What Influences Banks Lending in Sub-Saharan Africa?

Abstract

This article analyses the broad determinants of bank lending in Sub-Saharan Africa (SSA) using both micro-bank and macro-country level data of 264 banks across 24 SSA countries. The core finding is that the structure of banking markets influences credit delivery in SSA in an environment where the financial sector is reformed and banks are allowed to operate freely. Also, there is an evidence to suggest a link between bank credit and the financial strength of the banks. The overall results suggest that regulatory initiative, which restricts banking activities, imposes severe entry requirements and requires high regulatory capital, influences banks’ decisions to supply loans.

JEL Classification: E52, G21, G28, N27

Keywords

Bank lending government policy and regulation Sub-Saharan Africa

Introduction

The importance of banks in any economy developed or developing, liberalised or centrally controlled, cannot be overemphasised. Banks play a key role in financing economic activities in a country. They provide liquidity on demand to depositors and extend credit as well as liquidity to borrowers through lines of credit (Kashyap et al. 1999). Due to these fundamental roles, banks have always been concerned with both solvency and liquidity risk. Hence, banks actively evaluate and take risks on a daily basis as part of their core business processes. To assess, manage risks and extend credit, banks must have effective ways to determine the appropriate amount of capital that is necessary to absorb unexpected losses arising from their market, credit and operational risk exposures. Banks are also involved in shaping the operations of borrowers through screening, monitoring and enforcement of loan repayment, thus, involving themselves in forms of corporate governance. More importantly, the governance of banks also influences their lending behaviour. Studies have however shown not only that bank lending is constrained by the level of capital, risk or its governance, but also that bank lending decision is directly constrained by the monetary policy actions, macroeconomic uncertainty, legal and financial structure as well as regulatory environments (Balazs et al. 2006; Baum et al. 2009; Cecchetti 1999; Ehrmann et al. 2003; Pruteanu-Podpiera 2007; Quagliariello 2009). Thus, the supply of bank loans depends on multifaceted issues including bank’s financial positions, Balazs et al. (2006); the country’s financial structure, Hainz (2003); the monetary policy, Pruteanu-Podpiera (2007); the macroeconomic environment, Baum et al. (2009); and regulatory framework, Cottarelli et al. (2003).

The recent trend in financial intermediation suggests a less impressive performance of banks in the supply of loans to the private sector in Sub-Saharan Africa (SSA). Figure 1 shows the development of private sector credit relative to GDP of SSA banks which has been increasing steadily; it is below that of Europe and East Asia and Pacific. The poor bank credit, according to the 2006 International Monetary Fund (IMF) report, is in itself a function of widespread poverty and a high share of the population is engaged in subsistence agriculture. The large concentration of populations on subsistence production limits the financial resources available for intermediation. Demirguc-Kunt et al. (2004) argue that the low-income countries’ private sector correlates positively with GDP per capita income and negatively with the size of the agriculture sub-sector. The low performance of SSA banks in the area of credit has occurred in the environment of high liquid reserves, broad money ratio (excess liquidity) and extreme risk aversion in the banking system (IMF 2006). In addition to excess liquidity and the high ratio of non-performing loans in the SSA banking system, the debt position of SSA countries has also accounted for the poor performance in bank credit extension (Christensen 2004; Nissanke and Aryeetey 2006).

Figure 1.

Source: World Bank Development indicators (2009).

The issue of non-performing loans could largely be due to the limited capacity of banks in SSA to monitor and efficiently assess the risk of their loan clients. Nissanke and Aryeetey (2006) added that SSA banks predominantly extend credit to large-scale formal real sector activities, the bulk of which are owned by the state. These organisations are characterised by inefficiencies which have resulted in low return and poor financial performance. A corollary to the non-performing loans is the issue of enforcement of contract in SSA. McDonald and Schumacher (2007) emphasise that banks will be willing to extend more credit if in the event of default, they could enforce contracts by ensuring repayment or seizing of collateral. On the issue of the SSA financial system, Nissanke and Aryeetey (1998) suggest that the decades of prolonged financial repression could be a factor of low bank lending. They argue that economic growth in African countries was retarded because the size of the financial system was reduced by the distortions of financial prices, control over credit allocation, interest rate ceilings and fixed exchange rates. The pressure of government domestic financing practices has also led to crowding out of the credit to the private sector. Christensen (2004) provides an evidence to suggest that government domestic securitised debt crowded out the credit to private sector.

To enhance credit delivery and to foster a credit culture in SSA, Sacerdoti (2005) suggests among other things; efficient accounting standards, availability of and quality of collateral, credit information and credit recovery. Certain questions are raised as to whether the low credit delivery in SSA is due to bank-specific conditions, or an issue of ‘fragmented financial system’ or the subject of monetary policy, macroeconomic environment or is it an issue related to regulatory frameworks. This is exactly what this article seeks to address by employing both bank and country level data. It is only when the bank lending determinants are identified that the necessary policy actions can be put in place to reduce the constraints related to the credit supply.

The main contribution of this article is to analyse the factors that impact bank lending, focusing on whether bank lending in SSA is influenced by bank-specific characteristics, the monetary policy stance, macroeconomic variables and legal and financial structure.

The rest of the article is organised as follows: The second section reviews the existing empirical literature. The third section presents credit delivery model, other explanatory variables used, data and the estimation methodology. The fourth section contains the analysis and the results. Finally, the fifth section concludes.

Literature Review

In the literature, the supply of bank loans is usually expressed as a function of both internal and external determinants. The internal determinants are termed micro- or bank-specific determinants of bank lending, while the external determinants are variables that are not related to bank management but reflect the monetary, economic and legal environment that affect the operations and performance of financial institutions.

Bank Level Environment

Studies dealing with internal determinants employ variables such as size, liquidity, capital and efficiency (either labour or management). Bank size is introduced to account for existing economies or diseconomies of scale in the market. Smirlock (1985) finds a positive relationship between size and bank’s performance, suggesting that the bigger the bank the higher the supply of loan. Demirguc-Kunt and Huizinga (2000) argue that the extent to which various financial, legal and other factors affect bank’s performance is closely related to size. Bikker and Hu (2002) and Goddard et al. (2004) link bank size to capital and hence performance. On the contrary, Vihriälä (1997) finds that the lower the degree of capitalisation of a bank, the more expansionary the supply of loans. However, Ehrmann et al. (2003) find no relationship between capitalisation level of European banks and supply of loan. Instead, liquidity to an extent allows the bank to draw on it instead of going to the market. According to Kashyap and Stein (2000) and Pruteanu-Podpiera (2007), liquidity reduces the increase in the marginal cost of funds after a monetary tightening. High levels of reserve requirements on deposits in developing countries have been cited as an instrument of financial repression (Chamley and Honohan 1990). Balazs et al. (2006) argue that changes in bank’s financial position affect the availability of credit supply in developing countries. High non-performing loans on the balance sheet of SSA banks could explain the weak credit delivery that exists in most African countries.

Macroeconomic Environment

A bank’s balance sheet position is not the only factor that influences the supply of bank loans. Studies suggest that the stance of monetary policy, reflected in the level of interest rate, affects the supply of bank loans. According to Hofmann (2004), the supply of bank loans may arise from the effect of monetary policy on the creditworthiness of firms (balance sheet channel) and of households (interest rate channel) via its effect on their financial positions, or from a drain of reserves and thus loanable funds from the banking sector (bank lending channel) following changes in the stance of monetary policy operated through open market operations sales by the Central Bank. However, there is no conclusive position on the effect of monetary policy on bank lending. Bernanke and Gertler (1995) refer to this as ‘a black box’. Some empirical studies find no evidence of a significant influence in bank lending with changes in monetary stance, whilst others find a significant relationship. For instance, Gertler and Gilchrist (1993) conducted a study that specifically looked at how banks business lending responds to policy tightening. Their study reveals that business lending does not decline when policy is tightened. In contrast to Gertler and Gilchrist’s (1993) findings, Kashyap and Stein (1995), Ehrmann et al. (2003) and Pruteanu-Podpiera (2007) find evidence that the supply of bank loans may respond to a tightening of monetary policy.

In addition to monetary policy stance and bank’s balance sheet position, there is evidence supporting the hypothesis that macroeconomic conditions affect the performance of the banking system and this affects financial intermediation. Quagliariello (2007) finds that during economic booms, banks tend to expand their lending activity, often relaxing their selection criteria resulting in an increase in bad loans. Hoggarth et al. (2005) provide a link between the state of the UK business cycle and the banks’ supply of loans. Baum et al. (2005) use a sample of US bank and portfolio models and find that as macroeconomic uncertainty increases, the cross-bank dispersion of the share of risky loans to total assets diminishes since the uncertainty hinders bank’s ability to foresee investment opportunities. Thus, the uncertainty pushes banks to rebalance the composition of their assets in line with the new signals revealed by credit markets. This would adversely affect the allocation of financial resources. Garcia and Calmes (2005) use Baum et al. (2005) approach on Canadian banks and confirm a negative relationship between macroeconomic uncertainty and the cross-bank variance dispersion of the loan-to-asset ratio. However, macroeconomic uncertainty has a limited effect on SSA banks’ performance in relation to their credit delivery (Al-Haschimi 2007). In SSA, it is the governments’ fiscal deficit and the level of public debt that explain the level of financial intermediation. Christensen (2004), regressing private sector lending on domestic government debt, reveals evidence of some crowding out. Though monetary policy stance and macroeconomic uncertainty influence the supply of credit to the private sector, the regulatory environment plays a role in determining bank lending. Cottarelli et al. (2003) show the effect of the institutional and regulatory environment as well as economic policy impact on private sector credit growth. They use variables that capture the level of financial liberalisation, the quality of implementation of accounting standards, entry restrictions and the evolution of public sector debt. This article employs entry restrictions, entry requirements, supervisory power and initial capital requirements as contestability variables.

Bank Market Structure

Studies show that several features of national banking structures are important for the response of bank lending to a monetary policy action, and for the assessment of the macroeconomic importance of such response. Campello (2002) examines the role of internal capital markets in influencing the investment allocation process of conglomerate banks. The results suggest that frictions between conglomerate headquarters and external capital markets are at the root of investment inefficiencies generated by internal capital markets. Brissimis and Magginas (2005) on their part find mixed results. In the US and the UK where financial systems are market-based, the market structure has no significant impact on determining supply of bank loans to private sector. On the one hand, their results suggest that the financial structure significantly influences the supply of bank loans in Japan and three other European countries—Germany, France and Italy. Cecchetti (1999) finds that the strength and the magnitude of the supply of bank loans depend not only on the size and the concentration of the banking system, but also on the legal structure. In SSA, poor saving/investment performance appears to be related to financial market fragmentation resulting in poor intermediation. A corollary to the above is the type of branch network in SSA which studies have shown affects credit delivery (Beck et al. 2005). Branch networks are generally small and concentrated in Africa. This, they concluded, makes physical access to financial services very difficult. The average branch network density in SSA is only six branches per 1,000 square kilometres, compared with 34 for non-SSA countries (IMF 2005). Clearly, the type of banking structure determines the supply of bank loans.

Data and Methodology

The Bank Credit Delivery Model

The analysis of the determinants of bank credit delivery in SSA considers bank portfolio behaviour using data measured over a period of time as well as across many banks in a number of countries. The article uses a panel data regression model and the analyses are conducted using data over an eight-year period, 2000–07, covering 264 banks from 24 SSA countries. The model specification followed that of Baum et al. (2009), Balazs et al. (2006) and Cottarelli et al. (2003). The relationship between bank’s financial position, monetary and macroeconomic shocks, on the one hand, and bank credit delivery, on the other hand, can be intuitively explained as follows: during stable periods, banks take advantage of the perceived investment opportunity and respond more accurately to loan demand, having identified and assessed their balance sheet position and regulatory environment. Conversely, during the period of uncertainty where the returns to bank lending are difficult to predict because the environment provides no prospect to identify profitable and viable lending opportunities, bank lending therefore reduces. The estimation strategy considers the relationship where credit delivery is a function of monetary policy stance, economic activities, bank’s market structure, regulatory initiative and bank-specific characteristics. Following Adams and Amel (2005), the dollar amount of bank i loan to private sector in period t is used as the dependent variable as:

{Credit}_{ic, t}_{1} {Credit}_{ic, t 1} \underset{j}{}_{j} X_{ic, t}^{j} \underset{m}{}_{m} X_{c, t}^{m}_{i, t}

(1)

where Credit_ic,t is the dollar amount of bank i credit to private sector of country c in period t, Credit_ic_,t–1 is the observation of the same bank from the same country in the previous year, the variables $X_{ic, t}^{j}$ and $X_{c, t}^{m}$ denote bank-specific characteristics, respective countries’ specific macroeconomic environments, market structure and regulatory initiatives. a's are the parameter vectors and f_it is the disturbance term. Equation (1) recognises that the credit delivery equation captures the impact of adjustment in derived demand in a dynamic panel format. The estimation therefore takes into consideration a lagged dependent variable among the explanatory variables. The advantage of this approach according to Baum et al. (2009) is that we can relate the behaviour of bank lending directly to a measure of bank-specific characteristics, bank’s market structure, regulatory initiatives and monetary and macroeconomic shocks.

Explanatory Variables

The estimation technique for bank credit to the private sector relies on explanatory variables used in the previous studies. However, this article extends the previous studies to include the impact of bank-specific, market structure, contestability, monetary and general level of economic development variables on bank lending as follows.

Bank-specific variables

Studies on bank lending behaviour have noted that bank-specific variables have a capacity to explain the behaviour of credit delivery (Gaiotti and Secchi 2006; Kashyap and Stein 2000; Kishan and Opiela 2000). More specifically, the size of the bank, the efficiency of the management, bank liquidity, bank capitalisation level and bank growth are the bank-specific variables used in the discussion of bank lending behaviour in SSA. Bank size is measured as a logarithm of total assets. Management efficiency is measured as the earning assets divided by total asset. Liquidity is constructed as total cash plus bank total short-term investments divided by total assets. For this study, excess liquidity is used as it reflects the shallowness of financial market as well as the inefficiency of banking operations shown in high intermediation and transactions costs. Excess liquidity is calculated as the difference between bank’s liquidity ratio and reserve ratio requirement. Bank growth is measured as a first difference of total assets divided by previous total assets while bank capitalisation is measured as the ratio of equity capital to total assets.

Market structure variables

Concentration, bank density and the Lerner index are used as bank’s market measures. Bank concentration is measured as a fraction of a country’s total banking assets held by three banks; the logarithm of the number of banks per million inhabitants in a particular country, as a proxy for the density of banks; and the Lerner index as a measure of bank’s market power. The Lerner index provides a direct measure of the degree of market power as it represents the mark-up of price over marginal cost. It is the only measure of competition according to Berger et al. (2009) calculated at the bank level as Lerner_it = (Price_it - MC_it)/Price_it. Here the Price_it is the price of the total assets calculated as a ratio of total income to total assets. MC_it is the marginal cost of producing an additional unit of output and it is derived from the translog cost function.

Bank stability

Difference risk exposure indicators are used as a proxy for bank stability: the Z-score is used as a measure of overall bank risk, the risk-adjusted profit as a measure of performance, the volume of non-performing loans as a measure of bank loan portfolio risk. The Z-score uses bank level data and potentially measures the accounting distant to default for a given bank. It measures the overall bank insolvency risk; the risk-adjusted profit is used as a measure of profitability and the volume of non-performing loans to total gross loans measures bank loan portfolio risk.

General level of development

General economic development, macroeconomic and monetary stability and institutional framework are controlled as these are likely to affect banking system performance. GDP growth is used as a control for cyclical output effect which appears to have a positive influence on bank lending. However, when the GDP growth slows down and in particular during recessions, credit quality deteriorates, and default increases thus, reducing subsequent bank lending (Flamini et al. 2009). Thus, GDP per capita is used to control for different levels of economic development in each country. GDP/GDP per capita growth is measured as the annual rate of growth of GDP/GDP per capita and inflation is measured as the annual growth rate of the consumer price index (CPI) index. On the monetary policy stance, this article employs the respective countries’ short-term interest rates as a measure of monetary policy indicator.

Stock market capitalisation, banking freedom and financial reform index are also included in the estimation equation. Following Demirguc-Kunt and Huizinga (2000), stock market indicator is used to describe the contribution of capital markets and to investigate its impact on bank lending. The size of the country’s stock market capitalisation to GDP is used as a proxy for contribution from non-bank financial institutions. The banking freedom provides the overall measures of the openness of the banking sector and the extent to which banks are free to operate their businesses. The measure describes the country’s financial climate and assigns an overall score between 0 per cent and 100 per cent, with a higher percentage score signifying more freedom. The liberalisation index constructed by Abiad et al. (2010) is used as financial reform variables. The index measures financial reforms that have taken place and ranges from 0 to 21 with the highest scores indicating fully reformed.

Activity restrictions, entry into banking requirements, initial capital requirement and bank supervision power are used as contestability (regulatory) variables. Activity restrictions measure the degree to which national authorities allow banks to engage in activities that generate non-interest income and the measure varies from 4 to 16 with higher scores indicating more restrictions. The entry requirement indicates the severity (range from 0 to 8) of entry regime with higher values indicating more restrictiveness.¹ Capital index measures overall capital stringency. It ranges from 0 to 9, with a higher value indicating greater stringency. The official supervisory power describes whether the supervisory authorities have the power to take specific actions to prevent and correct problems and it ranges from 0 to 16 with the higher score indicating more supervisory power.

Data Sources

Micro-bank level and macro-country level data are used. Bank level data (financial statement) is taken from BankScope database maintained by Fitch/IBCA/Bureau Van Dijk. Series are yearly, covering a sample of 264 banks across 24 SSA countries during the eight-year period, 2000–07. As the study focuses on bank intermediation, unconsolidated balance sheet data are used whenever possible even though in some cases the study has to depend on consolidated statement because of data unavailability. The sample includes all commercial banks, cooperative banks, development banks, savings banks, real estate and mortgage banks for which annual data is available for some period or the years during the period 2000–07. To ensure that banks that are important players in the deposit and/or loan markets are not omitted, medium- and long-term credit banks and specialised government institutions are included as they remain important in SSA countries. Macro data are obtained from the World Development Indicator of the World Bank Development indicator and International Financial Statistics database of the IMF and the respective central banks, while activity restrictions, entry into banking requirements, capital stringency and supervisory power variables are sourced from Barth et al. (2004). Banking freedom data and financial reform index are obtained from Heritage Foundation and Abiad et al. (2010), respectively.

Empirical Results

Descriptive Summary Statistics

The descriptive statistics are presented in Tables 1, 2 and 3. Table 1 provides bank-specific variables averaged for the period 2000–07 and Table 2 provides summary statistic for bank’s market structure and contestability, while Table 3 deals with the measures of banking stability variables. Table 1 shows that on the average, 73.18 per cent of Namibian banks’ assets in the sample are loans extended to customers. This is the highest in the sample. The least is the Angolan banks whose average loan portfolio is 24.00 per cent. The overall average is 49.10 per cent indicating that more than 50 per cent of the selected African banks’ assets are outside loans. South African (SA) banks are the largest banks in terms of size. The average bank size of the SA banks is more than 8,104 million US dollars. On management efficiency, Botswana banks’ management is most efficient with the most growing banks located in Angola. However, Namibian banks are the most capitalised with a percentage of 31.07, the highest capitalisation level among the sample, while Nigerian banks are the banks with excess liquidity. Table 2 shows that least concentrated banking system is located in SA and the largest number of banks per million of population is in Mauritius. That is, Mauritius has the highest bank density. Togolese banks are the banks with the highest market power. Activity restrictions are severe among the Ethiopian banks. This means that banks in Ethiopia cannot freely engage in non-banking activities such as securities markets, insurance and real estate. Despite Ethiopia being the country with severest activity restrictions, it scores least with regard to the entry requirement. It is therefore easy to establish banking activities in Ethiopia. On the issue of initial capital requirement, SA banks seem to have the most capital stringency requirements. On the measure of banking stability, Table 3 shows that Swaziland banks are the most stable and the least stable are the Zimbabwean banks with a Z-score of 3.21 (SSA average is 14.03). Namibian banks are the most profitable with a Sharpe ratio of 5.16. Sierra Leonean banks are the banks with the highest non-performing loans. Almost 32 per cent of their loans are non-performing. It is the highest and above the overall average of 10.94 per cent.

Cross-country Determinants of Bank Loan to Private Sector

The specification in Equation (1) relates the observed variations in the supply of bank loans to private sector to its lag, a monetary policy indicator, and several control variables to account for the general economic situation, some bank-specific characteristics and certain regulatory initiatives adopted by the various countries. The specification of the model is based on data for 24 SSA countries for the period 2000–07 and the use of the General Least Square techniques. The random effect estimator is preferred given our interest in examining the effect of a number of time-invariant variables. In addition, a Hausman specification test could not reject the hypothesis of no correlation between the errors and the regressor. The regressions are conducted in columns using bank-specific variable, bank market structure, bank stability and contestability that include regulatory initiative variables—activity restrictions, entry into banking requirements, capital stringent and supervisory power. Banking structure variables are concentration, density of banks and market power. The column labelled bank stability includes the Z-score, ratio of non-performing loans to total loans and Sharpe ratio (risk-adjusted return on equity). All the regressions include monetary policy indicator and two macroeconomic variables to control for differences in monetary policy stance, macroeconomic shocks and the respective countries’ level of economic development. The results are given in Table 4.

Table 1.

Bank-specific Variables: Averages for the Period 2000–07

	Loan to Assets	Bank Size	Liquidity	Mgt Efficiency	Bank Growth	Capital Ratio
Benin	0.5583	252.3278	0.1421	0.8033	0.1005	0.0929
Burkina Faso	0.6028	206.3341	0.1698	0.8578	0.1383	0.0883
Cameroon	0.5115	475.4163	0.2453	0.8676	0.1484	0.0752
Cote d’Ivoire	0.6247	412.0526	0.1439	0.8328	0.0935	0.0918
Ghana	0.4035	154.9792	0.4989	0.8327	0.3829	0.1189
Nigeria	0.3580	1,554.3790	0.6178	0.8381	0.3672	0.1546
Senegal	0.5657	344.8411	0.1411	0.8772	0.1499	0.0946
Sierra Leone	0.2519	32.8857	0.1788	0.8137	0.1958	0.1943
Togo	0.5788	121.1300	0.3420	0.7588	0.1325	0.1779
Kenya	0.5628	223.7234	0.4662	0.8460	0.1673	0.1861
Uganda	0.4255	133.9829	0.4590	0.8788	0.2288	0.1393
Tanzania	0.3845	186.9849	0.5074	0.8738	0.2579	0.1317
Ethiopia	0.5918	526.0118	0.3142	0.8850	0.2593	0.1163
Angola	0.2400	653.6827	0.3458	0.7729	0.4372	0.1281
Botswana	0.5493	409.5558	0.3298	0.9289	0.2139	0.1930
Malawi	0.3377	62.0842	0.3563	0.7800	0.2741	0.1335
Madagascar	0.4641	151.2108	0.2871	0.7310	0.1660	0.1085
Mauritius	0.5827	790.8535	0.1932	0.8914	0.1350	0.1826
Mozambique	0.4091	223.9475	0.3466	0.7420	0.2525	0.1549
Namibia	0.7318	520.1613	0.1721	0.9373	0.1766	0.3107
South Africa	0.6567	8,104.8160	0.2842	0.9272	0.1588	0.1779
Swaziland	0.7047	137.9059	0.2204	0.8883	0.1281	0.1436
Zambia	0.2849	129.9619	0.5363	0.8137	0.2569	0.1784
Zimbabwe	0.4036	1,358.3580	-0.0879	0.7414	0.2025	0.1628

Source: Bankscope and author’s own calculation.

Notes: The data comprises 264 banks across 24 SSA countries over the period 2000–07. The table presents summary statistics on selected bank-specific variables. Loan to assets ratio indicate bank portfolio mix. Bank size is measured as a logarithm of total assets. Liquidity is constructed as total cash plus bank total short-term investments divided by total assets management efficiency is measured as the earning assets divided by total asset.Bank growth is measured as a first difference of total assets divided by previous total assets while bank capitalisation is measured as the ratio of equity capital to total assets.

Table 2.

Financial Structure and Contestability

	Financial Structure			Contestability
	C3 (%)	Bank Density	Market Power	Activity Restriction	Entry Requirement	Capital Stringent
Benin	0.8429	0.0000	0.16597	10.500	8.000	7.000
Burkina Faso	0.6662	0.0000	0.04301	10.500	8.000	7.000
Cameroon	0.6369	0.5272	0.54417	9.500	8.000	3.000
Cote d’Ivoire	0.6266	0.0000	0.78942	10.500	8.000	7.000
Ghana	0.5885	1.4583	0.25226	10.500	8.000	6.000
Nigeria	0.4584	0.4730	0.32018	12.000	8.000	5.000
Senegal	0.6263	0.0000	0.35988	10.500	8.000	7.000
Sierra Leone	0.8718	0.0000	0.06861	10.000	8.000	—
Togo	0.9427	0.2690	0.91797	6.000	8.000	7.000
Kenya	0.3370	1.5363	0.21031	11.000	7.500	7.000
Uganda	0.5369	0.0000	0.36347	12.000	8.000	5.000
Tanzania	0.4593	0.0000	0.30627	9.000	8.000	3.000
Ethiopia	0.8627	0.0003	0.42468	15.000	5.000	2.000
Angola	0.7151	0.5905	0.07575	12.500	8.000	3.000
Botswana	0.7242	7.5766	0.20806	10.000	8.000	6.000
Malawi	0.6116	0.0006	0.31535	13.000	8.000	5.000
Madagascar	0.7712	0.0000	0.30246	11.000	7.000	7.000
Mauritius	0.5036	13.8419	0.21698	11.000	7.500	5.000
Mozambique	0.7344	0.0008	-0.08541	11.000	8.000	4.000
Namibia	0.4142	7.3006	0.46569	11.250	8.000	2.000
South Africa	0.2698	1.7088	-0.03238	9.000	8.000	7.000
Swaziland	0.6727	6.1758	0.19379	14.000	7.000	4.000
Zambia	0.6275	1.7817	0.33030	—	—	—
Zimbabwe	0.3834	0.0001	0.26762	10.000	8.000	6.000

Source: Bankscope, World Development indicators, Barth et al. (2004) and author’s own calculation.

Notes: The data comprises 264 banks across 24 SSA countries over the period 2000–07. ---, information not yet available. CR (3) % is three bank concentration ratio; bank density is the number of banks per million population; activity restriction is the degree to which national authorities allow banks to engage in activities that generate non-interest income; entry requirements are the specific legal submissions required to obtained a licence to operate as a bank; and the capital stringency is the explicit regulatory requirement on the amount of capital that a bank must have relative to various guidelines.

The entire countries’ bank-specific variables considered in the sample statistically impact bank lending behaviour. The results show that credit delivery to the private sector in SSA is influenced by the size of the bank, the liquidity position of such bank, the growth level as well as the efficiency of the management of the banks. The coefficient of bank size, its growth variable and efficiency is positive demonstrating that in SSA, bigger and most growing banks provide credit to the private sector. The result shows that, ceteris paribus 1 per cent growth of banks will increase credit to private sector by more than 35 per cent. Also, the coefficient of the efficiency variable is positive suggesting that in SSA, the most efficient banks support the private sector with loans. The relationship between bank credit and liquidity is negative and statistically significant, suggesting that bank liquidity is not a barrier for extending loans to those who demand bank loans. A possible explanation for this is that banks in SSA, especially the most liquid ones, tend to use their liquid position for activities other than supply of loans. The lagged dependent variable positively influences credit delivery to the private sector. Indeed, the current year banks’ support to private sector is influenced by the previous year’s results. Thus, the magnitude and significance of the coefficient on the lagged bank credit in our equations confirms the dynamic nature of the model.

Table 3.

Measures of Bank Stability

	Z-score	Sharpe Ratio	Bad Loan
Benin	10.08392	0.79597	0.12688
Burkina Faso	12.66114	2.30429	0.11106
Cameroon	14.98758	2.57901	0.11919
Cote d’Ivoire	8.34316	0.58859	0.08294
Ghana	11.75616	3.28749	0.11333
Nigeria	14.80174	1.95349	0.13765
Senegal	22.37535	3.74055	0.05615
Sierra Leone	7.72912	2.01718	0.31804
Togo	17.38936	2.11418	0.31642
Kenya	22.14288	2.43361	0.18155
Uganda	11.84336	2.35885	0.04331
Tanzania	13.70788	1.60701	0.10501
Ethiopia	12.36937	2.45812	0.13413
Angola	7.20847	0.69648	0.07227
Botswana	15.83030	4.42543	0.03671
Malawi	9.58383	2.56210	0.10396
Madagascar	9.24150	2.88216	0.07469
Mauritius	27.79948	3.27104	0.01931
Mozambique	12.67306	2.29813	0.07849
Namibia	18.41460	5.16238	0.06514
South Africa	15.13181	2.40621	0.12682
Swaziland	29.29393	3.16957	0.04153
Zambia	8.29063	0.40917	0.06997
Zimbabwe	3.21637	0.96761	0.09281

Source: Bankscope and author’s own calculation.

Notes: The data comprises 264 banks across 24 SSA countries over the period 2000–07. Z-score is a measure on insolvency risk. Risk-adjusted return of equity (Sharpe ratio) measure overall performance. Bad loans are a proportion of non-performing loans to total loans.

Table 4.

Cross-country Determinants of Bank Lending

Dependent Variable: Bank Credit to Private Sector
	Bank-Specific	Market Structure	Stability	Contestability
Bank credit to private sector_-1	0.6300***	0.9063***	0.9793***	0.9176***
	(0.0176)	(0.0089)	(0.0079)	(0.0086)
Bank size	0.3641***
	(0.0195)
Excess liquidity	-0.5655***
	(0.0437)
Efficiency	0.5704***
	(0.0899)
Bank growth	0.3508***
	(0.0346)
Concentration		-0.1591*
		(0.0859)
Bank density		-0.0033
		(0.0047)
Bank market power		0.0347
		(0.0443)
Z-score			0.0256
			(0.0190)
Sharpe ratio			-0.0507***
			(0.0144)
Bank bad loan ratio			-0.6076***
			(0.1154)
Activity restriction				0.0961*
				(0.0571)
Entry restriction				0.4157**
				(0.1893)
Regulatory capital requirements				-0.1327**
				(0.0512)
Supervision power				0.0300
				(0.0624)
Monetary policy indicator	-0.0352**	-0.0180	0.0673***	-0.0018
	(0.0141)	(0.0230)	(0.0203)	(0.0222)
GDP growth	0.0209*	0.0389*	0.0253	0.0249
	(0.0123)	(0.0206)	(0.0197)	(0.0186)
Inflation	-0.0001	0.0017	0.0005	0.0005
	(0.0007)	(0.0015)	(0.0013)	(0.0013)
Diagnostic tests
Number of observations	1,189	1,221	648	1,174
R ²	0.9803	0.9533	0.9707	0.9789
Wald chi²	33,698.88	13,190.54	16,652.61	1,225.74
p-value	0.0000	0.0000	0.0000	0.0000
Hausman (chi²)	680.35	275.73	83.18	125.61
P-value	0.0000	0.0000	0.0000	0.0000

Notes: The estimation is based on data for 24 SSA countries using random effects GLS estimators. The dependent variable is bank credit to private sector. Constant term included but not reported. Standard errors are reported in parentheses. ***, ** and * indicate statistical significance at the 1 per cent, 5 per cent and 10 per cent level, respectively. The results are presented in columns, depending on the categories (bank-specific, structure and contestability) of independent variables. All regressions include monetary policy stance and two macroeconomic variables.

With regard to bank market structure variables, the only variable that influences credit supply is the concentration. The parameter of the concentration is negative and statistically significant, indicating a strong relationship between bank lending and concentration in SSA. The negative relationship shows that in the country where banks are most concentrated, the supply of loans is less. Contrary to this, we find that bank’s market power has a positive sign though insignificant, meaning that the bank with relative market power increases bank loan private sector. This finding therefore suggests that the bank’s market power has no significant impact on bank lending behaviour in SSA.

On the issue of bank solvency, there is some level of evidence that the level of bank stability, risk-adjusted profit and non-performing loan ratio influence the supply of loans in SSA banks. Notwithstanding that the relationship between bank lending and the Z-score is insignificant, the positive coefficient indicates that a stable bank supplies more loans. Studies have shown that a high non-performing loan on the balance sheet of banks hinders credit delivery. As expected, the coefficient of non-performing loan to total loan ratio is negative, meaning that SSA banks with high non-performing loan relative to their total loan portfolio will supply fewer loans.

The results also suggest that the activity restrictions, entry requirement, capital stringency and bank supervisory power explain the cross-country variations in bank lending. Activity restriction, entry requirement and supervisory power have a positive relationship with the supply of loans. Severe entry requirement, effective supervisory power and allowing banks to concentrate on their core business of banking promote supply of banks loans in SSA. The positive coefficient of entry requirement shows that a country with rigid entry requirements provides an avenue for quality of new entrants, leading to less financial crisis and more credit delivery. More stringent initial capital requirements hinders credit delivery to the private sector. The supervisory power has an insignificantly positive relationship with bank lending in SSA. These findings show that regulatory initiative which restricts banking activities imposes severe entry requirements and requires high regulatory capital influences banks’ decision to supply loans in SSA.

The policy-induced domestic interest rate is negative only in column 1. The effect of monetary policy shocks on bank lending among banks in SSA shows that tightening of monetary policy stance reduces supply of bank credit in SSA especially when the bank-specific variables are controlled. For instance, 1 per cent increase of interest rate reduces bank credit to private sector by at least 3.5 per cent. This finding suggests that increasing policy interest rates reduces bank lending. The result is consistent with monetary policy theory and confirms existing empirical research that shows that bank lending increases when monetary policy stance is relaxed. Surprisingly, the monetary policy stance is positive in column 3 (where bank stability variables are controlled), meaning that stable banks increase supply of loans when monetary policy stance is tightened. With respect to the influence of macroeconomic factors on each bank lending, the study reveals that an increase in GDP growth increases bank lending in SSA. Our findings are consistent with Quagliariello’s (2009) result that in Italy, during economic booms, banks tend to expand their lending activity, often relaxing their selection criteria resulting in an increased bank lending. Inflation does not seem to influence supply of bank loans. Inflation only impacts on bank lending when market structure variables are used as explanatory variables. The non-impact of inflation and insignificance of GDP growth in columns 2 and 3 on supply of loan is consistent with studies on SSA. For instance, Al-Haschimi (2007) finds that macroeconomic environment has only limited effect on SSA banks’ margins and for that matter supply of loan.

Preferred Model Estimation Results

The term ‘preferred model’ is meant to reveal the impact of banking freedom and financial reforms on bank credit to private sector. Here, Equation (1) is estimated emphasising how banking freedom and financial reforms in addition to the bank-specific, market structure and macroeconomic variable influenced banks’ supply of loans. The result is presented in three columns. Column 1 is the preferred model. In column 2, banking freedom and financial reform index are included. The objective is to find out whether a country that has reformed its financial sector and allows bank to operate freely influences credit delivery. In column 3, SA is excluded from the estimation. The results are presented in Table 5.

Table 5.

Determinants of Bank Credit Delivery in SSA: Preferred Model

Dependent Variable: Bank Credit to Private Sector
	(1)	(2)	(3)
Bank credit to private sector	0.4643***	0.4509***	0.4576***
	(0.0261)	(0.0290)	(0.0295)
Bank size	0.5264***	0.5347***	0.5314***
	(0.0268)	(0.0295)	(0.0298)
Excess liquidity	-0.6399***	-0.6512***	-0.6887***
	(0.0451)	(0.0516)	(0.0537)
Efficiency	0.3499**	0.2881**	0.3433***
	(0.1039)	(0.1166)	(0.1199)
Bank growth	0.2178***	0.2257***	0.1836***
	(0.0357)	(0.0380)	(0.0384)
Concentration	-0.3083***	-0.0505	0.0386
	(0.0625)	(0.1022)	(0.1053)
Bank density	-0.0075**	0.1021***	0.1596**
	(0.0035)	(0.0237)	(0.0282)
Z-score	0.0342**	0.0349***	0.0318**
	(0.0113)	(0.0115)	(0.0128)
Bank bad loan ratio	-0.0071	-0.0031	-0.1014
	(0.0839)	(0.0857)	(0.0905)
Bank capitalisation	-0.2780***	-0.2867**	-0.1617
	(0.1044)	(0.1260)	(0.1483)
Activity restriction	0.0079	0.0115	0.0208***
	(0.0053)	(0.0073)	(0.0076)
Entry restriction	-0.0540***	-0.0359*	-0.0436**
	(0.0170)	(0.0191)	(0.0192)
Regulatory capital requirements	0.0289***	0.0073	-0.0045
	(0.0079)	(0.0100)	(0.0103)
Banking freedom		0.0002	0.0008
		(0.0008)	(0.0008)
Financial reforms		0.0150**	0.0244***
		(0.0070)	(0.0078)
Monetary policy indicator	-0.0021***	-0.0046**	-0.0027
	(0.0008)	(0.0019)	(0.0019)
GDP growth	0.0040**	0.0094***	0.0071**
	(0.0018)	(0.0030)	(0.0030)
Inflation	-0.0014*	-0.0010	-0.0029*
	(0.0008)	(0.0015)	(0.0015)
Diagnostic tests
Number of observations	636	519	476
R ²	0.9879	0.9901	0.9865
Wald chi²	30,367.75	31,423	21,618.77
p-value	0.0000	0.0000	0.0000
Hausman (chi²)	935.12	1,253.08
P-value	0.0000	0.0000

Notes: The estimation is based on data for 24 SSA countries using random effects GLS estimators. The dependent variable is bank credit to private sector. Constant term included but not reported. Standard errors are reported in parentheses. ***, ** and * indicate statistical significance at the 1 per cent, 5 per cent and 10 per cent level, respectively. All regressions include monetary policy stance and two macroeconomic variables.

As reported in Table 4, credit delivery in SSA is to some extent influenced by some bank-specific, market structure, bank stability and contestability variables. The results in Table 5 column 1 are similar to the results in Table 4 except the Z-score and the bank capitalisation. The coefficient of bank capitalisation is negative and significant indicating that credit delivery is decreased in a country in SSA where banks’ capitalisation level decreases. The results demonstrate that in SSA capitalisation level is used for stability purposes and not that of credit. Furthermore, in column 2 where banking freedom and financial reforms index have been included in the equation, the results show that, though the banking freedom index is insignificant, the financial reform index and the banking density are positive and statistically significant. This result shows that the number of banks per country’s population alone does not increase credit delivery unless the financial sector is reformed and the banks are free to conduct their activities. These findings suggest that in a country with high bank density, operating its banking activities freely and openly and which has liberalised its financial systems, banks loans turn to increase even when monetary policy stance is tightened. A test of consistency is conducted by excluding SA data. The results provided in column 3 are relatively the same in terms of the sign and the magnitude of that of the results in column 2. Thus, inclusion of the SA data in the estimation process does not necessarily bias the findings.

Determinants of Bank Credit in SSA: Bank Types versus Level of Income

This subsection provides further implication on how bank types and the income level affect bank credit delivery to private sector in SSA. To achieve this, bank type (a dummy variable denoting either commercial or development bank) and the income level (a dummy variable representing either low- or middle-income countries) are added to Equation 1. The results are presented in Table 6 and in columns: column 1 for commercial banks; column 2 for development banks; column 3 for low-income economies; and column 4 representing middle-income countries in SSA. The results are similar to that of the findings presented in Table 5. The coefficient of commercial banks is negative and statistically significant, meaning that commercial banks in SSA do not provide credit to the private sector. The possible explanation of this result is that banks in Africa especially the commercial banks prefer trading in commercial and government securities than extending credit to the private sector. Contrary to the result of the commercial banks, the majority of the bank loans in Africa are provided by the development banks. On the level of development, the result shows that banks in the low-income economies in SSA supply very few loans to the private sector. However, those operating in middle-income countries in Africa extend more credit to the private sector. The results therefore suggest that as the level of a country’s income improves, banks take advantage of the perceived investment opportunities and respond positively to the loan demand.

Table 6.

Determinants of Bank Credit in SSA: Bank Types versus Level of Development

Dependent Variable: Bank Credit to Private Sector
	(1)	(2)	(3)	(4)
Bank credit to private sector_-1	0.4490***	0.4393***	0.4395***	0.4395***
	(0.0290)	(0.0290)	(0.0292)	(0.0292)
Commercial banks	-0.0667**
	(0.0292)
Development bank		0.1787***
		(0.0505)
Less-developing countries			-0.0745**
			(0.0316)
Middle-income countries				0.0745**
				(0.0316)
Monetary policy indicator	-0.0049**	-0.0046**	-0.0049**	-0.0049**
	(0.0019)	(0.0019)	(0.0019)	(0.0019)
GDP growth	0.0095***	0.0097***	0.0105***	0.0105***
	(0.0030)	(0.0030)	(0.0030)	(0.0030)
Inflation	-0.0012	-0.0010	-0.0008	-0.0008
	(0.0015)	(0.0015)	(0.0015)	(0.0015)
Diagnostic tests
Number of observations	581	518	519	519
R ²	0.9903	0.9904	9,902	9,902
Wald chi²	31,818	32,296.05	31,582.48	31,582.48
P-value	0.0000	0.0000	0.0000	0.0000
Hausman (chi²)	543.61	479.53	955.23	955.23
P-value	0.0000	0.0000	0.0000	0.0000

Notes: The estimation is based on data for 24 SSA countries using random effects GLS estimators. The dependent variable is bank credit to private sector. Bank-specific, structure and contestability variables and constant term included but not reported. Standard errors are reported in parentheses. ***, ** and * indicate statistical significance at the 1 per cent, 5 per cent and 10 per cent level, respectively. All regressions include monetary policy stance, regulatory and macroeconomic variables.

Determinants of Bank Credit in SSA: Regional Analysis

Several variations are made to Equation (1) with the aim of testing its robustness. The first is the grouping of SSA’s banks into three zones and the second variation is to employ system general method of moments (GMM) estimators. SSA countries are categorised into three zones—Economic Community of West African States (ECOWAS), the East African Community (EAC) and the Southern African Development Community (SADC).² This allows us to examine whether there is a regional difference of banks loans to private sector.

For the system GMM, dynamic panel-data estimation, two-step system GMM, Windmeijer-correct standard error, small sample adjustments and orthogonal deviation estimators are employed. It addresses any endogeneity issues related to benchmark estimations. Several diagnostic tests are conducted to ensure that the models are fits and the estimations are precised and consistent. The system GMM according to Roodman (2009) exploits the time series element of the data, controls for firm-specific effects, allows for the inclusion of lagged dependent variables as regressors and controls for the endogeneity of explanatory variables. As a result, diagnostic tests are conducted to check the validity of the results.³

Table 7.

Regional Analysis of Bank Lending Determinant Using Bank-specific Variables

Dependent Variable: Bank Loan
	ECOWAS	EAC	SADC	ALL
Ln loan_t-1	0.59816***	0.63142***	0.67162***	0.72838***
	(0.06810)	(0.07513)	(0.16246)	(0.07025)
Bank size	0.43100***	0.32908***	0.3212*	0.27340***
	(0.07569)	(0.08122)	(0.17117)	(0.07399)
Excess liquidity	-0.49612**	-0.34720*	-0.58039	-0.49437***
	(0.21313)	(0.19318)	(0.37913)	(0.17397)
Management efficient	0.7315814	0.2544774	1.5509***	0.85714***
	(0.51065)	(0.24812)	(0.52740)	(0.28847)
Bank growth	0.38946***	0.1651815	0.52565*	0.58062***
	(0.12085)	(0.19481)	(0.26338)	(0.17388)
Capitalisation	-1.17558**	-0.40789*	0.155859	-0.02239
	(0.57627)	(0.24382)	(0.28698)	(0.29448)
Monetary policy	0.0591361	-0.02373*	-0.01582	-0.03695**
	(0.07112)	(0.01257)	(0.04994)	(0.01893)
Log GDP per capita	0.0333356	-0.0603493	-0.00176	-0.02568*
	(0.02031)	(0.03713)	(0.02196)	(0.01372)
Log inflation	0.02009	0.03641	0.085	0.06960***
	(0.02639)	(0.04407)	(0.08167)	(0.01894)
Constant	-0.94451**	0.12103	-1.5393	-0.77676
	(0.40009)	(0.35014)	(0.56066)	(0.36981)
Diagnostic tests
Number of observations	326	243	367	936
Number of instruments	135	114	138	85
Wald test	10,221	6,506.11	8,957.15	3,978.03
P-value	0.0000	0.0000	0.0000	0.0000
Hansen	59.82	40.64	71.39	74.30
P-value	1.000	1.000	1.000	0.371
AB2	-0.21	0.26	1.49	-1.37
P-value	0.835	0.792	0.136	0.172

Notes: Two-step system GMM, Windmeijer-correct standard error, small-sample adjustments and orthogonal deviation. The dependent variable is bank loan. Robust standard errors are reported in parentheses. ***, ** and * indicate statistical significance at the 1 per cent, 5 per cent and 10 per cent level, respectively. The results are presented in columns, basing on the regional groupings. ECOWAS is Economic Community of West African States and countries included for our analysis are Benin, Burkina Faso, Cameroon, Cote D’ivoire, Ghana, Nigeria, Senegal, Sierra Leone and Togo. The EAC is Eastern African Community and countries included for analysis are Kenya, Uganda, Tanzania and Ethiopia. The SADC refers to the Southern African Development Community and made up of Angola, Botswana, Malawi, Madagascar, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Zambia and Zimbabwe. All regressions include monetary policy stance and two macroeconomic variables. We reported following diagnostic test: (a) the instruments counts; (b) the Hansen test for over identification restriction (which the null hypothesis is that instruments are exogenous); (c) the Arellano–Bond tests for first and second order serial correlation in residuals (which null hypothesis is that there is no serial correlation; and (d) the Wald test for joint significant of parameters.

Table 7 shows that bank size influences bank credit delivery. The coefficient is positive indicating that the bigger the bank, the higher it is in position to supply loans. Banks with excess liquidity within ECOWAS and EAC increase their supply of loans to private sector. Management efficiency only significantly influences credit delivery of banks within SADC region. This means that more loans are extended to private sector in SADC where management is most efficient. With regard to bank market structure variables, the results show that the parameter of the concentration is negative and statistically significant only of the banks in ECOWAS, indicating a strong relationship between bank lending and concentration in ECOWAS. The negative relationship shows that in ECOWAS the supply of loan increases when the level of competition increases. While concentration is only significant in ECOWAS, bank density is also negatively related to credit delivery only with banks in Eastern African countries. This finding suggests that there are more banks relative to the population in EAC and this enables banks to invest more in the private sector. With regard to the degree of market power of a bank in the domestic markets, the study reveals that the greater the share a bank possessed, in ECOWAS and SADC countries, the lesser the banks are in the position to supply loans. This finding suggests that the banks’ market power influences bank lending behaviour in SSA except banks in EAC. This is presented in Table 8.

Table 8.

Regional Analysis of Bank Lending Determinant Using Bank Market Structure Variables

Dependent Variable: Bank Loan
	ECOWAS	EAC	SADC	ALL
Bank loan to private sector_t-1	0.97053***	0.83486***	0.93957***	0.96944***
	(0.03669)	(0.09975)	(0.02623)	(0.01823)
Concentration	-0.56467**	-0.5912843	-0.29378	0.18319*
	(0.26748)	(0.40384)	(0.18807)	(0.10717)
Bank density	0.0668974	-0.12949*	0.01034	-0.00751**
	(0.04779)	(0.06865)	(0.00791)	(0.00365)
Bank market power	-0.2885***	-0.0501179	-0.22562***	0.0849388
	(0.10820)	(0.33244)	(0.07312)	(0.14916)
Monetary policy	-0.0256618	-0.10406**	0.0471692	-0.0111472
	(0.08056)	(0.04625)	(0.04565)	(0.01463)
Log GDP growth	0.0410424	0.0504968	0.09808*	0.0526924
	(0.03202)	(0.05045)	(0.05550)	(0.03295)
Log inflation	0.0283433	0.0339351	0.19412***	0.07454***
	(0.02332)	(0.11955)	(0.07117)	(0.02395)
Constant	0.90945	1.2893	0.00977	0.10699
	(0.46387)	(0.5376)	(0.19686)	(0.12987)
Diagnostic tests
Number of observations	448	343	425	1,112
Number of instruments	85	63	91	111
Wald test	3,978.03	2,345.56	2,599	5,767.12
P-value	0.0000	0.000	0.000	0.000
Hansen	74.30	52.35	77.59	119.58
P-value	0.371	0.345	0.460	0.060
AB2	-1.37	-0.44	1.51	1.17
P-value	0.172	0.661	0.132	0.243

Table 9 shows that bank solvency matters in credit delivery in SSA. There is evidence to suggest that the level of bank stability and risk-adjusted return on equity affect private loans only of banks in ECOWAS. It therefore means that bank stability (in terms of higher Z-score and higher profitability) has an influence on bank credit in EAC and SADC. As expected, the coefficient of non-performing loans to total loan ratio is negative, meaning that all banks in SSA with high non-performing loans relative to their total loan portfolio will supply fewer loans.

The coefficients of the activity restrictions, entry requirement and capital stringency to a large extent can explain the cross-regional variations in bank lending. Supply of bank loan is hindered in ECOWAS where banks are not allowed to freely operate in non-banking activities. The opposite is true of banks in EAC and SADC where more loans are extended when banks are allowed only to concentrate on their core banking activities. It should however be noted that the relationship between bank activity restriction and bank lending is insignificant in SADC. Entry requirement has a positive relationship with the supply of loans in SADC. This means that severe entry requirements promote the supply of bank loans in SADC. The positive coefficient of entry requirement shows that the rigid entry requirement in SADCs provides an avenue for quality new entrants, leading to less financial crisis and more credit delivery. The supervisory power has a significant and positive relationship with bank lending in ECOWAS and EAC and insignificant but equally positive relationship with bank lending in SADC. These findings show that regulatory initiative which restricts banking activities imposes severe entry requirements, requires high regulatory capital and provides efficient supervisory services, influences only banks in ECOWAS to supply loans. The results are presented in Table 10.

Table 9.

Regional Analysis of Bank Lending Determinant Using Bank Stability Variables

Dependent Variable: Bank Loan
	ECOWAS	EAC	SADC	ALL
Bank loan to private sector_t-1	0.95548***	0.94272***	0.97014***	0.97363***
	(0.03995)	(0.04879)	(0.01468)	(0.03461)
Z-Score	0.14050**	0.0963848	-0.0211661	0.12096**
	(0.06681)	(0.06906)	(0.07261)	(0.06170)
Shape ratio	-0.15860***	-0.0629262	-0.0238231	-0.1688***
	(0.03925)	(0.04156)	(0.04304)	(0.04805)
Bank bad loans	-1.21452*	-0.75771**	-0.967891*	-0.8972***
	(0.70740)	(0.34453)	(0.54475)	(0.31843)
Monetary policy	0.214091**	-0.04896***	0.1438128	-0.0024781
	(0.09758)	(0.01520)	(0.09552)	(0.03805)
Log GDP per capita	0.0277168	0.0083949	-0.0077643	0.0165674
	(0.04366)	(0.03462)	(0.03011)	(0.02908)
Log inflation	-0.0357185	0.0000319	0.0858138	0.0503972
	(0.04930)	(0.06158)	(0.12178)	(0.04555)
Constant	0.09168	0.46192	0.02805	0.21152
	(0.26450)	(0.29639)	(0.2442)	(0.2617)
Diagnostic tests
Number of observations	196	177	158	531
Number of instruments	81	65	83	83
Wald test	3,236.04	2,056.13	8,095.44	3,020.71
P-value	0.0000	0.0000	0.0000	0.0000
Hansen	38.00	34.11	35.19	78.05
P-value	0.998	0.974	1.000	0.213
AB2	0.71	1.03	1.13	1.52
P-value	0.481	0.301	0.258	0.129

Table 10.

Regional Analysis of Bank Lending Determinant Using Contestability Variables

Dependent Variable: Bank Loan to Private Ssector
	ECOWAS	EAC	SADC	ALL
Bank loan to private sector_t-1	0.92829***	0.98505***	0.88798***	0.9644***
	(0.06145)	(0.01581)	(0.05150)	(0.08344)
Activity restriction	-0.98985***	8.2110**	0.1512489	0.196402
	(0.39698)	(3.9977)	(0.32486)	(0.23951)
Capital requirement	0.126596**	-3.5552**	-0.0773074	-0.93065**
	(0.06081)	(1.6336)	(0.10607)	(0.38136)
Banking supervision	1.36235***	21.44193**	0.1436672	0.7104136
	(0.52688)	(9.8260)	(0.22918)	(0.60967)
Entry restriction		-23.5227**	2.6949***	1.40275***
		(11.0488)	(0.84466)	(0.44339)
Monetary policy	-0.13086	-0.08079***	0.0029186	-0.0671***
	(0.19826)	(0.02441)	(0.06780)	(0.02312)
Log GDP per capita	-0.01571	0.0537047	-0.0350052	-0.06298**
	(0.02929)	(0.133267)	(0.02649)	(0.02987)
Log inflation	-0.03191	0.0297355	0.1209398	0.06745*
	(0.02292)	(0.08179)	(0.08770)	(0.03702)
Constant	-0.04981	22.7535*	-5.6182	-3.42473
	(0.79071)	(10.9067)	(2.3804)	(2.0380)
Diagnostic tests
Number of observations	357	253	342	1,015
Number of instruments	29	29	46	50
Wald test	3,498.53	13,793.11	944.13	1,124.02
P-value	0.0000	0.0000	0.0000	0.0000
Hansen	15.66	20.19	35.17	42.46
P-value	0.405	0.165	0.277	0.181
AB2	-0.79	0.53	0.59	0.15
P-value	0.429	0.594	552	0.880

Conclusion

This article contributes to the empirical literature on bank lending behaviour in SSA. The broad determinants of the supply of bank loans are analysed. Both micro-bank level and macro-country level data employed from a sample of 264 banks across 24 SSA countries are analysed. The analyses are presented in three subsections. The first subsection examines cross-country determinants of bank lending; the second subsection includes financial reform and banking freedom index into the main equation as ‘the preferred model’; and the last subsection examines the regional determinants of bank credit.

The entire countries’ bank-specific variables sampled statistically influence bank credit to private sector. Credit delivery to private sector in SSA is influenced by the size of the bank, the liquidity position of such bank, the growth level as well as the efficiency of the management of the banks. The coefficient of bank size, its growth variable and efficiency is positive demonstrating that in SSA, bigger and most growing banks provide credit to the private sector. The results also show that banking entry requirements, supervisory power and capital stringency to large extent explain the cross-country variations in bank lending. Supply of bank loans is not hindered in a country where banks are not allowed to freely operate in non-banking activities. Thus, these findings show that regulatory initiative, which imposes severe entry requirements, provides effective supervisory power and requires high regulatory capital requirements, influences banks’ decisions to supply loans in SSA.

The results support the fact that banking system structure explains bank lending behaviour only when the financial sector is reformed and the banks are allowed to operate freely. The study reveals that in countries where banks are most concentrated, the supply of loan is less. However, there is evidence to support that bank density increases supply of loans. On the issue of bank solvency, there is no evidence that the level of bank stability and risk-adjusted profit influence the supply of loans in SSA banks. The results also reveal that monetary policy significantly influences bank lending in SSA. The negative relationship suggests that increasing policy-induced interest rates reduces bank lending. The result is consistent with monetary policy theory and confirms existing empirical research that shows that bank lending increases when monetary policy stance is relaxed. The level of economic activity does affect bank lending especially in an environment where the financial sector has been reformed and bank density is high.

On how bank types and the income level affect bank credit delivery to private sector in SSA, our results show that banks in the low-income economies in SSA supply few loans to the private sector but those operating in middle-income countries in Africa extend more credit to the private sector. The results therefore suggest that as the level of a country’s income improves, banks take advantage of the perceived investment opportunities and respond decisively to the loan demand.

With regard to the regional analyses, the article sets out to identify whether there are regional differences of bank credit delivery in SSA using bank-specific, bank market structure, bank stability and contestability variables. The result shows that management efficiency only influences credit delivery of banks operating in SADC region. This means that more loans are therefore extended to the private sector in SADC where management is most efficient. While concentration is only significant in ECOWAS, bank density is also negatively related to credit delivery only with banks in EAC. This finding suggests that there are more banks relative to the population in EAC and this, according to the results, enables banks to invest more in the private sector. With regard to the degree of market power of a bank, the study reveals that the more the share a bank possesses, in ECOWAS and SADC countries the lesser the banks are in the position to supply loans. This finding suggests that the bank’s market power matters in bank lending behaviour in SSA except banks in EAC. There is evidence to suggest that the level of bank stability and risk-adjusted return on equity affect private loans only of banks in ECOWAS. We find that activity restrictions, entry requirement and capital stringency to a large extent can explain the cross-regional variations in bank lending. Supply of bank loans is hindered in ECOWAS where banks are not allowed to freely operate in non-banking activities. These findings clearly demonstrate that there are different determinants of bank credit delivery across the various regions (ECOWAS, EAC and SADC) in SSA. It is therefore recommended that the policymakers adopt multifaceted region specific approaches in dealing with credit constraints facing the private sector.

The results of₹ this study have some policy implications for SSA banks. First of all, policies that improve bank-specific condition such as the bank growth, size and efficiency of the management should be pursued. Second, policy direction that increases only the number of banks per population will not improve the access of financial services, unless the entire financial sector is reformed and that banks are allowed to conduct their activities freely. Furthermore, policy implication that supports the establishment of development banks should be encouraged as the results suggest that development banks extend credit to the private sector.

Footnotes

Appendix 1.

Pair-wise Correlations Coefficients between Bank-Specific Variables

	Bank Size	Liquidity	Mgt Efficiency	Bank Growth	Z-score	Bad Loans	Capital Ratio
Bank size	1
Excess liquidity	-0.1071*	1
Efficiency	0.1688*	0.0333	1
Bank growth	0.0431	0.2086*	-0.0553*	1
Z-score		0.1039*	0.1193*	0.2197*	-0.042	1
Bad loans	-0.1626*	0.0699*	-0.1571*	-0.187*	-0.182*	1
Capital ratio	-0.2240*	-0.0221	-0.0807*	-0.084*	0.1689*	0.1804*	1

Note: *Significant at 5 per cent level.

Appendix 2.

Pair-wise Correlation Coefficients between Country Level Variables

	C3 (%)	Liquidity	Activity	Entry	Regulatory Capital	Banking Freedom
C3 (%)	1
Bank density	-0.1152*	1
Activity	0.1710*	0.0197	1
Entry	-0.2463*	-0.0916*	-0.3354*	1
Reg. capital	-0.2847*	-0.0726*	-0.2923*	0.2413*	1
Bank. freedom	-0.1557*	0.3869*	-0.1886*	0.1077*	0.1454*	1
Fin. reforms	-0.5951*	0.1407*	-0.3109*	0.5007*	0.2308*	0.2853*
M. policy	0.0615*	-0.1505*	0.1524*	0.1532*	-0.1497*	-0.2196*
GDP growth	0.2089*	-0.0385	0.2559*	0.003	-0.2175*	-0.0419
Inflation	0.0475*	-0.0801*	0.1519*	0.0764*	0.0076	-0.2122*

Note: *Significant level at 5 per cent level.

Acknowledgements

The author thanks Victor Murinde, Ernest Aryeetey, Machiko Nissanke, Chris Adam, Laurence Harris, Amadou Sy, Kaddour Hadri, Catherine Pattillo and all the participants of Biannual African Economic Research Consortium (AERC) workshop for their helpful comments. I am also grateful to AERC for funding this research.

Notes

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