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Banking efficiency and its determinants in selected frontier african markets

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Abstract

The degree of banking efficiency is of key importance as this has significant implication on the stability of financial systems and ultimately impacts on an economy. In this paper, we extend the existing literature by measuring the degree of bank efficiency in ten frontier African countries. We also attempt to analyse the determinants of banking efficiency in the sample countries. We employ a bank-level panel data set over the period 2008–2012 to measure banking efficiency in a two-stage procedure. In the first stage, we use the Data Envelopment Analysis technique to estimate technical, pure technical and scale bank efficiency. In the second stage, we use Simar and Wilson (J Econom 136:31–64, 2007) truncated bootstrapping approach to analyse the determinants of banking efficiency. The results of our analysis show that, to a greater extent, banks in the countries studied have efficient banking sectors. The results of truncated regression indicate that bank size is negatively related to banking sector efficiency while the degree of risk is positively related bank efficiency. Overall, the present study provides empirical information that may be used to guide future financial reform policies in the Frontier African countries.

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Notes

  1. The Frontier African countries used in the study are Ghana, Uganda, Tanzania, Tunisia, Morocco, South Africa, Botswana, Mauritius, Kenya and Nigeria. From South Africa, 22 banking institutions were selected. The sample from Tanzania and Uganda each comprises of 15 licensed banking institutions respectively. The other frontier African countries all have on average above 7 banking institutions.

  2. The SFA was developed by Aigner, Lovell and Schmidt (1977) and Meeusen and Van den Broeck (1977). The SFA specifies a functional form for the cost or profit production frontier and allows for random error. Further, the predicted standard cost function is assumed to characterize the frontier while any inefficiency is captured in the error term, which is by structure orthogonal to the predicted frontier (Ferrier and Lovell, 1990).

  3. This approach was conceptualized, formulated, developed by Deprins and Simar (1984) and extended by Lovell et al. (1994).

  4. The number of banks per each of the sample countries is presented in Appendix B. Data was not available to cover the period up to 2015.

  5. The actual coverage probabilities of these bootstrap confidence interval methods however, may not coincide with their nominal coverage probabilities. The difference between the actual coverage rate of a confidence interval and the claimed value 1 − 2a is defined as the coverage error.

  6. Table 2 in Appendix shows Financial ratios used as explanatory Variables.

  7. We carry out a sktest for normality and we do not reject the null hypothesis thus concluding that the distributions are normal. See “Appendix 5”.

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Authors and Affiliations

  1. Graduate School of Business, University of Cape Town, Cape Town, South Africa

    Roland Banya & Nicholas Biekpe

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  1. Roland Banya
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Correspondence to Nicholas Biekpe.

Appendices

Appendix 1

See Table 6.

Table 6 Variables used in the Computation of Bank Efficiency
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Appendix 2

See Table 7.

Table 7 Financial ratios used as explanatory variables
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Appendix 3

See Table 8.

Table 8 Classification of frontier African countries by provider
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Appendix 4

See Table 9.

Table 9 Summary stats of efficiency scores
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Appendix 5

See Table 10.

Table 10 Results from sktest for Normality
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Banya, R., Biekpe, N. Banking efficiency and its determinants in selected frontier african markets. Econ Change Restruct 51, 69–95 (2018). https://doi.org/10.1007/s10644-016-9200-3

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