Abstract
This study analyses the impact of OBS on banking productivity growth using a sample of 712 banks from 84 countries between 1999 and 2006. Our international setting allows us to analyse differences on the performance on banking institutions among various groups of countries with different economic and financial innovations development. We estimate cost and profit productivity growth using a parametric approach that decomposes the change in risk-adjusted cost and profit performance into two main components: changes in business conditions and changes in productivity. The latter one is decomposed further into the change in best practice and change in (in)efficiency.
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Notes
Berger and Mester (1997) argue that the alternative profit function may provide useful information and be preferred when one or more of the following conditions are applicable: (a) there are substantial unmeasured differences in the quality of banking services; (b) outputs are not completely variable; (c) output markets are not perfectly competitive; (d) output prices are not accurately measured. Based on these arguments, Kasman and Yildirim (2006) point out that in international comparisons with a diverse group of countries and competition levels it seems more appropriate to estimate an alternative rather than a standard profit function. Furthermore, DeYoung and Hasan (1998) point out that output quantities tend to vary across banks to a greater extent than input prices, thus explaining a larger portion of the variation in profits in regression analysis.
Total cost is defined as: Interest expense + Non-interest expense (i.e. expenses related to commission, fee, trading activities + personnel expenses + other operating expenses). Profit before tax is defined as: Net interest income + net commission income + net fee income + net trading income + other operating income – personnel expenses – other operating expenses – loan loss provisions +/− other adjustments (e.g. non-recurring income, non-recurring expenses, change in fair value of own debt, etc.).
We use the OBS figure reported in the global format of Bankscope, which is calculated as the summation of the nominal values of the following four categories: (1) Acceptances, (2) Documentary credits, (3) Guarantees, (4) Other contingent liabilities. Our definition of OBS is consistent with other recent studies such as Bos and Kolari (2005), Lozano-Vivas and Pasiouras (2010).
There is a debate in the literature as for the appropriate modelling of deposits. In the current paper, we use deposits as an input only. This draws on the well-known asset approach (Sealey and Lindley 1977), and it is consistent with numerous studies like Kwan and Eisenbeis (1997), Berger and DeYoung (2001), Koetter (2006), Chronopoulos et al. (2013), Lozano-Vivas and Pasiouras (2010). The findings of Fethi and Pasiouras (2010) provide further support to our approach, since their review of 151 DEA applications in bank efficiency/productivity reveals that only 20 studies use deposits as part of the output vector.
In calculating W3, we use total assets rather than the number of employees due to data unavailability. Our approach is consistent with several other studies (e.g. Altunbas et al. 2001).
We would like to thank an anonymous referee for highlighting the necessity of accounting for risk in our models, and for suggesting to us to follow this approach. The banking literature has already highlighted the association between risk and OBS (Hassan et al. 1993), while a related strand of the literature examines the association between non-interest income and risk (DeYoung et al. 2001; Stiroh 2004, 2006). Following another suggestion of the anonymous referee, we also re-estimated the models using as dependent variables the cost/equity and the profit/equity, normalized by the standard deviation of profit/equity, i.e. some sort of a Sharpe ratio, which reveals the return per unit of risk (and cost/equity per unit of risk). Obviously, in this case, equity was not part of the right hand side of the model. The conclusions remain the same. Thus, to conserve space we do not present these estimations. However, they are available from the authors upon request.
Some other studies rely on the Fourier Flexible (FF) specification to estimate efficiency (e.g. DeYoung and Hasan 1998). Berger and Mester (1997) found that both the translog and the FF function form yielded essentially the same average level and dispersion of measure efficiency, and both ranked the individual banks in almost the same order. However, Altunbas and Chakravarty (2001) compare the FF and translog specifications and urge caution about the growing use of the former to investigate bank efficiency. We therefore use the translog specification as in several other recent studies (e.g. Radić et al. 2012).
For brevity of space, we present only one of the models. In the case of Model P2 one has to replace TC with PBT and change the sign of the inefficiency term. In the case of Models C1 and P1, one has to drop Q3 from the specification.
Additionally, as in previous studies, since a number of banks in the sample exhibit negative profits (i.e. losses), the profit value in the profit model is transformed to, where is the minimum absolute value of over all banks in the sample.
We focus on publicly quoted banks because it enhances comparability across countries. Furthermore, we focus on commercial banks for two reasons. First, because it allows us to examine a more homogenous sample in terms of services, and consequently inputs and outputs, enhancing further the comparability among countries. Second, as mentioned in Demirguc-Kunt et al. (2004), since the regulatory data of the Barth et al. (2001, 2006, 2008) database are for commercial banks, it is more appropriate to use bank-level data for this type of banks, only.
This WB database is available in only three points in time. Version I was released in 2001 (Barth et al. 2001). For most of the countries, information corresponds to 1999, while for others information is either from 1998 or 2000. Version II describes the regulatory environment at the end of 2002 (Barth et al. 2006). Version III describes the situation in 2005/06 (Barth et al. 2008). Consequently, we had to work under the assumption that the scores of our regulatory variables (CAPRQ, PRMON, SPOWER, RESTR) remain constant within short windows of time. More precisely, we used information from Version I for bank observations from the period 1999–2000, from Version II for bank observations from the period 2001–2003, and from Version III for bank observations from 2004–2006. In the case of a few countries for which information was not available in all versions, we used information from the most appropriate one. While acknowledging this shortcoming, we do not believe that it has an impact on our results. Other studies that have used this database across a number of years have obviously worked under a similar assumption (e.g. Demirguc-Kunt and Detragiache 2002; Demirguc-Kunt et al. 2004; Fernandez and Gonzalez 2005; Beck et al. 2006; Pasiouras et al. 2009).
We calculate liquid assets as: total assets - fixed assets – customer loans - other earning assets. In other words, liquid assets include the following items: loans and advances to banks, reserve repos and cash collateral, cash and due from banks, trading securities and at FV through income.
The figures in Table 1 show that the average bank in major advanced countries uses OBS of the value of $10,173,387 thousands (in constant 1995 terms) compared to only $7,639,661 thousands in advanced countries, $696,434 thousands in transition countries, and $1,485,130 thousands in developing countries. However, due to the extremely larger size of banking institutions operating in major advanced countries, the average OBS to total assets ratio in Table 2 is lower than the one recorded for the rest of the groups.
Since the performance metrics that we use in the present study are adjusted for risk, this could also explain why advanced countries appear to be the ones with the best performance.
For brevity of space we present only geometric averages for the entire period (1999–2006). Results by year are available from the authors upon request.
This also means that the changes in costs due to a shift in best practise and inefficiency changes are also small and there are only marginal differences across the two models and the various groups.
It is interesting to note that the poor performance of banks in transition and developing countries appears to be in contradiction with their high level of ROA and ROE shown in Table 2. However, one should keep in mind that: (i) the two profitability ratios obtained from the raw data are not adjusted for risk. The mean risk adjusted ROA (i.e. ROA/st. dev. ROA) for major advanced countries equals 2.25, for advanced ones it is equal to 4.52, for transition ones it equals 2.61, while the corresponding figure for developing countries is 3.38. The corresponding figures in the case of the mean risk-adjusted ROE (i.e. ROE/st. dev. ROE) are: 2.31 (major advanced, 4.92 (advanced), 2.98 (transition), 3.23 (developing), (ii) the use of financial ratios to measure bank performance is not without its criticisms (Halkos and Salamouris 2004), (iii) the use of efficient frontier approaches is considered superior to the use of traditional financial ratios—such as ROA or the cost/revenue ratio—in terms of measuring performance (Berger and Humphrey 1997; Bauer et al. 1998), and (iv) as mentioned in the main body of the text the productivity measures take into account simultaneously various inputs and outputs (i.e. loans, other earning assets).
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Acknowledgment
The first author acknowledges financial support from Ministerio de Economía y Competitividad, Dirección General de Investigación y Gestión del Plan de I+D+i, grant reference ECO2011-26996. We would like to thank an anonymous referee and Haluk Unal (Editor) for various comments that helped us improve earlier versions of the manuscript. Any remaining errors are our own.
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Lozano-Vivas, A., Pasiouras, F. Bank Productivity Change and Off-Balance-Sheet Activities Across Different Levels of Economic Development. J Financ Serv Res 46, 271–294 (2014). https://doi.org/10.1007/s10693-013-0181-3
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DOI: https://doi.org/10.1007/s10693-013-0181-3