Abstract
Banking sectors in transition economies have experienced major transformations throughout the 1990s. While some countries have been successful in eliminating underlying distortions and restructuring their financial sectors, in some cases financial sectors remain underdeveloped and the rates of financial intermediation continue to be low. We estimate indicators of commercial bank efficiency by applying a non-parametric estimation technique, data envelopment analysis (DEA), to bank-level data from a wide range of transition countries. In addition to stressing the importance of some bank-specific variables, the censored Tobit analysis suggests that: (1) foreign ownership with controlling power and enterprise restructuring enhance commercial bank efficiency; (2) the effects of prudential tightening on the efficiency of banks vary across different prudential norms; and (3) consolidation is likely to improve efficiency of banking operations. Overall, the results confirm the usefulness of DEA for transition-related applications and shed some light on the question of the optimal architecture of a banking system.
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Notes
de Melo et al. (1997) and, subsequently, Havrylyshyn and van Rooden (2003) show that although the initial conditions were important in defining the difference in performance across countries, their significance diminished over time.
Exceptions are Claessens (1996) and, more recently, Barth et al. (2001) and Asaftei and Kumbhakar (2005).
There is, however, a large number of studies that look at the impact of ownership and management on bank efficiency and productivity in various transition economies. Recent country-specific research includes Podpiera and Pruteanu (2005) on Czech Republic; Hasan and Marton (2003) on Hungary; and Jemric and Vujcic (2002) on Croatia. In addition, studies by Bonin et al. (2005), Dimova (2004), Fries and Taci (2005), Stavárek (2003), and Weill (2003) look at cross-country data. While highlighting the differences in banking sector privatisation and restructuring in sample economies, these studies show a strong impact of foreign ownership on banking efficiency, while offering evidence that domestically owned private banks are not necessarily more efficient than their state-owned counterparts.
Regulations (or their easing) may also have unintended consequences. For instance, allowing banks to collect rents by imposing less stringent regulations may have the potential of deterring them from taking excessive risks.
Their method is based on the assumption that the production units have constant returns to scale. Banker et al. (1984) later relaxed the assumption and proposed a model with variable returns to scale. Theoretical extensions of these methods and empirical applications are discussed in Seiford (1996) and Cooper et al. (2000).
See Berger and Humphrey (1997) for a detailed survey.
X=[x1,…,x N ] is a (K × N) input matrix with columns x i and Y=[y1,…,y N ] is an
output matrix with columns y i .
The efficiency indexes calculated in such a way are termed overall technical efficiency indexes and can subsequently be decomposed into pure technical and scale efficiency indexes, to help identify the source of inefficiency of each sample DMU. Doing so, however, is outside of the scope of this paper and could be accomplished by subsequent research. See Rezvanian and Mehdian (2002) for design and Grigorian and Manole (2005) for an application of those indices.
Various versions of the DEA are used for monitoring and/or early warning systems used by bank regulatory agencies (see Barr et al. (1994), and Brockett et al. (1997)).
See Berger and Humphrey (1997) for a detailed survey.
In addition to BankScope data, with the help of World Bank field staff, we collected data on banks' employment and foreign ownership. Unless otherwise noted, the current dollar values of financial indicators (as reported by BankScope) are used.
Belarus and Ukraine would be the exceptions.
Different approaches in choosing the inputs and outputs are presented in Berger and Humphrey (1997). A comparative analysis of asset and value-added approaches is conducted in Tortosa-Ausina (2002).
Revenues are defined as the sum of interest and non-interest income.
Net loans are defined as loans net of loan loss provisions.
Liquid assets include cash, balances with monetary authorities, and holdings of treasury bills.
The term ‘profit maximization’ is intentionally not used here since it is not explicitly modeled in equations 1, 2 and3. However, from the way the model is set up, one could think of the bank's objective as ‘conditional or constrained profit maximisation’: here the banks are assumed to be maximising their revenues conditional upon (or subject to) a fixed level of costs. For a given level of costs, maximising revenues would be identical to maximising profits. Of course, owing to duality property, this problem is identical to minimising costs subject to a fixed level of revenues.
The treatments also differed by countries according to the way the collateral entered the formula for determining the required provisioning, ranging from full exclusion to full inclusion.
The first cluster, Central Europe (CE), includes the Czech Republic, Hungary, Poland, the Slovak Republic, and Slovenia. The second cluster, Eastern Europe and Baltic Republics (EEB), consists of Bulgaria, Croatia, Romania, Estonia, Latvia and Lithuania. Finally, the third cluster, the Commonwealth of Independent States (CIS), includes Armenia, Belarus, Kazakhstan, Moldova, Russia, and Ukraine.
Since the DEA index is a relative measure of efficiency vis-à-vis the frontier, the term catch-up rate (that accounts for a reduction of distance between the bank in question and a static frontier) is a more appropriate one to use than the term growth rate (which would also account the movement of the frontier, or technological innovations). Fare et al. (1994) use this term in the context of a decomposition of the Malmquist total factor productivity index.
The two-stage procedure, in which the efficiency results from DEA are regressed on environmental variables – initially proposed by Lovell et al. (1995) – was implemented by Coelli et al. (1998). Lozano-Vivas et al. (2002) introduced the environmental variables into the calculation of DEA indexes (ie, the first stage). However, as noted by the authors, this approach requires a prior knowledge of the (structure of the) relationship between the environmental variables and efficiency scores, and is, therefore, be very limiting.
This variable takes the value of 1 if a bank is more than 30 per cent foreign owned, and 0 otherwise. Unfortunately, given bank secrecy and confidentiality laws in some countries, data on ownership could not be collected in such a way as to allow the use of a continuous variable of foreign ownership. A threshold value of foreign ownership was, therefore, chosen to set up a dummy variable with the above specifications. Here, not only do we believe that a foreign ownership of this magnitude (ie, 30 per cent) would be sufficient to allow effective leverage over critical business decisions (including veto power), but also it would be large enough to signal that the bank operates independently from the state and is otherwise free of any serious corporate governance problems. (The latter is one of the first aspects prospective foreign buyers verify before buying equity stakes in transition environments). Thus, while it could be argued that a 50 per cent plus 1 share ownership might have constituted a better legal threshold for effective power of foreign ownership, the latter is likely to understate the true effect of foreign ownership with less than a majority stake but effective say in decision making.
This variable takes the value of 1 if a bank is newly established and 0 if it was established before 1990.
Controlling for crisis in the region proved to be a challenging task, which we were unable to complete. The primary reason for abandoning the idea of controlling for crisis is the mere definition of it. Although, it is true that some events that affected transition countries in the 1990s had little or no cross-boarder repercussions (eg, those in Estonia and Latvia in 1995, Bulgaria in 1996, the Czech Republic and Romania in 1997), at least one event in the history of the region had major sub-regional, if not regional, implications; the 1998 Russian crisis. While this was clearly a major adverse shock to the banking sector and the economy of Russia as a whole (which one ideally would like to be able to control for), it also had adverse effects on economies of the region, the countries of former Soviet Union in particular. It is a well-documented fact that a number of economies in the region (eg, Armenia, Hungary, Kyrgyz Republic, Ukraine, to name a few) were hit almost as hard as Russia's. The basic question would then be whether these countries would qualify to get a 1 as far as the dummy variable for crises is concerned. If yes, where does one draw the line? This is where the idea of controlling for crisis was given up. Yet, we believe that the regressions will still capture the instance of crisis, since relevant information is likely to be contained in the macroeconomic indicators, such as inflation, stock market capitalisation, and aggregate income/production.
Examples where high inflationary environment led banks to build excessive branch networks (as a hedge against inflation) include Argentina, Brazil, Turkey, to name a few. Examples of, and reasoning behind, this could be obtained from Hanson and Rocha (1986).
For presentational simplicity, the coefficients on sub-regional dummy variables – which turned out to be insignificant – were eliminated from the table and subsequent discussions.
The crisis of 1992 was caused by freezing by Russian authorities of accounts of the two largest Estonian banks in Moscow, partly coupled with severe liquidity crunch imposed by the Bank of Estonia (see Fleming et al., 1996). Yet, this was relatively mild compared to the crises that hit Latvia and Lithuania in early and late 1995, respectively.
A similar result is obtained by Weill (2003) for banks from Czech Republic and Poland, using the stochastic frontier approach.
A notable example of this taking place in a transition environment is Yerevan-based HSBC-Armenia bank (originally, Midland-Armenia bank). For years, being the only foreign-owned bank in Armenia and having offered deposit rates three to four times lower than those offered by its domestic counterparts, the bank has managed to increase its share of deposits over time.
It should be noted that the data set did not contain information on state ownership of the banks, and therefore it was impossible for us to explicitly control for domestic private ownership.
This result runs contrary to Barth et al. (2001), who find that there is no relationship between stringency of capital requirements and bank performance.
Note that, unlike capital adequacy ratio, higher values of single borrower and foreign exchange exposure limits imply less stringent control.
The diversification index (an aggregate of diversification guidelines and foreign lending-related limits) was, however, found by Barth et al. (2001) to have explanatory power in terms of predicting major banking crises in small countries.
The link between the level of a country's development and revenue-based efficiency is less clear, since higher risks of investment projects may end up being outweighed by higher marginal returns on investments.
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Acknowledgements
We thank Anahit Adamyan for excellent research assistance. Comments and suggestions from Biagio Bossone, Alex Fleming, Edward Gardner, Jeffrey Miller (the Editor), Oleh Havrylyshyn, Giuseppe Iarossi, Roberto Rocha, seminar participants at the World Bank and International Monetary Fund, and two anonymous referees are gratefully acknowledged. Remaining errors are our own.
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APPENDIX: SUMMARY OF INDICATORS USED
APPENDIX: SUMMARY OF INDICATORS USED
Dependent variables–efficiency indicators
DEA1 individual bank DEA indicator with revenues, net loans and liquid assets used as outputs.
DEA2 individual bank DEA indicator with deposits, net loans and liquid assets used as outputs.
Individual bank-specific variables
Number of employees.
Value of fixed assets.
Interest expenditures.
Value of outstanding loans net of accumulated provisions.
Cash balances with monetary authorities and treasury bills.
Interest and non-interest income.
Total value of deposits.
Equity of the bank over total assets.
Dummy variable for foreign ownership (1 if more than 30 per cent owned, 0 otherwise).
Dummy variable for new versus old bank (1 if the bank is established after and 0 if before 1990).
Ratio of bank's total assets of the total assets of the banking system.
Macroeconomic indicators
GDP per capita, PPP adjusted.
Annual average rate of inflation.
Ratio of M2 to GDP.
Institutional quality and ‘Rule of Law’ indicators
Overall score for extensiveness and effectiveness of law (EBRD).
Transition/restructuring indicators
Enterprise restructuring (EBRD).
Prudential standards
Capital adequacy ratio.
Maximum exposure to single borrower.
Limit on foreign exchange open position.
Capital markets/non-bank financial institutions
Stock market capitalisation in percent of GDP.
Securities markets and non-bank financial institutions (EBRD).
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Grigorian, D., Manole, V. Determinants of Commercial Bank Performance in Transition: An Application of Data Envelopment Analysis. Comp Econ Stud 48, 497–522 (2006). https://doi.org/10.1057/palgrave.ces.8100129
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DOI: https://doi.org/10.1057/palgrave.ces.8100129