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Vlad Manole is a consultant at the World Bank. The authors would like to thank Anahit Adamyan for excellent research assistance. Comments and suggestions from Biagio Bossone, Alex Fleming, Edward Gardner, Oleh Havrylyshyn, Giuseppe Iarossi, Roberto Rocha, seminar participants at the World Bank, and two referees are gratefully acknowledged. Remaining errors are the sole responsibility of the authors. An earlier version of this paper was issued as a World Bank Policy Research working paper.
Yet de Melo and others (1997) and, subsequently, Havrylyshyn and van Rooden (2000) show that although the initial conditions were important in defining the difference in performance across countries, their significance diminished over time.
Regulations might also have the opposite effect of what was originally intended, that is, discouraging banks from taking unjustified risks. To see this, note that 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, Charnes, and Cooper (1984) later relaxed the assumption and proposed a model with units of production with variable returns to scale. Theoretical extensions of these methods and empirical applications are discussed in Seiford (1996) and Cooper, Seiford, and Tone (2000).
X = [x1, …, xN] is a (K × N) input matrix with columns xi and Y = [y1,…, yN] is an (M × N) output matrix with columns yi.
Essentially, θi measures the distance between a bank and the efficiency frontier, defined as a linear combination of best practice observations (a convex set thereof), with θi < 1 implying that the bank is inside the frontier (i.e., it is an inefficient bank), while θi = 1 implying that the bank is on the frontier (i.e., it is an efficient bank).
Various versions of the Data Envelopment Analysis are used for monitoring and/or early warning systems used by bank regulatory agencies (see Barr, Seiford, and Siems (1994), and Brockett and others (1997)).
In addition to making use of data collected by BankScope—which largely contained balance-sheet and income-statement information—with the help of World Bank field staff, we collected data on banks’ employment and foreign ownership.
Belarus and Ukraine would be the exceptions.
For simplicity, it is assumed that beyond the difference explained by general macro-, as well as business environment-related indicators, no systemic differences exist between banking sectors in the sample countries.
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 1–3. However, from the way the model is set up, one could think of the bank’s objective as “conditional or constrained profit maximization”: here the banks are assumed to be maximizing their revenues conditional upon (or subject to) a fixed level of costs. For a given level of costs, maximizing revenues would be identical to maximizing profits. Of course, owing to duality property, this problem is identical to minimizing 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.
More details on these and other prudential standards are available from authors upon request.
The first cluster, Central Europe (CE), includes the Czech Republic, Hungary, Poland, the Slovak Republic and Slovenia. The second cluster, Southern and Eastern Europe and the Baltic Republics (SEE), consists of Bulgaria, Croatia, Romania, Estonia, Latvia, and Lithuania. Finally, the third cluster, the Commonwealth of Independent States (CIS), includes Armenia, Belarus, Kazakhstan, Moldova, the Russian Federation, and Ukraine.
Since the DEA index is a relative measure of efficiency vis-à-vis the most efficient bank, the term catch-up rate (symbolizing the reduction of distance between the bank in question and the most efficient bank) is a more appropriate one to use than the term growth rate.
This variable takes the value of 1 if a bank is more than 30 percent foreign owned, and 0 otherwise.
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 quite 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 course of the 1990s had little or no cross-border repercussions (e.g., those in Estonia and Latvia in 1995, Bulgaria in 1996, and the Czech Republic and Romania in 1997), at least one event in the history of the region had major subregional, if not regional, implications. It is the Russian crisis of August 1998. 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), it also had adverse effects on economies of the entire region (the countries of former Soviet Union in particular). It is a well documented fact that a number of countries in the region (Armenia, Hungary, Kyrgyz Republic, and Ukraine, to name but a few), were perhaps hit almost as hard as Russia itself in terms of the effect on quality of bank assets, the stock markets, purchasing power of economic agents, etc. Now, the question is, do these countries qualify to get a value of 1 as far as the dummy variable for crises is concerned? Where does one draw the line? Yet, this said, we assume that the regression will still capture the events of the crisis since relevant information is likely to be contained in the rate of inflation, stock market capitalization, and per capita income.
Examples where high inflationary environment led banks to build excessive branch networks include Argentina, Brazil, Turkey, to name but a few.
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, Chu, and Bakker (1996)). The total fiscal cost of banking crisis in Estonia was estimated to be 1.9 percent of GDP, compared with 2.7 and 3.1 percent in Latvia and Lithuania respectively (see Tang, Zoli, and Klytchnikova (2000), p. 35).
A notable example of this taking place in a transition environment is Yerevan-based HSBC-Armenia bank (originally set up as Midland-Armenia bank). For years being the only foreign owned bank in Armenia and having offered deposit rates, which are 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 dataset 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, Caprio, and Levine (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, Caprio, and Levine (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.