Abstract
We test the bank lending channel of monetary policy in Africa and examine the role of bank cost efficiency in this relationship. We use the stochastic metafrontier approach to estimate cost efficiency scores of 447 commercial banks in Africa. The fixed effect (FE) estimator is used as the baseline estimation method. The 2SLS instrumental variables (IV) and two-step system GMM approaches are used as main estimation techniques to control for endogeneity. The results consistently show the existence of the bank lending channel in Africa: thus, bank credit responds to changes in monetary policy rate. Again, we find strong evidence to show that higher cost efficiency leads to higher loan growth. The results further show that cost-efficient banks are less responsive to monetary policy shocks. The evidence suggests that bank cost efficiency weakens the bank lending channel. This implies that the effect of monetary policy on bank lending depends not only on bank size, capitalization, and liquidity as espoused in the literature but also on bank efficiency. The results are robust in formal sample-splitting. Policy implications are discussed.
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Notes
Let \(lnL\left( \theta \right) \) be the log-likelihood function of Eq. (10). The standard ML estimators of \(\theta , \hat{\theta }\), have the inverse of the Fisher information matrix \(I\left( \theta \right) =-E\left( \frac{\partial ^2lnL\left( \theta \right) }{\partial \theta \partial \theta ^T}\right) \) as the covariance matrix of \(\hat{\theta }\). However, the covariance matrix of the quasi-maximum likelihood estimators has the so-called sandwich form: \(I^{-1}\left( \theta \right) [S(\theta )S^t(\theta )]I^{-1}\theta \) where \(S(\theta )=E(\partial \ nL\left( \theta \right) /\partial \theta )\) is the score function. Johnston and DiNardo (1997: pp. 428–430) provide a brief discussion of the quasi-maximum likelihood estimation of misspecified models and the derivation of the covariance matrix.
As a robustness check on the possible endogeneity of this concentration variable rising from the concern of one reviewer, we tested the effect of the inclusion or otherwise of this variable by correlation analysis between the efficiency scores. The results are provided in Table 12 under Appendix A. The correlation shows no statistical difference with the two scores having near-perfect correlation with or without the inclusion of the concentration variable. As a result, the inclusion of this variable on endogeneity effect is marginalized.
The predict bc option of the sfpanel is used to generate the efficiency scores. This option estimates the cost efficiency scores following Battese and Coelli (1988) via \(E\ \left\{ exp\left( \varepsilon \right) \right\} \).
A list of the countries is indicated under Appendix A
We thank an anonymous referee for suggesting this additional test
We also create dummy variables of the percentiles of cost efficiency and estimate using the 2SLS given that the interaction between MP and EFF resulted in weak identification.
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Acknowledgements
We are grateful to Subal C. Kumbhakar (Editor-in-Chief), and two anonymous referees for their insightful comments which improved the work. We thank Matthew Ntow-Gyamfi for discussions at various stages of the work which also improved the work. We thank Lawrence Ansah-Addo and Emmanuel Joel Aikins Abakah for their support in the data collection. The usual disclaimer applies.
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Appendices
Appendix A (countries and summary statistics)
List of Countries
Angola, Algeria, Botswana, Benin, Burkina Faso, Cape Verde, Cameroon, Congo, Dem. Rep., Cote d’ivoire, Egypt, Arab Rep., Eswatini, Ethiopia, Gabon, Ghana, Kenya, Lesotho, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe.
Variable Description and Descriptive statistics
Table 8 presents the variables used and their sources. In Table 9, the descriptive statistics of these variables are presented. From the table, the mean total cost of the continent is around US$152 million. The Southern African region has the highest mean total cost of around US$314 million with the Central African region having the least mean total cost of around US$ 30 million. The varying degrees of bank cost allows the study to better appreciate the difference in the management of bank costs by different banks with different cost structures. This ensures that the cost efficiency scores are not biased for either banks with higher costs or those with lower costs.
The results also show that the mean loans for the continent is around US$ 1.2 billion again with the Southern African region recording the highest mean total loans of around US$2.8 billion followed by North Africa with a mean total loans of around US$1.5 billion. Again, the Central African region recorded a mean total cost of US$215 million. These suggest that the southern African region has the largest market followed by North Africa with Central Africa being the smallest market for loans. Again, this shows the varying degrees in size of the banks in terms of giving out credit. This helps the study to test the bank lending channel through different means; both banks with small credit and large credit portfolios.
The table also shows similar mean price of labour for the continent and the sub-samples. The continent recorded a mean labour price of 0.02 with the minimum of 0.01 recorded in North Africa. The continent however recorded a mean price of capital of 197.75 with Southern Africa recording the highest price of capital of 434.45 while East Africa recorded the least of 81.73. Also, the continent recorded a mean deposit price of 3.18 with the highest deposit price of 4.36 recorded in Southern Africa and the least recorded in Central Africa.
From the table also, the mean age of banks in Africa is around 30 years. Indeed, the sub-regions seem to have relatively experienced banking markets with the oldest banks being in North Africa with a mean age of 32 years, while the youngest banks are found in Central Africa with a mean age of 21 years. The varying experience levels in the sample further strengthens the argument of accounting for this in the efficiency estimation as the study conjectures that banks learn by doing and thus more experienced banks are more likely to be cost efficient than less experienced banks.
The mean equity ratio for the continent is around 13% ranging from a low of 9% in Central Africa to a high of 14% in both North and East Africa. This presents a fairly well-represented data combining both riskier banks (low equity ratio) and less risk banks. The continent recorded a mean capital ratio (CAR) of 21% with the North and East Africa also recording the same mean ratio. The highest capitalization is seen in Central Africa with a mean capital ratio of around 32%. Southern Africa has a mean CAR of 20% a little shy away from the continent’s average. Hence, while banks in the various regions have similar levels of capitalization, the Central African region is relatively highly capitalized suggesting low risk.
The mean total assets for the continent is around US$ 2.3billion. The largest banking sector is that of Southern Africa with mean total assets of around US$ 4.4billion while the Central Africa has the smallest banking sector with a mean of US$ 433 million. The varying degrees of bank size in terms of assets could show how both small and big banks absorb monetary policy shocks. This presents a good blend for the sample. Bank concentration shows that the three-largest banks control a mean of 64% of total industry assets on the continent. Southern Africa has the highest concentrated market with a mean of 72% above the continent’s average with North Africa having 59
Concerning the monetary policy variable, on average the monetary policy rate for the continent is 9% with the Central African region recording the highest mean rate of 12%. Southern and East Africa regions also recorded double digit monetary policy rate of 10.6 and 11.4%, respectively. The lowest mean monetary policy rate is in North Africa with a rate of 5.9%. The mean central bank independence index for the continent is 0.51 with West and central Africa having higher central independence index of 0.58 above the continent’s average. The least independent central banks are in Southern Africa with a mean of 0.48.
The average Inflation (CPI) of the continent is 7.9% with the highest rate of 25.9% recorded in Central Africa. The lowest mean inflation rate is 5.3% recoded in North Africa. The variation in consumer prices in different countries and regions presents the need to account for loan demand factors. The average GDP per capita (PPP) for the continent is around US$5,200 with North Africa recording the highest mean per capita GDP of US$10,000. The least mean per capita GDP is around US$2,100 recorded in East Africa. This shows different income levels or development status of the countries included in the study. Also, the continent recorded a mean GDP per capita growth of 2.79% with the highest growth recorded in East Africa and the least growth recorded in Central Africa.
Correlation Matrix
Here, the study test for multicollinearity in the independent variables using the correlation matrix. Kennedy (2008) indicates a threshold correlation coefficient of 0.70 below which the variables are free of multicollinearity. First, the results in Table 10 show the correlation between the variables used in estimating the group-specific and metafrontier efficiency scores. The table shows that all correlation coefficients are below the 0.70 mark with the least being 0.013 between the price of deposits \((lnw_3/w_1)\) and AGE and the highest being 0.469 between Loans and GDP p.c. PPP. Table 11 similarly shows correlation coefficients below 0.700 with a least coefficient of 0.011 between GDP p.c.g and CAR and between GDP p.c.g and SIZE. The highest correlation coefficient is 0.570 which is recorded between EQUITY and CAR. Even though these are below the 0.70 threshold EQUITY and CAR are used in separate estimations as measures of firm risk.
Appendix B (robustness checks)
Here, from Tables 13 to 18, the conventional first-stage F-statistics threshold of 10 for weak instruments is exceeded in most of the estimations except for Central Africa. Also, where the suggested 104.70 first-stage F-statistics threshold of Lee et al. (2021) is not met, the “tF” procedure of Lee et al. (2021) is used and the null hypothesis (given \(t^2\) > c(F)) of weak instruments is rejected in most of the estimations.
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Dwumfour, R.A., Oteng-Abayie, E.F. & Mensah, E.K. Bank efficiency and the bank lending channel: new evidence. Empir Econ 63, 1489–1542 (2022). https://doi.org/10.1007/s00181-021-02166-5
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DOI: https://doi.org/10.1007/s00181-021-02166-5