Determinants of bank lending behaviour in ghana

Journal of Economics and Sustainable Development
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.4, No.17, 2013

www.iiste.org

Determinants of Bank Lending Behaviour in Ghana
Jonas Ladime1* Emmanuel Sarpong-Kumankoma2 Kofi A. Osei2
1. Department of Banking and Finance, Methodist University College, P. O. Box DC 940, Dansoman,
Ghana
2. Department of Finance, University of Ghana Business School, P. O. Box LG 78, Legon, Ghana
* E-mail of the corresponding author: ladime2002@hotmail.com
Abstract
This paper investigates the determinants of bank lending behaviour in Ghana. Using the GMM-System estimator
developed by Arellano and Bover (1995) and Blundell and Bond (1998), we find that bank size and capital
structure have a statistically significant and positive relationship with bank lending behaviour. We also find
evidence of negative and significant impact of some macroeconomic indicators (central bank lending rate and
exchange rate) on bank lending behavior. Again, competition in the industry was found to have a positive and
significant impact on bank lending behaviour. Finally, relationship banking was found to have a positive
correlation with bank lending behaviour in Ghana. Thus, policies aimed at maintaining stable macroeconomic
fundamentals would greatly accelerate bank lending decision.
Keywords: Banks, lending behaviour, Ghana
1. Introduction
The banking industry is one critical component of the financial system in developing countries capable of
facilitating capital accumulation and economic processes. This is possible through efficient financial
intermediation. The banks mobilize funds from the surplus spending units in order to bring financial costs down.
Banks mostly transform liquid assets like deposits into illiquid assets like loans (Diamond and Rajan, 1998).
This transformational process of banks’ activity is at best influenced by a host of factors, namely,
macroeconomic, bank level (Peek and Rosengreen, 1995) and industry level characteristics (Boot and Thakor,
2000). Boot and Thakor (2000) indicate that the level of banking industry competition greatly influences bank
lending strategy positively. Again, Kashyap and Stein (2000), find a strong case that in situations where a bank is
handicapped in terms of credit, it will only take the bank capital to measure its ability of lending.
Ghana’s rapid economic growth in the past was supported by the good performance of its financial institutions.
Indeed Ghana’s macroeconomic performance has been very significant with major indices enjoying relative
stability. The national output grew at an annual average of about 6.3%. Although macroeconomic stability is
highly regarded as a major condition for sectorial credit flow, gross credit to the private sector has remained
relatively stagnant and difficult to access (World Bank, 2008). Credit to the Agriculture sector actually declined
by about 3% with total flow to the manufacturing subsector of the industrial sector remaining relatively low. A
total of ¢25,197.6 billion credit facility was allotted to the private and the public sectors by the deposit money
banks representing a growth rate of 40.5% in the year 2006. Overall, total credit increased by 27.1% in real terms
by the end of 2006 as against a 21.9% in 2005 (Bank of Ghana, 2006). The banking industry in Africa and
Ghana in particular forms a strategic hub of the financial system. Lending decisions by banks cannot be
overlooked as they are the principal providers of funds to governments, corporate bodies and individuals as a
whole (stock markets are just recent developments in the financial system). Existing literature provides paucity
of empirical evidence on bank lending behaviour in emerging markets like Ghana. In a developing economy like
Thailand, Suwanaporn (2003) provides that banks consider risk and relationship factors in their bank lending
decisions. This work tries to fill this gap and find evidence of the determinants of bank lending behaviour in
Ghana. Specifically, we investigate the effect of bank specific, macroeconomic factors and industry
characteristics on bank lending behaviour.
The rest of this paper is organized as follows: section two discusses existing bank-lending behaviour literature.
Section three focuses on methodology and the estimation approach while section four discusses the results,
provides conclusions and policy implications of the study.
2. Review of Literature
The fundamental role of a bank is intermediation by way of collecting savings from depositors and making these
savings available as loans to borrowers. Banks are more efficient in the collection of information and loan
production to dispel doubt on asymmetric information (Suwanaporn, 2003). Again, banks are in the right
position to evaluate the future potentials of good investments as they have much more experience in doing that
with similar investments. Notwithstanding, these specialties, banks are more circumspective about their lending
decisions. Lending decisions are influenced by a host of factors as explained below.
One of the underlying factors for lending decision is the level of bank capital. The effects of bank capital on
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lending behaviour have been widely debated since the 1988 Basel Capital Accord (Gambacorta and Mistrulli,
2004). Diamond and Rajan (1999) also show that there is a positive relationship between loan growth and capital
requirements and its regulations. The real impact of bank capital on its lending behaviour has received more
attention in the USA banking system. A clear example is Kishan and Opiela (2000). The general conclusion is
that there is a connection between bank capital and bank lending behaviour. However, the empirical literature on
European countries is rather inconclusive. Ehrmann et al. (2003) find that monetary tightening has a severe
negative impact on rather undercapitalized banks’ lending. Thus, one can conceive that, the precise relationship
between bank capital and lending is mixed. Empirical evidence from emerging economies would therefore
enrich our understanding of the bank capital-lending nexus.
Bank interest rate spread has also been shown to affect lending behaviour. Monetary policy, through a prime rate
(Central Bank’s rate) has a transmission mechanism on interest rates in the financial market (Borio and Fritz,
1995). Bank lending rates are mostly seen as being rigid for the reason that they do not move in tandem with the
markets. A number of explanations have been suggested to account for the rigidity in bank lending rates. In the
case of loans, the rigidity has been as a result of the rationing of credit to borrowers owing to the fact that there
are problems of asymmetric information (Blinder and Stiglitz, 1983). Indeed, financial markets are not perfect;
in the presence of adverse selection and moral hazard issues, banks are more likely to opt for credit rationing
than to adjust their lending rates in a situation where there has been an upward adjustment of interest rates by the
central bank. It may also be possible that when large banks capture large market share, the impact of tight
monetary policy on bank lending will be minimal. However, Berger and Udell (1992) could not find concrete
support for the rationing of credit as a reason for the rigidity of lending rate.
Bank size is considered as an important determinant of bank lending decision (Berger and Udell, 2006, Uchida et
al. 2007). Berger and Udell (2006) provide that large and complex banks tend to lend few loans to small scale
firms. Stein (2000) explains that small banks have comparative advantages in producing soft information
whereas large banks also have comparative advantages in lending based on hard information. On the other hand,
when large and complex banks are able, through technical expertise, to process soft information about small
scale firms, then there would be positive relationship between bank size and lending.
Additionally, the macroeconomic environment within which a bank operates matter for its lending decision. For
instance, in the period of economic boom, businesses demand for loans to take advantage of expansion and
banks investment opportunities equally soar. On the other hand, in periods of economic recession, demand for
credit plummets. This provides a pro-cyclical relationship between economic growth and bank lending.
Dell’Ariccia and Marquez (2006) find that bank credit expansions tend to be pro-cyclical; that is, high rates of
growth in GDP tends to induce a high rate of growth in bank credit. This is because in the period of economic
boom, banks relax their criteria and lend to both good and bad projects, then in times of economic recession most
loans become non-performing and the source of credit dries up, rationing out even good projects. In Italy,
Vazakidis and Adamopoulos (2009) indicated that economic growth had a positive effect on credit market
development. Again, the central bank’s prime rate serves as an indicator to the movement in key economic
variables like inflation which in turn affects interest rates. Through the transmission mechanism, an increase in
prime rate negatively affects banks’ lending behaviour. Exchange rate fluctuations, specifically currency
depreciation in a home country results in banks’ assets being valued less in foreign currencies as against their
liabilities. Additionally, Lindgren et al. (1996) find that fluctuation in exchange rate is a prime cause of poor
performance of banks’ borrowers, which subsequently affects bank profitability. This situation is more certain in
developing economies which are exposed to foreign trade. Excessive exchange rate variation weakens economic
and financial growth in a country and is seen to be the most significant cause of the banking crises in a lot of
countries (Lindgren et al. 1996). In a developing and open economy like Ghana, one expects that exchange rate
depreciation will negatively affect bank lending behaviour.
With regards to industry structure, the precise relationship between bank industry structure and lending is moot.
From an economic theory perspective, exercise of market power in banking will lead to a higher rate of interest
and a lower supply of available credit than in a perfectly competitive market. On the other hand, there seems to
be no consensus in literature as to the precise impact of bank market structure on the supply of lendable funds.
One school of thought argue that in the presence of market power (high concentration), banks have more
incentive to invest in the acquisition of soft information through relationship banking (by establishing close
relationships) with borrowers over time thereby, enhancing the supply of credit and consequently reducing firms’
financial constraints (Dell´Ariccia and Marquez, 2004). Additionally, Boot (2000) argued that, even though a
borrower runs the risk of paying higher interest rates in a context of non-competitive banking markets, the
borrower can benefit from a greater availability of finance. Another school of thought posits that in a market
characterized by competitive conditions, lending rates are lower hence more financing for firms. In other words,
concentration heightens financing obstacles to firms, especially from developing countries like Ghana (Beck et
al. 2004).

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3. Data and Model Specification
The study uses panel data, which involves pooling of seventeen (17) banks over the period 1997 – 2006, and
further adopts the model used by Alfaro et al. (2003). This model assumes that bank lending behaviour today is
explained by past lending experience (lag of the dependent variable), banking industry characteristics,
macroeconomic and bank-specific variables. Thus we formulate the model in the following way:
3

3

yit = ρyit −1 + β1 HHI t + ∑ α j BC i jit + ∑ γ s MAC i st + ε it
j =1

s =1

Where yit represents the log of bank lending behavior proxied by total loan portfolio of bank i at a given period, t.
BC is a vector of bank specific variables including size, bank spread and capital structure (measured by total debt
over shareholders’ funds). HHI represents the industry characteristic which is an index of competition, and MAC
is macroeconomic variables including prime rates, the growth rate of real gross domestic product and log of
exchange rate. The variables ρ, β1, α and γ are vectors of estimators or coefficients and ε is an error structure
defined below:

ε it = vi + µ it
is the disturbance with
the unobserved bank–specific effect and µ the idiosyncratic error.
The lag of the dependent variable was included as an independent variable on assumption that for every current
loan supply or bank lending, there is a credible past relationship (in other words bank lending will depend on the
previous credit relationship) and the bank will re-assess the borrower for the current level of information
asymmetry. When the lag of the dependent variable coefficient assumes a value of 1 then the relationship could
be termed as good and a value close to zero will represent a fairly good relationship between lenders and
borrowers. The size of the bank is measured by the log of total assets or the size of deposits. Size is expected to
have a positive influence on bank lending behavior. With regards to Capital structure, it is measured as the debt
to capital or equity ratio and it is expected to move in tandem with bank lending behaviour. The bank spread is
measured by the net interest margins of the banks as a percentage of the net interest income of every bank. The
spread represents a premium charged by the banks as an additional cost to borrowers and the numerous risks
levels that are faced by the banks. Therefore, higher spread depicts the volatility among borrowers and that
makes it difficult for banks to give out loans to individuals and firms. Hence, banks will only lend if and only if
borrowers are willing and able to pay more premium. We also used exchange rate consistent with (Alfaro et al.
2003). We measured exchange rate as the Ghanaian Cedi per United States Dollar. The exchange rate is expected
to have a negative effect on bank lending behavior, especially on the domestic banks.
We also control for the level of economic activities likely to influence bank lending using the growth rate of real
gross domestic product. GDP growth is expected to have a positive impact on bank lending behavior. Bank
lending behaviour in response to monetary policy from the central bank was captured using central bank’s
prime/lending rate. This rate measures how Bank of Ghana loosens and tightens the monetary policy that either
eases up credit or tightens up credit. The prime rate in the regression equation is expected to move negatively
with bank lending behaviour. The macroeconomic variables were obtained from the Bank of Ghana website
whereas bank-specific variables are from the audited financial reports of the banks.
3.1 Estimation Method
The study used Generalized Method of Moments (GMM) estimators, which was propounded by Arellano and
Bond (1991), particularly the GMM-System estimator developed by Blundell and Bond (1998). The dynamic
model is the most appropriate model specification for the general form of the first-differenced GMM estimation.
Several merits inform our choice of dynamic model estimations. It is preferable in situations of omitted variables.
In addition, the variables used as instruments permit the estimators to be estimated harmoniously in models that
have the problem of endogeneity. Last but not the least is that instruments are able to permit harmonious
estimation in times where there might have been some measurement error (Bond et al., 2001). The
appropriateness of the model is tested using the Sargan and the serial correlation tests. The Sargan test, tests for
the over-identifying restrictions. This study tests the sample analog of the moment conditions that were engaged
in the process of the estimation to test for the overall validity of the instruments. The test statistic here is a ( )
distribution where m is the number of degrees of freedom obtained from the difference between the number of
instruments and regressors. A test for serial correlation is looked at by formulating a null hypothesis that the
error term is not serially correlated. The null hypothesis formulated as the error term in the differenced equation
shows no second-order serial correlation.
4. Empirical Results
Table 1 provides the results of the dynamic model. The coefficient of the lag variable is statistically significant,
positive and less than unity (1). This implies a fairly good relationship among banks and borrowers and could be
further reinforced by previous lending relationship, giving high likelihood that banks will lend more in a current
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period. The bank size has a positive and statistically significant influence on bank lending. Bigger banks thus
lend more than smaller ones. This is consistent with existing studies such as Alfaro et al. (2003) that bank size
indeed contributes significantly to loan supply.
Table 1: Dynamic Model Result
Dependent variable:
Loans and advances

Coef.

Z

P> Z

0.1904

3.46

0.001

0.7031

10.69

0.000

Llad
L1.
Bank characteristics
Log of Size
Capital Structure

0.8208

3.06

0.002

Spread

-0.3405

-1.26

0.209

Prime rate

-6.493

-7.10

0.000

Log of Exchange

-0.703

-4.45

0.000

Real GDP growth

-0.7986

-0.15

0.881

HHI

11.0094

3.91

0.000

Cons

6.9929

3.90

0.000

Macroeconomic indicators

Industry characteristic

Wald chi2(8) = 5467.56
Prob > chi2 = 0.0000
Note: AR(1) is -3.16 and AR(2) is -0.0859 with p-values 0.0016 and 0.9315 respectively. Sargan test χ2(2)
=9.0117 with p-value 0.7721.
What this means is that if banks are able to raise enough capital from the capital market, it is most likely this will
enhance their lending decision and economic development because banks and the stock market play a
complementary role in economic growth as has been found by Zukarnain (2008). With regards to the capital
structure, the coefficient reports a positive and statistically significant effect on bank lending behaviour. Again,
because maturity mismatch has a great negative impact on bank profit as well as capital, if all doors are shut for
banks to raise some additional capital, they are forced to reduce lending as regulation requires that bank capital
has to be around a certain minimum percentage of loans (Van den Heuvel, 2001). In addition, the study confirms
studies done on US banks which suggest that there is an impeccable necessity of bank capital having some sort
of relationship with bank lending behaviour (Kishan and Opiela, 2000).
Log of exchange rate and Bank of Ghana prime rate have negative coefficients and are significant in all
estimations. As expected, anytime the central bank tightens monetary policy, bank lending is narrowed or
reduced. The results suggest that increases in inflation reduces the real return to the banks and hence restrict the
amount of money the banks wish to lend. The log of exchange rate with a negative coefficient indicates that
depreciation of the domestic currency against a foreign currency tends to reduce the volume of loans banks make.
This is because banks tend to invest more in foreign currency if they expect the domestic currency to depreciate
hence reducing the amount available for loans and advances. Unexpectedly, the results indicate that real gross
domestic product has a negative coefficient and is not significant in all regressions. However, this may suggest
that bank lenders’ expectations do not depend on the current phase of business activities, which confirms
findings of Danilowska (2008). With regards to HHI, the results indicate that the banking industry structure has a
positive and significant effect on bank lending behaviour. By implication, a competitive banking environment is
likely to enhance bank lending behaviour in Ghana. This confirms a study by Beck et al. (2004).
5. Conclusions
The study observes a relationship between bank lending behaviour and a set of macroeconomic indicators,
industry and bank level characteristics. Bigger banks seem to be in a better position to lend more than otherwise.
This might be due to enough resources they have to cushion lending. Similarly, high level of bank capital is
found to support much higher volumes of bank lending. A strong and resilient financial system is necessary for
economic growth. It restores confidence and determines the elasticity of the system to shocks as well as
enhancing the credibility of the financial institutions in the system. Therefore, the Bank of Ghana recapitalization
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policy must be encouraged so as to help the economy prepare against any disastrous macroeconomic shocks. It
will also enable well-capitalized financial institutions to respond to increased demand for credit. Moreover, the
central bank must adopt a pragmatic approach that would ensure the stability of the local currency against the
foreign currencies to enhance bank lending decision in Ghana. However, in as much as tight monetary policy
would be considered favorable in certain situations, it has negative implications on bank lending decision. Thus,
as a policy guide, the central bank should critically weigh all the outcomes and the trade-off of tight monetary
policy so as not to dampen the lending decision of banks to firms since this would have dire consequences on
economic development of the nation. Again, a competitive banking industry structure enhances bank lending
decision. This might be due to associated low lending rate that characterizes competitive banking systems.
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