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Efficiency in Islamic and conventional banking: an international comparison

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Abstract

The paper investigates the efficiency of a sample of Islamic and conventional banks in 10 countries that operate Islamic banking for the period 1996–2002, using an output distance function approach. We obtain measures of efficiency after allowing for environmental influences such as country macroeconomic conditions, accessibility of banking services and bank type. While these factors are assumed to directly influence the shape of the technology, we assume that country dummies and bank size directly influence technical inefficiency. The parameter estimates highlight that during the sample period, Islamic banking appears to be associated with higher input usage. Furthermore, by allowing for bank size and international differences in the underlying inefficiency distributions, we are also able to demonstrate statistically significant differences in inefficiency related to these factors even after controlling for specific environmental characteristics and Islamic banking. Thus, for example, our results suggest that Sudan and Yemen have relatively higher inefficiency while Bahrain and Bangladesh have lower estimated inefficiency. Except for Sudan, where banks exhibits relatively strong returns to scale, most sample banks exhibit very slight returns to scale, although Islamic banks are found to have moderately higher returns to scale than conventional banks. While this suggests that Islamic banks may benefit from increased scale, we would emphasize that our results suggest that identifying and overcoming the factors that cause Islamic banks to have relatively low potential outputs for given input usage levels will be the key challenge for Islamic banking in the coming decades.

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Notes

  1. If output quality is not controlled for, unmeasured differences in loan quality that are not captured by banking data may be mistakenly measured as inefficiency (Berger and Mester 1997).

  2. “Netputs” are operating characteristic variables that have been made fully interactive with inputs and outputs in the function.

  3. We gratefully acknowledge an anonymous referee who highlighted this issue to us.

  4. In the literature, the translog function is preferred in estimating a parametric distance function (Fuentes et al. 2001) because it is flexible, easy to calculate and permits the imposition of homogeneity.

  5. Some current Islamic banks also practice debt-like financing such as murabaha.

  6. In some Islamic banks, deposits or equity contributed by depositors are categorised under shareholders’ funds, but some Islamic banks group them as deposits from customers, similar to conventional banks (Karim 2001).

  7. Some studies have treated equity capital as a netput in the translog function (Berger and Mester 1997; Hasan and Marton 2003; Bonaccorsi di Patti and Hardy 2005; Kraft et al. 2006).

  8. Dietsch and Lozano-Vivas (2000) and Kasman (2005) treat equity-to-assets ratio as a country-specific variable to proxy bank regulation.

  9. In the estimation, all variables are normalized around their means and linear homogeneity in outputs is imposed using the output Y2 as a numeraire.

  10. All data employed in this analysis is converted into constant international dollars according to the purchasing power parity hypothesis (Lozano-Vivas et al. 2002).

  11. This is demonstrated by a likelihood ratio test of 0.38 with 1 degree of freedom.

  12. We note that a log likelihood ratio test for the joint significance of the 6 parameters related to equity is 17.98, thus we can reject the null hypothesis that these parameters are jointly insignificant at the 99% confidence level.

  13. Bank-specific loan quality and merger dummy variables were also found to be statistically insignificant when they were included in the distance function.

  14. The finding is consistent with cost function studies in which higher population density contributes to an increase in banking costs in France and Spain (Dietsch and Lozano-Vivas 2000), and Latin American and Caribbean countries (Carvallo and Kasman 2005).

  15. All countries in the sample are developing economies except for Bahrain (World Bank 2007).

  16. The exclusion of bank size from the inefficiency specification can be rejected given a likelihood ratio test of 62.90 with 1 degree of freedom. In the rejected restricted model, Iranian banks are found to have the lowest estimated inefficiency, which is consistent with previous international studies that do not control for bank size (e.g., Brown 2003; Brown and Skully 2003). This therefore suggests that our approach of controlling for both asset size and countries in the inefficiency specification is necessary to better measure cross county differences in inefficiency.

  17. Al-Jarrah and Molyneux (2005) also found that Bahraini banks are relatively efficient when compared to Jordanian banks.

  18. Even within Sudanese banks, wide inefficiency difference exists (Hussein 2004).

  19. For example, Islamic banks in countries other than Malaysia may have a higher percentage of equity-based financing which has been controlled for in this study.

  20. Alshammari (2003) found almost constant returns to scale in banks (including Islamic banks) in GCC countries and no difference across bank types. However, Yudistira (2004) found that small and medium-sized Islamic banks in most countries have diseconomies of scale.

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Authors and Affiliations

  1. School of Economics, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia

    Mariani Abdul-Majid

  2. Economics and Strategy Group, Aston Business School, Aston University, Birmingham, B4 7ET, UK

    David S. Saal

  3. Industrial Economics Division, Nottingham University Business School, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK

    Giuliana Battisti

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  1. Mariani Abdul-Majid
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  2. David S. Saal
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  3. Giuliana Battisti
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Correspondence to David S. Saal.

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Abdul-Majid, M., Saal, D.S. & Battisti, G. Efficiency in Islamic and conventional banking: an international comparison. J Prod Anal 34, 25–43 (2010). https://doi.org/10.1007/s11123-009-0165-3

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