect competition, on the one hand, an increase in input prices would have raised marginal
costs and total revenue by the same amount, where an H-statistic is equal to unity. Under pure
monopoly, on the other hand, marginal costs would have increased but equilibrium output
would have declined, such that H-statistic is less than zero (Claessens & Laeven, 2003).
4.4.3 Interpretation and discussion
Consistent with the outcome of monopolistic competition, Fosu (2013) suggested that
reform/liberalisation and prudential policies would have likely influenced pricing behaviour of
individual banks and market discipline. The findings, therefore, supported H1, that there is a
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statistically significant positive association between the effect of market conditions (banking
reform) and the extent of competitive behaviour.
The results of the LSDV model had a mixed outcome regarding the statistical significance of
independent variables in explaining differences in REV or pricing behaviour of banks (Bikker
& Haaf, 2002). Specifically, main independent variables PF and PL strongly influenced REV
at the one percent level of significance. Their coefficients carried positive signs, which
intuitively suggested the impact of liberalisation of interest rates (Fosu, 2013) on financial
intermediation, and the significance of talent acquisition for bank operations (Biekpe, 2011),
such as risk management and loan pricing. PK, on the other hand, carried a negative sign, but
not statistical significance.
Unexpected, bank-specific control variables largely carried no statistical significance
(RISKASS, ASSET and CREDIT), while RISKASS had a negative sign. EQUITY, however, had
a strong statistical significance, with a positive sign on the coefficient, implying that higher
capital levels led to strong pricing power. This was expected in view of the impact of prudential
policies which followed the onset of the 2007/2008 financial crisis, where a significant number
of banks in Sub-Saharan Africa increased capital via M&A activity, IPOs and/or capitalisation
of reserves. Further, the coefficients of macroeconomic control variables (GROWTH and
INFL) carried positive signs but were not statistically significant. Notably, the dummy
variables were all statistically significant, vindicating the application of a LSDV approach
(Brooks, 2013: 497).
5. Policy Implications
The findings of this research assignment had significant implications for policy design in
financial sector development strategy. This was mainly due to the linkage between the extent
of competition, technological advancement, efficiency of financial intermediation, access to
financial services, performance and stability (Sanya & Gaertner, 2012; Fosu, 2013).
First, from the findings it was evident that reform/liberalisation and prudential policies had a
bearing on the revenue and cost functions of banking systems (Bikker & Haaf, 2002, Fosu,
2013). As such, market players would likely continue to alter their conduct to ensure profit
maximisation. For example, interest rate liberalisation could drive higher cost of funds as banks
sought to compete for market share of deposits. In turn, banks could increase lending rates by
a higher proportion to expand net interest margin. Second, policy-driven recapitalisation of
banks resulted in increased market power in pricing of loan output.
Third, higher contestability of markets, owing to unrestricted/universal banking approaches
and deregulation of formal barriers, had the implication of driving excessive risk-taking by
banks in order to defend or expand market share. For example, excessive risk-taking by banks
in Nigeria, via the provision of ill-fated margin loans (Sanusi, 2012), had significant
implications in triggering regulatory responses to the ensuing crisis. Fourth, foreign bank entry
also had implications for contestability of markets, as well as technological advancement,
recalibration of risk management frameworks and capital flow (Claessens & Van Horen, 2011).
In East Africa, for example, where formal regulatory barriers were largely withdrawn (Sanya
& Gaertner, 2012) as a part of policy design, there were a number of implications. These
included a rise in foreign bank participation, significant cross-border capital flows and strong
technological innovation, which ultimately enhanced competitive conduct of banks.
Fifth, policy implications on the provision of credit to the private sector indicated a trend
towards promoting access to finance, further enhancing competitive conduct and performance
among banks. Although findings in relation to lending activity were statistically insignificant,
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banks made significant progress in extending their market to low income households and
SMEs. Lastly, empirical testing and analysis of descriptive statistics found that policy helped
to foster the development of banking systems and efficiency of financial intermediation in the
real economy, which ultimately influenced economic growth.
6. Recommendations
In view of the implications highlighted above, policymakers should continue to develop and
promote policies geared towards the development of financial intermediation and improved
competitive conduct of banks in Sub-Saharan Africa. In the first instance, liberalisation of
interest rates should remain a pivotal tool for increased contestability of markets and
sustainable performance, while attracting new players into the market. Further, policy design
in modernisation of banking infrastructure via technological advancement in
branchless/alternative distribution should further ease contestability by alleviating wage rates.
Likewise, prioritising the development and modernisation of credit information bureaux and
legal systems should further reduce perceived high risk of lending, which currently inhibits
effective financial intermediation. Also, financial literacy programmes targeted at the
household and SMEs should foster a financially inclusive approach by banks. This is so,
because numerous banks operating on the sub-continent remain averse to extending their
markets beyond a traditional large corporates base. For example, a pilot program for the
provision of basic accounting techniques through open tutorials in East Africa (Equity Bank,
2013) has met with success as it has helped to reduce the perception of high risk attached to
small borrowers.
Lastly, enhancing contestability of markets by privatising state-run banks and promoting
regional integration should remain key policy objectives. This would ensure a level playing
field for existing competitors and present an opportunity for new investors (Sanya & Gaertner,
2012). In addition, new investment would expand and develop the credit industry, ultimately
driving real sector growth.
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APPENDIX A:
SUMMARY OF SAMPLE BANKS BY COUNTRY AND YEAR
Country 2008 2009 2010 2011 2012 2013
Ghana 16 16 16 16 16 16
Nigeria 14 14 14 14 14 14
Kenya 18 21 22 23 23 23
Uganda 7 8 9 9 9 6
South Africa 11 11 11 11 11 11
Mauritius 8 9 10 10 10 10
Source: Authors Compilation.
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Wald test
Null hypothesis: Pooled OLS is appropriate; dummy variables/fixed-effects are equal to zero
Hausman test
Null hypothesis: Random-effects model is appropriate
Alternative: Fixed-effects model is appropriate
Decision: Reject null hypothesis
Dependent variable: LNREV
Test summary Chi-Sq. statistic Chi-Sq. d.f. Probability
Cross-section random 42.669 9 0.000
Cross-section random effects test comparisons:
Variable Fixed Random Probability
LNPF 0.345 0.337 0.382
LNPL 0.264 0.302 0.161
LNPK 0.015 0.019 0.730
LNRISKASS 0.012 -0.015 0.000
LNASSET 0.049 0.000 0.017
LNCREDIT 0.106 0.031 0.041
LNEQUITY 0.024 0.069 0.000
GROWTH 0.0174 0.040 0.006
INFL 0.041 0.043 0.869
Cross-section random-effects test equation:
Method: Fixed-effects; Dependent variable: LNREV
Variable Coefficient Std. Error Probability
C -0.449 0.207 0.030
LNPF 0.345 0.021 0.000
LNPL 0.263 0.039 0.000
LNPK 0.015 0.021 0.476
LNRISKASS 0.012 0.015 0.423
LNASSET 0.049 0.021 0.021
LNCREDIT 0.106 0.043 0.015
LNEQUITY 0.024 0.026 0.363
GROWTH 0.017 0.023 0.445
INFL 0.041 0.020 0.036
R2 (adj.) 0.883 Probability (F-stat) 0.000
S.E of regression 0.151 Mean dependent var. -2.407
Sum squared residuals 8.820 S.D. dependent var. 0.441
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APPENDIX B: DIAGNOSTIC TEST FOR H1.
F-statistic 40.949 Durbin-Watson stat. 1.418
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APPENDIX C:
DIAGNOSTIC TEST FOR H2
Hausman test
Null hypothesis: Random-effects model is appropriate
Alternative: Fixed-effects model is appropriate
Decision: Reject null hypothesis
Dependent variable: LNROA
Test summary Chi-Sq. statistic Chi-Sq. d.f. Probability
Cross
-
section random
25.314
9
0.003
Cross-section random effects test comparisons:
Variable Fixed Random Probability
LNPF 0.000 -0.002 0.305
LNPL -0.005 -0.008 0.518
LNPK 0.000 0.002 0.308
LNRISKASS 0.000 -0.001 0.557
LNASSET 0.011 0.000 0.000
LNCREDIT -0.003 0.005 0.097
LNEQUITY 0.011 0.011 0.986
GROWTH 0.007 0.007 0.737
INFL 0.005 0.002 0.008
Cross-section random-effects test equation:
Method: Fixed-effects; Dependent variable: LNROA
Variable Coefficient Std. Error Probability
C -0.042 0.025 0.091
LNPF 0.000 0.002 0.903
LNPL -0.005 0.005 0.265
LNPK 0.000 0.002 0.984
LNRISKASS 0.000 0.002 0.818
LNASSET 0.011 0.003 0.000
LNCREDIT -0.003 0.005 0.620
LNEQUITY 0.011 0.003 0.000
GROWTH 0.007 0.003 0.006
INFL 0.005 0.002 0.021
R2 (adj.) 0.441 Probability (F-stat) 0.000
S.E of regression 0.018 Mean dependent var. 0.019
Sum squared residuals 0.126 S.D. dependent var. 0.024
F-statistic 5.154 Durbin-Watson stat. 2.141
Source: Authors Compilation.
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Wald test
Null hypothesis: Pooled OLS is appropriate; dummy variables/fixed-effects are equal to zero
Alternative: Panel least squares dummy variables model is appropriate
Decision: Reject null hypothesis
Dependent variable: LNROA
Test statistic Value d.f. Probability
F-statistic 17.701 (5, 466) 0.000
Chi-square 88.505 5 0.000
Coefficient Std. Error Probability
C(1) 0.031 0.016 0.048
C(2) -0.001 0.002 0.470
C(3) -0.005 0.002 0.059
C(4) 0.003 0.002 0.069
C(5) -0.001 0.001 0.324
C(6) 0.005 0.001 0.000
C(7) 0.003 0.002 0.130
C(8) 0.013 0.002 0.000
C(9) 0.008 0.003 0.018
C(10) 0.004 0.003 0.164
C(11) -0.053 0.008 0.000
C(12) -0.065 0.007 0.000
C(13) -0.007 0.004 0.063
C(14) -0.017 0.006 0.004
C(15) -0.020 0.006 0.000
R2 (adj.) 0.223 Probability (F-stat) 0.000
S.E of regression 0.021 Mean dependent var. 0.019
Sum squared residuals 0.210 S.D. dependent var. 0.024
F-statistic 10.816 Durbin-Watson stat. 1.291
Null hypothesis: C(11)=C(12)=C(13)=C(14)=C(15)
Normalised restriction (=0) Value Std. Error
C(11) -0.053 0.007560
C(12) -0.065 0.007308
C(13) -0.007 0.004026
C(14) -0.017 0.006076
C(15) -0.020 0.005632
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