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International evidence on the impact of regulations and supervision on banks’ technical efficiency: an application of two-stage data envelopment analysis

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Abstract

This study uses a sample of 715 banks from 95 countries and two-stage data envelopment analysis (DEA) to provide international evidence on the impact of regulations and supervision approaches on banks’ efficiency. We first use DEA to estimate technical and scale efficiency. We then use Tobit regression to investigate the impact of several regulations related to capital adequacy, private monitoring, banks’ activities, deposit insurance schemes, disciplinary power of the authorities, and entry into banking on banks’ technical efficiency. We estimate several specifications while controlling for bank-specific attributes and country-level characteristics accounting for macroeconomic conditions, financial development, market structure, overall institutional development, and access to banking services. In several cases, the results provide evidence in favour of all three pillars of Basel II that promote the adoption of strict capital adequacy standards, the development of powerful supervisory agencies, and the creation of market disciplining mechanisms. However, only the latter one is significant in all of our specifications. While the remaining regulations do not appear to have a robust impact on efficiency, several other country-specific characteristics are significantly related to efficiency.

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Notes

  1. We were able to identify 38 cross-country studies half of them being published after 2003. These studies are summarized in the next section, while a Table providing basic information about the samples, techniques, results etc, is available from the author upon request.

  2. Some other studies that focus on individual countries also attempt to account for regulations or deregulation by introducing dummy variables (e.g. Drake et al. 2006) or splitting the sample in before and after financial reform periods (e.g. Yu and Luu 2003). Studies in the US also examine differences in regulations among states (e.g. Berger and Mester 2003). However, these studies are obviously quite different from the present one since they do not provide international evidence and they do not use variables similar to the ones used in the present study.

  3. The analysis could be extended to include economic efficiency as well. However, while technical efficiency requires only input and output data, economic efficiency also requires price data. Several papers that examine individually some of the countries (e.g. Malaysia, Japan, India) that are considered in the present study point out problems with obtaining reliable and transparent price information (Dogan and Fausten 2003; Drake and Hall 2003; Ataullah and Le 2006). In our case, due to numerous missing data on the number of employees, it was also not possible to produce an accurate measure of the labour expenses per worker. Furthermore, Bos and Kool (2006) argue that input prices derived from the bank’s own accounts are endogenous to the bank’s behaviour, including its managerial efficiency, and lead to misspecification of the efficient frontier. One should therefore use exogenous rather than endogenous prices. Thus, in the absence of accurate input prices in our study, we decided to concentrate on measures of technical and scale efficiency. Recent studies that focus on these measures of efficiency, in addition to the studies mentioned above, are Lozano-Vivas et al. (2002), Pastor (2002), Casu and Molyneux (2003), Canhoto and Dermine (2003), Ataullah et al. (2004), Das and Ghosh (2006), Drake et al. (2006), among several others.

  4. This database was initially constructed by Barth et al. (2001a) and data were mainly from 1999. It was updated in early 2004 with data from 2003 (henceforth mentioned as Barth et al. 2004b).

  5. Gonzalez (2005a) also examines a large sample drawn from 69 countries and uses DEA to estimate the efficiency of banks. However, the efficiency score is used only as independent variable, in regressions where market share and market concentration are the dependent variables. Hence, the study of Gonzalez (2005a) examines the impact of efficiency and political economy variables on market structure rather than the impact of regulations on efficiency.

  6. Some other recent studies examine the impact of these regulations on banking sector crisis (Demirguc-Kunt and Detragiache 2002; Beck et al. 2006a), banks’ risk taking behaviour (e.g. Gonzalez 2005b; Laeven and Levine 2006), credit risk ratings (Demirguc-Kunt et al. 2006; Pasiouras et al. 2006).

  7. For a discussion of these approaches, see Coelli et al. (1999).

  8. Berg et al. (1992) made the original observation and included nonperforming loans in a non-parametric study of bank production, whereas Hughes and Mester (1993) applied the concept to parametric estimations (Berger and DeYoung 1997). Other studies such as Drake (2001), Drake and Hall (2003), Drake et al. (2006) and Pasiouras (2006) have use loan loss provisions rather than equity. However, this was not possible in our case due to a large number of missing and/or negative values.

  9. Other studies (e.g. Altunbas et al. 2001; Isik and Hassan 2002, 2003; Pasiouras 2006) have used the value of off-balance sheet items rather than non-interest income. While we had data for all the banks in the sample for non-interest income, there were missing values for several banks for off-balance sheet items. We therefore decided to rely on the former, as several other studies mentioned in the text.

  10. For information on deposit insurance schemes around the world and detailed discussions, see Demirguc-Kunt and Detragiache (2002) and Demirguc-Kunt et al. (2005).

  11. These variables were selected on the basis of data availability and previous studies. It would also be worthwhile to consider additional non-financial bank-specific variables related to corporate governance or distribution network (e.g. ATMs, branches) that have not been used in the present study due to data availability. We hope that future research will consider any variables omitted in the present study.

  12. We focus on 2003 because as mentioned earlier the data in the WB database of Barth et al. (2004b) are also from 2003. Data from another WB database constructed by Beck et al. (2005) that are used in a latter stage of the analysis to control for access to banking services across countries are also from the 2003–2004 period.

  13. In a limited number of cases, that data for FOREIGN and GOVERN are missing in Barth et al. (2004b) but are available in Barth et al. (2001a) we use data from the latter one. Furthermore, all regulatory data for 6 banks listed in Shenzhen Stock Exchange and Shanghai Stock Exchange are from Barth et al. (2001a). The reason is that while data for the People’s Republic of China as a whole are available in Barth et al. (2001a), data in Barth et al. (2004b) are available only for the two special administrative regions (i.e. Hong Kong, Macau) and one of the provinces (i.e. Taiwan).

  14. Descriptive statistics for all bank-specific variables are after trimming values above and below the 99% and 1% percentiles, respectively, hence reducing the impact of outliers in second stage of the analysis (i.e. Tobit regression) while retaining all the observations in the sample.

  15. In order to check that the results are not too sensitive to outliers, we followed a procedure used, among others by Resti (1997) and Casu and Molyneux (2003). DEA was first applied on all observations in the sample. Then, all banks with an efficiency score equal to one were deleted and DEA was applied on the reduced sample. The correlation between the efficiency scores obtained on the original sample and on the reduced sample is an indicator of the robustness of the results (Casu and Molyneux 2003). Both the Pearson and Spearman rank correlation coefficients were statistically significant at the 1% level, indicating that the results obtained on the full sample were not influenced by outliers (at least to a large extent).

  16. We focus on PTE for two reasons. First, it corresponds to the efficiency score under VRS which is the most frequently adopted assumption in recent studies. Second, the results of the first stage indicate that indeed most of the banks in our sample experience VRS rather than CRS. However, Tobit regressions were also performed with OTE as the dependent variable. The results were fairly similar and are available from the author upon request.

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Acknowledgments

I would like to thank an anonymous reviewer as well as seminar participants at the University of Bath and Coventry University for their helpful comments and suggestions. Any remaining errors are of course my own.

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    Fotios Pasiouras

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Pasiouras, F. International evidence on the impact of regulations and supervision on banks’ technical efficiency: an application of two-stage data envelopment analysis. Rev Quant Finan Acc 30, 187–223 (2008). https://doi.org/10.1007/s11156-007-0046-7

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