metadata: authors: 0: author: Grigorian, David A.
1: author: Manole, Vlad
docna: 0: docna: Determinants of commercial bank performance
            in transition - an application of data envelopment analysis
docty: Policy Research Working Paper
subtopic: State Owned Enterprise Reform,Public Sector Administrative & Civil
            Service Reform,Legal Institutions of the Market Economy,Capital Markets and Capital Flows,Non Bank Financial Institutions,Capital Flows,Inflation,Privatization,Energy Privatization,Democratic Government,Economics and Finance of Public
            Institution Development,Financial Sector Policy,Public Sector Administrative and Civil
            Service Reform,De Facto Governments
teratopic: Finance and Financial Sector Development
abstracts: cdata!: 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 quite low. The authors estimate indicators of
            commercial bank efficiency by applying a version of Data
            Envelopment Analysis (DEA) to bank-level data from a wide
            range of transition countries. They further extend the
            analysis by explaining the differences in efficiency between
            financial institutions and countries by a variety of
            macroeconomic, prudential, and institutional variables. 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. 3) Consolidation is
            likely to improve efficiency of banking operations. Overall,
            the results confirm the usefulness of DEA for
            transition-related applications and may shed light on the
            optimal architecture of a banking system.
repnme: repnme: Determinants of commercial bank performance
            in transition - an application of data envelopment analysis
display_title: Determinants of commercial bank
            performance in transition - an application of data
            envelopment analysis
listing_relative_url: /research/2002/06/1808699/determinants-commercial-bank-performance-transition-application-data-envelopment-analysis
topicv3: Non-Bank Financial Institutions m1326281 336,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Privatization m1327236 1283,Financial Sector Development m1327681 1920,Financial Institutions m1505015 3797
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Finance and Financial Sector Development
origu: Private/Financial Sectors Devt (ECSPF)
no_of_pages: 40
model_extractions: I POLICY RESEARCH WORKING PAPER 2850

# 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 quite low.

Grigorian and Manole estimate indicators of commercial bank efficiency by applying a version of Data Envelopment Analysis (DEA) to bank-level data from a wide range of transition countries. They further extend the analysis by explaining the differences **in** efficiency between financial institutions and countries by a variety of macroeconomic, prudential, and institutional

> variables. In addition to stressing the importance of some bank-specific variables, the censored Tobit analysis

> suggests that:

> * Foreign ownership with controlling power and enterprise restructuring enhance commercial bank efficiency.

> * The effects of prudential tightening on the efficiency of banks vary across different prudential norms.

* Consolidation is likely to improve efficiency of banking operations.

Overall, the results confirm the usefulness of DEA for transition-related applications and may shed light on the optimal architecture of a banking system.

> This paper-a product of the Private and Financial Sector Development Unit, Europe and Central Asia Region-is part of

> a larger effort in the region to disseminate the results of research on transition issues. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Sylvia Torres, room H6-216, telephone 202-473-9012, fax 202-522-0005, email address storres@worldbank.org. Policy Research Working Papers are also posted

> on the Web at http://econ.worldbank.org. The authors may be contacted at dgrigorian@imf.org or

> manole@wueconc.wustl.edu. June 2002. (36 pages)

> _The Policy Research Working Paper Series disseminates the_ **_findings_** _of work_ **_in_** _progress to encourage the exchange of ideas about development issues An objective of the senes_ **_is_** _to get the findings out_ **_quickly,_** _even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The_ **_findings,_** _interpretations, and conclusions expressed_ **_in this_** _paper are entirely those of the authors. They do not necessanly represent the view of the World Bank,_ **_its_** _Executive Directors, or the countnes they represent._

Produced by the Research Advisory Staff
# **Determinants of Commercial Bank Performance in Transition:**

**An Application of Data Envelopment Analysis***

David A. Grigorian Vlad Manole At the time this research was initiated, David A. Grigorian was<sup>with Privateand Financial</sup> Sector Development Unit, Europe and Central Asia Region, of<sup>the World Bank.He now works at</sup> the Intemational Monetary Fund and can be reached at Dgrigoriani.mf.org.<sup>The coauthor is</sup> Vlad Manole from Washington University who can be reached at manoleawueconc.wustl.edu. This research was funded by the ECA Regional Initiatives<sup>Fund of the World Bank. The authors</sup> would like to thank Anahit Adamyan for excellent research assistance.<sup>They would alsolike to</sup> express their appreciation to colleagues in the World Bank country<sup>officesin Belarus, Bulgaria,</sup> Croatia, Hungary, Moldova, Romania, Russian Federation, Slovak<sup>Republic and Ukrainefor</sup> their help in putting the dataset together. Comments and<sup>suggestions fromBiagio Bossone,Alex</sup> Fleming, Oleh Havrylyshyn, Giuseppe Iarossi, Roberto Rocha,<sup>and seminar participantsat the</sup> World Bank at various stages of this research are gratefully<sup>acknowledged.</sup>
# **Chapter 1. Introduction**

Banking sectors in transition<sup>economies of Eastem Europe and FormerSoviet Union</sup> have experienced major transformations<sup>throughout the1990's. In the pre- and early-transition</sup> periods, state policies generally<sup>distorted resourceallocation,as credit (subject to a variety of</sup> controls) was directed toward<sup>sustaining existingindustries and maintainingliving standards</sup> through explicit and implicit<sup>subsidies to enterprisesand households. Since the primary role of</sup> the banking system was to<sup>channelfunds to the realsector,efficiency and profitability were not</sup> among the top priorities. The<sup>banks were notengaged in evaluatingthecredit conditions of their</sup> borrowers, and therefore no<sup>risk management techniques werein use.Old statisticalstandards</sup> were designed to serve the<sup>objectiveof easy planningas opposed to disclosure of the true</sup> financial state of banks.

While some countries in the<sup>regionhave been successfulin eliminatingunderlying</sup> distortions and restructuring<sup>their financialsectors, in some casesfinancial sectors remain</sup> underdeveloped and the<sup>rates of financial intermediation continue to be quitelow (Table1).This</sup> in<sup>light of mounting evidenceof the effectof financial sector development</sup> is especially alarming on economic growth (see,<sup>forinstance, Levine and Renelt (1992), King and Levine (1993),etc.).and</sup> Although part of the success<sup>acrosscountries could be attributed to better initial conditions1</sup> achievements of early macroeconomic<sup>stabilization programs, the importanceof structural</sup> reforms were the dominant<sup>force behind the successin performance(e.g., Berg</sup><sup>_et al.,_1999).</sup> Progress in legal and regulatory<sup>reforms,enterprise privatization, priceand external trade</sup> liberalization were partially<sup>responsiblefor the changesoccurringin some transition countries.</sup> These changes manifested<sup>themselves not only in the financialsoundness of banking institutions,</sup> but also in a wider range and<sup>sophisticationof financial services provided by the latter.A few</sup> countries in the region were<sup>able to transform their banking sectorsfrom astate where dozens of</sup> small banks were set up to serve<sup>individual enterprises(or worse, tooperate as scam machines!),</sup> to a state where putting all<sup>their eggs in one basketis considered suicidal, establishing</sup> correspondent relationships<sup>with major Western banks is a matter of lifeand death, and internet</sup> banldng is no longer a luxury.<sup>Reducedavailability of centralizedfinancing encouraged banksin</sup> I Yet de Melo _et al._ (1997)<sup>and, subsequently,Havrylyshyn and van Rooden (2000) show that</sup> although the initial conditions<sup>wereimportantin defining the differencein performanceacross</sup> countries, their significance<sup>diminished over time.</sup> 3
the region to look for altemative sources of financing and undertake measures aimed at deposit mobilization. As economies grew, triggering an increase in savings, so did the number of financial intermediaries (securities market institutions, pension funds, insurance companies, and brokerage firms) claiming a share of the pie and exerting additional pressure on banks to improve service provisions.

In light of these changes, it has become important to assess the relative role of different institutional and policy settings in explaining the difference between outcomes across countries and financial institutions. Yet, the effect and relative importance of various macro and regulatory elements of financial reforms in transition countries have so far not been properly evaluated. Nor is there a consensus on the scope and the scale of effective regulatory regime. Studies on transition economies have yet to look into these issues.

> One should note that different policy measures may have different, and often opposite, effects on operational efficiency and technological improvements of banking operations. For instance, although tightening prudential requirements may limit banks' profitability and reduce the operating efficiency in the short run, doing so may encourage banks to look for new and innovative ways to invest, thereby expanding the production possibilities frontier.<sup>3</sup> It is therefore

> essential for a policymaker to be able to identify policy instruments that are effective in bringing about changes in productivity and efficiency and come up with the best (often least-cost) policy response. This is of particular importance for economies in transition, where the choice of instruments for policymakers can be rather limited as well as costly.

This paper aims to fill in the gap. Its purpose is to (1) calculate an appropriate measure of commercial bank efficiency in a multiple-input/output framework and (2) evaluate the effects of policy framework on the performance of commercial banks as measured by these efficiency indicators. We do so in a wide range of transition countries and across a number of years. We argue that to fully assess the efficiency of commercial bank operations, it is necessary to model various types of functions performed by banks. In the beginning we skip the discussion on the potential links between various policy measures and bank efficiency, only to discuss them later, 2 Exceptions are Claessens (1996) and, more recently, Barth _et aL_ (2001).

'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.

4
along with the final results of estimation. This paper<sup>is a productof ongoing research and is not</sup> intended to provide answers to all questions raised in the<sup>paper.On the contrary,we realize that</sup> more questions may have been created than answered.

The range of questions of concem are as follows: Do banks<sup>that are subjectto tighter</sup> prudential standards do better? Do banks with foreign ownership<sup>outperform their domestically</sup> owned counterparts? Does competition necessarily<sup>improve efficiency?Does enterprise</sup> restructuring affect bank performance in any significant way? What<sup>is the role of capital market</sup> development in strengthening bank performance? Do<sup>a country'slegal traditionsand rule of law</sup> affect bank efficiency indicators?

The paper is structured in the following way. Chapter<sup>2 provides an overview of the</sup> literature on bank efficiency analysis and describes the methodology<sup>used in the study.Chapter</sup> 3 describes the dataset and discusses the results of Data<sup>Envelopment Analysis in cross-country</sup> and cross-regional settings. Chapter 4 explains differences<sup>in cross-bank efficiency indicatorsby</sup> regressing them on the set of policy instruments<sup>and controlvariables.Finally,Chapter 5</sup> concludes.

# Chapter 2. Methodology

During the past few decades the banking sectors around<sup>the world experienced profound</sup> regulatory and technological changes. Advanced<sup>applications in computerand communications</sup> technology together with the introduction of new<sup>financialinstruments have altered the way</sup> banking is conducted. Such changes significantly<sup>modified the technology of bank production.</sup> In this regard, a frequently asked question was about<sup>the effectof these changeson the efficiency</sup> of banks. A large number of studies (see Berger and<sup>Humphrey (1997)for a survey) tried to</sup> answer this question by proposing new methods to<sup>measure the efficiencyof the banking sector.</sup> The most widely used methods are Data Envelopment<sup>Analysisand the Stochastic Frontier</sup> Approach. Below we present a short review of<sup>the productivity and efficiency literaturewith a</sup> particular focus on the methodology to be used in<sup>this paper.</sup> Studies on productivity growth in the baiking sector<sup>usually base their analysison cost</sup> ratio comparisons. There are several cost ratios to be used<sup>and each one of them refersto a</sup> particular aspect of bank activity. Since the banking<sup>industryuses multiple inputs to produce</sup> _5_
multiple outputs, a consistent aggregation may be problematic (see<sup>Kim, 1986).Someattempts</sup> have been made to estimate average practice cost functions. While these<sup>approacheswere</sup> successful in identifying the average practice productivity growth, they<sup>failed to take into</sup> account the productivity of the best practice banks. These problems associated<sup>with the,</sup> "classical" approach to productivity led to the emergence of other<sup>approacheswhich incorporate</sup> multiple inputs/outputs and take into account the relative performance<sup>of banks.</sup> One string of literature uses frontier analysis to separate the<sup>banking units that perform</sup> well from those that perform poorly, according to a specified set of<sup>criteria.The focus of this</sup> literature is on the changes of the efficiency frontier and on how close<sup>banks are to operating on</sup> the efficiency frontier. In an influential article, Farrell (1957) proposed<sup>two ways toestimate the</sup> frontier. The first method called for identifying efficient production<sup>units and building a piece-</sup> wise linear efficiency frontier using these efficient units. This was<sup>first implemented by Charnes</sup> _et al._ (1978), who used linear-programming methods to identify the<sup>efficient units and coined the</sup> term Data Envelopment Analysis (DEA).<sup>4</sup> Throughout the past two decades, hundreds of articles have developed<sup>the Data</sup> Envelopment Analysis and over a thousand papers have applied<sup>the method to different fields</sup> ranging from banking to education.<sup>5</sup> This study will estimate the<sup>efficiencyof banks by usinga</sup> model which assumes a variable return to scale (VRTS). We start<sup>with presentinga model with</sup> constant returns to scale (CRTS) and then extend it to allow for variable<sup>returns to scale.In the</sup> case of a CRTS technology, the linear programming method establishes<sup>which of thedecision-</sup> making units (DMUs), in our case banks, determines the envelopment<sup>surface.The latter is</sup> referred to as the empirical production function or efficient frontier.<sup>This benchmark frontier is a</sup> linear combination of the efficient banks in the sample. The set of best practice<sup>or frontier</sup> observations are those for which no other decision making unit or linear<sup>combinationof units has</sup> as much or more of every output (given a fixed amount of inputs<sup>-</sup> for<sup>an output oriented model)</sup> or as little or less of every input (given a fixed amount of outputs - for an input<sup>oriented model).</sup> 4Their method is based on the assumption that the production<sup>units have constant returns to</sup> scale. Banker _et al._ (1984) later relaxed the assumption and proposed<sup>a model with units of</sup> production with variable returns to scale. Theoretical extensions of these<sup>methodsand empirical</sup> applications are discussed in Seiford (1996) and Cooper _et al._ (2000).

5For detailed surveys and recent developments in the field see Charnes _et_<sup>_al._(1994) and Cooper</sup> _et al._ (2000).

6
The DEA frontier is formed as the piecewise linear<sup>combination that connectsthe set of these</sup> best practice observations, yielding a convex production<sup>possibility set.The DEA providesa</sup> computational analysis of relative efficiency for multiple<sup>input/outputsituations by evaluating</sup> each decision-making unit and measuring its performance<sup>relative toan envelopment surface</sup> composed of best practice units. Units that do<sup>not lie on the surface are termed inefficient.Thus</sup> this method provides a measure of relative efficiency.

Let us provide a brief description of the underlying<sup>linear programming model. We</sup> assume that there are K inputs and M outputs for every<sup>DMU. For the</sup><sup>**_/h_**DMU the inputs and</sup> outputs are represented by vectors xi and y2 respectively.<sup>For each DMU we intend to obtain a</sup> measure of the ratio of all outputs over all inputs, such<sup>as ui'yi/vi'xi,where u; and v; are vectors</sup> of weights. To select the optimal weights, the following<sup>problem is proposed:</sup> A problem with this formulation it that it has an<sup>infinitenumber of solutions.This can be</sup> avoided by introducing a constraint v'x; = 1, and<sup>obtain the multiplierform of the linear</sup> programming problem: max W'y, _sj. ai'_ **_xi_** /.i, 2 _!, xi_ 0 _/a,_ ~7i. Ž 0 _i,j = 1,2,...N_ (2) _k =1,2,...K m 1,2,..M_ 7
where u's and v's are replaced with p's and a's. Using the duality property of this linear programming problem, one can derive an equivalent envelopment form: minO1 **_O,A_** _st.-Y,ik_ +YA20 (3)

**_°xim_** kR 2 0 A., Ž0

where X is an N by 1 vector of constants and 0, a scalar, is the efficiency score for the<sup>**_e4h_**</sup> DMU.<sup>6</sup> Note that O • 1, with O0 = 1 implying a DMU which is located on the efficiency frontier. Due<sup>to</sup> fewer number of constraints, this formulation is usually used for computations.

However, the above approach is somewhat simplified as it assumes a constant return to scale. The CRTS assumption is only appropriate when all banks are operating at an optimal scale. Factors that may cause banks not to operate at an optimal scale include imperfect competition, leverage concerns, certain prudential requirements, etc. The fact that banks face non-constant retums to scale has been documented empirically by, among others, McAllister<sup>and</sup> McManus (1993), and Wheelock and Wilson (1997). This phenomenon led Banker _et al._ (1984) to suggest an extension of the CRTS DEA model to account for a variable return to scale (VRTS). They added a convexity constraint Ni ' = 1 to problem 3 above (where Ni is a N by 1 vector of ones). This condition ensures that an inefficient bank is "benchmarked" against similar sized banks. As a result, VRTS technology envelops the data more closely than CRTS technology, and consequently, VRTS technical efficiency scores are greater than or equal to CRTS technical efficiency scores. The advantages of the VRTS model outweigh the increase<sup>in</sup> computational power necessary to solve the model, which allowed the VRTS to gain popularity over the CRTS method (Fried _et al._ (1993), Coelli _et al._ (1998), Cooper _et al._ (2000)).

Data Envelopment Analysis was used extensively in studies of the banking industry in developed market economies.<sup>7</sup> On U.S. data alone the method was used in more than 30 published articles. The method was also applied for cases in Norway, Spain, U.K., and several _6_ X _=_ [Xl,...,XN] is an K by N input matrix with columns x, and Y [yl,...,yN] is an M by N output matrix with columns y,.

> 7 Various versions of the Data Envelopment Analysis are used for monitoring and/or early warning systems used by bank regulatory agencies (see Barr _et al._ (1994), Brockett _et al._ (1997), and Simak (1997)).

8
other countries.<sup>8</sup> There is a vast literature which<sup>uses DEA for inter-countriescomparisons.For</sup> Scandinavian countries, a study by Berg _et al._ (1993)<sup>computeda separate frontierfor every</sup> country and a common frontier for all countries.<sup>The study found that Swedish banks were the</sup> most efficient, a result sustained by a follow-up<sup>paper by Bukh</sup><sup>_et aL_(1995).Bergendhal(1998)</sup> studied the same countries using a slightly modified<sup>approach.In a broader context,Pastor</sup><sup>_et al._</sup> (1994) applied DEA to 427 banks from 8 developed<sup>countries.Theyfound a mean efficiency</sup> value of 0.86, with the highest efficiency value of<sup>0.95for France and the lowestefficiency value</sup> of 0.55 for the U.K.

# Chapter 3. **Data** and Measurement Issues

The analysis presented below is based on a data<sup>set compiled by BankScope. 9The set</sup> contains comprehensive financial data on a large<sup>number of banks from transitioneconomies.</sup> The distribution of sample banks by countries<sup>for 1995-1998is shownin Table 2.A total of</sup> seventeen transition countries are represented.<sup>Table3 contains the sharesof assets of sarnple</sup> banks in total assets of the banking system<sup>in each particularcountry.As one can see,the data</sup> set is fairly representative: in some cases: (e.g. Croatia,<sup>Estonia,Slovenia)it coversover 90</sup> percent of the banking sector at a point in time between<sup>1995 and1998.Poorly represented</sup> banking sectors are those of Bulgaria, Russia<sup>and Ukraine, where the shareof sample banks does</sup> not exceed 40 percent for any particular year. The<sup>financialindicators of individualbanksare</sup> collected by BankScope using audit reports of<sup>banks completedby intemationallyreputable</sup> auditing firms, and are believed to be largely IAS<sup>compliant.As we foresee arguments</sup> concerning the reliability of some of the indicators<sup>in an environment whereserious misreporting</sup> and non-compliance takes place, the choice of<sup>countries (thosewith relativelyadvanced banking</sup> sectors compared with the overall population of<sup>transition economies'0),and sample period</sup>

**8** See Berger and Humphrey (1997) for a detailed survey.

9 In addition to making use of data collected by BankScope<sup>-which largely containedbalance</sup> sheet and income statement information - with the<sup>help of World Bank field staff, we collected</sup> data on banks' employment and foreign<sup>ownership.</sup> 1 Belarus and Ukraine would be the exceptions.
(mid- as opposed to early-transition) for this paper are believed to reduce the extent of<sup>these</sup> problems. Although there may be some pre-selection bias in the sample (since it<sup>could be argued</sup> that only better performing banks are likely to have arranged for an audit by international auditing firms) we believe that this would not jeopardize the validity of our (in essence crosscountry) arguments. Nevertheless, we acknowledge the presence of data problems specific<sup>for</sup> the transition environment and discuss them in greater details in subsequent sections.

_Definitions and Measurement issues_ Before analyzing bank level productivity indicators, we start by defining a bank's<sup>objectives and</sup> specifying its respective inputs and outputs. There is a long-standing debate over what exactly constitutes banking output. In Fixler and Zieschang's (1992) view this output consists of "transaction services and portfolio management services that banks provide to depositors<sup>while</sup> acting as their intermediary". Arguably the range of services, as defined above, could be fairly wide and largely dependent on the degree of the economy's financial development.<sup>One should</sup> not be surprised to see both the width (variety) and the depth (complexity) of financial services available to the general public change as the economy develops, so they should be expected to differ across countries." The precise definition of a bank's "mandate" is important<sup>inasmuch as</sup> the definition of inputs and outputs stems from the functions that banks perform. The latter<sup>is</sup> essential for building our model.

The most commonly presented approaches to bank production could perhaps be summarized under the following three headings: asset approach, user-cost approach, and<sup>value</sup> added approach. Under the _asset approach_ banks are considered only as financial<sup>intermediaries</sup> between liability holders and fund beneficiaries (i.e. debtors). Loans and other assets are considered to be the banks' outputs, while deposits and other liabilities are inputs to the intermediation process (see, for instance, Sealey and Lindley (1977) for a discussion). While this approach seems to be appropriate for large banks that purchase their funds in big chunks from other banks and large institutional depositors, it may not be so for the majority of banks. For the smaller banks this method fails to account for transaction services delivered<sup>by the latter</sup> to their depositors, and therefore underestimates the overall value added of banking activities.

11 For simplicity, it is assumed that beyond the difference explained by general macro-<sup>as well as</sup> business environment-related indicators no systemic differences exist between<sup>banking sectorsin</sup> the sample countries.

10
Under the _user-cost approach,_ the net revenue generated by a particular asset or liability item determines whether that financial product is an input or an output.<sup>Hancock(1991)</sup> developed a model for bank production and was among the first to apply the<sup>user cost approach</sup> to banking. Hancock stated that it is not clear _ex ante_ whether monetary<sup>goodsare inputs or</sup> outputs in the production process. She goes on to say that if the financial<sup>returnson an asset</sup> exceed the opportunity cost of funds (or if the financial cost of a liability<sup>is less than the</sup> opportunity cost), then the instrument is considered to be a financial output.<sup>Otherwise,it is</sup> considered to be an input. According to Hancock's rule, demand deposits would<sup>be classified as</sup> outputs while time deposits would be classified as inputs. However, there are problems<sup>with this</sup> approach. First of all, as interest rates fluctuate, so does the user cost.<sup>An item which is</sup> considered to be an output in one period can tum into an input in the next period if<sup>the sign of its</sup> user cost changes. Secondly, it is difficult to measure marginal revenues<sup>and costs for each</sup> individual liability item. Th?us the answer to the question whether an item is<sup>an input or output</sup> becomes a subject of significant measurement error and is sensitive to changes in<sup>data over time.</sup> And finally, the _value-added approach_ considers that both liability and asset categories have some output characteristics. Nevertheless, only those categories that have<sup>substantialvalue</sup> added are treated as outputs while the others are treated as either inputs or intermediate<sup>products</sup> depending on the specific attributes of each category. The value added approach<sup>differsfrom the</sup> user cost approach in that it is based on actual operating cost data rather than<sup>determining these</sup> costs explicitly. This approach has been widely used in studies of the banking<sup>industry (Berger</sup> _et al._ (1987), Berger and Humphrey (1997), etc.).

Taking into account the advantages and disadvantages of each method, the value added approach was employed, which enabled the classification of inputs and outputs<sup>based on their</sup> perceived value added. This method turns out to be quite attractive as it allows<sup>one to</sup> differentiate between various functions performed by the banks. What are they<sup>and why does</sup> one need to account for them? This too turns out to be crucial for the analysis.<sup>In analyzing the</sup> functions performed by commercial banks, Bergendhal (1998) mentions five<sup>fundamentalgoals</sup> of efficient bank management: profit maximization, risk management, service<sup>provision,</sup> intermediation, and utility provision. To keep things simple, we argue, without<sup>the loss of</sup> generality, that these five approaches could be pooled into two broadly defined<sup>ones:</sup><sup>_profit_</sup> _maximization_ (combining features of Bergendhal's profit maximization and risk<sup>management)</sup> and _service provision_ (combining elements of service provision, intermediation<sup>and utility</sup>
provision). In reality any bank operation combines<sup>the elementsof the twofunctions,as it is</sup> hard to imagine a legitimate bank which is not trying<sup>togenerate (if not maximize) profits or</sup> establish a good rapport with its clients. Bearing<sup>this in mind, no weight is added to any of the</sup> functions, but instead elements of the two are incorporated<sup>into the model.We nevertheless</sup> specified two different output sets to incorporate<sup>the elementsof both functions,each time to</sup> have greater emphasis put on one of the two<sup>functions performed.</sup> The following three inputs to the banking "production<sup>process" aredefined:</sup><sup>_labor, fixed_</sup> _assets_ and _interest expenditures._ Doing so accounts<sup>for all threeessentialinputsto commercial</sup> bank operations: (1) personnel and management,<sup>(2) computer hardware and premises(which</sup> also captures the extensiveness of a bank's branch<sup>network),and(3) leveragedfunds</sup> respectively. Holding output (however specified)<sup>and twoother inputs constant,a lesseramount</sup> of the third input used in the "production"<sup>would imply higher efficiency.</sup> Next, we define the two sets of outputs as follows:<sup>(1)</sup><sup>_revenues12,net loans'3,_and</sup><sup>_liquid_</sup> _assets_<sup>_14_</sup> _,_ and (2) _deposits, net loans,_ and _liquid assets._<sup>As mentioned above,theefficiency</sup> indicator constructed based on the first set of outputs<sup>(hereafter denotedas DEAl) places greater</sup> emphasis on profit generation.'<sup>5</sup> Similarly, an<sup>indicator based on the secondset of outputs</sup> (hereafter denoted as DEA2) would stress service<sup>provisionas a goal.Both indicators,DEAI</sup> and DEA2, however, recognize the quality of loans<sup>(and,therefore,ex post returns)and the</sup> provision of liquidity services as objectives of a<sup>banks'operations.Since one of theobjectives</sup> of the paper is to search for potential links between<sup>policiesand various functions performed by</sup> a bank, contrasting these indicators and the ways<sup>they are affected by policyinstruments will</sup> prove interesting.

12 Revenues are defined as the sum of interest and<sup>non-interestincome.</sup> 13 Net loans are defined as loans net of loan loss<sup>provisions.</sup> 14 Liquid assets include cash, balances with monetary<sup>authorities,and holdings of T-Bills.</sup> _15_ The term _"profit maximization"_ is intentionally<sup>not used here sinceit is not explicitly modeled</sup> in Equations 1-3. However, from the way the model<sup>is set up,one could thinkof the banks'</sup> objective as _"conditional_ or _constrained_ profit<sup>maximization": herethe banks areassumed to be</sup> maximizing their revenues conditional upon (or<sup>subjectto) a fixed levelof costs.For a given</sup> level of costs, maximizing revenues would be identical<sup>to maximizingprofits.Of course,owing</sup> to duality property, this problem is identical to minimizing<sup>costssubject to a fixedlevel of</sup> revenues.

12
Prior to tuming our attention to the results of the estimation, it is perhaps worth reminding the reader about limitations of transition-specific data which<sup>are likely to have</sup> implications for the analysis through its impact on output measures.<sup>Problems primarily stem</sup> from imperfections in accounting standards which generally allowed<sup>doubtful treatmentsof some</sup> transactions and financial positions. The most notorious of these<sup>wasa commonly used practice</sup> of rolling over the non-performing loans by extending fresh credits<sup>to delinquent borrowersand</sup> immediately crediting the same amount (or a portion thereof) back in, showing<sup>it as interest</sup> payment, hence inflating the balance sheet and overstating revenues.<sup>However,to the extent that</sup> these practices were common in all transition countries and all banks (exceptions, of<sup>course,</sup> apply), the analysis is unlikely to be significantly affected<sup>as a result of these imperfectionsin</sup> our measures of bank revenues and net loans.

Yet in addition to accounting imperfections which defined the playing<sup>fieldfor virtually</sup> all financial institutions in transition countries, there were country specific differences<sup>in the</sup> treatments of certain transactions. These primarily refer to differences<sup>in loan loss provisioning</sup> rules and classification requirements. For instance, while all principal<sup>and interest repaymenton</sup> a loan had to be current for the loan to qualify as standard in Belarus, Croatia,<sup>Kazakhstan,</sup> Latvia, Poland, Romania, and Slovenia (at least for a portion of the sample<sup>period),standardloan</sup> classification guidelines in Bulgaria, Czech Republic, Lithuania<sup>and Slovakiaallowedfor up to a</sup> 30 day delay in repayments. Even when a loan is qualified<sup>as watch (thesecond groupafter the</sup> category of standard loans), the regulations provided for different degrees of<sup>provisioning,</sup> varying from 0 percent in Lithuania to 25 percent in Croatia<sup>and Bulgaria.16 17</sup> However, the issue which may have the largest impact on the current analysis<sup>is the issue</sup> _of enforcement_ of prudential regulations (again, loan loss provisioning<sup>being the key one in this</sup> respect) across types of financial institutions. The anecdotal evidence<sup>suggests that thestrictness</sup> with which regulations are being enforced varies not only across countries<sup>but alsoacrosstypes</sup> of banks (e.g., state- vs. privately-owned, new vs. old, etc.). Although this<sup>differentiated</sup> approach towards state- and privately-owned banks in the short run may<sup>be advocatedon</sup> grounds of systemic/economic impact of possible bank closures<sup>-since old state-owned banks</sup> are likely to have a larger share of non-performing loans and are therefore likely<sup>to register</sup> 16 The treatments also differed by countries according to way the<sup>collateralentered theformula</sup> for determining the required provisioning, ranging from full exclusion<sup>to full inclusion.</sup> 17 More details on these and other prudential standards are available<sup>fromauthorsupon request.</sup> 13
negative capital if full provisioning is enforced - the existence of<sup>the differentiatedapproachis</sup> likely to overstate the net loans of state-owned banks and therefore their performance<sup>as</sup> measured by the DEA approach. Understandably, these and other data limitations<sup>(especially</sup> those which are bank-specific and not country- or, more generally,<sup>transition-specific)may have</sup> a heavy toll on the outcome of our analysis. Yet, by acknowledging<sup>the presence of these</sup> problems (and, in fact, our inability to deal with some of them within the framework<sup>of this</sup> paper), we still hope to be able to provide a useful framework for<sup>analyzing banking efficiency</sup> by, among other things, flagging the potential problems as we unveil<sup>our results.With these</sup> caveats in mind, let us now proceed to the results of the DEA analysis.

_Results of Efficiency Analysis_ The results of DEA analysis by countries using the model described<sup>in Equation3 is presentedin</sup> Tables 4 and 5. Countries that produced the best outcome in terms of<sup>a revenue-basedindex(i.e.,</sup> DEAl) are: Czech Republic (1995 and again in 1998), Slovak Republic (1996),<sup>andCroatia</sup> (1997). The list of service-based index (i.e., DEA2) winners consists<sup>of CzechRepublic(1995),</sup> Slovenia (1996 and again in 1998), and Latvia (1997). Conversely, countries<sup>that yielded the</sup> lowest revenue-based index in any one of the four years are: Lithuania<sup>(1995), Belarus (1996 and</sup> 1997) and Moldova (1998). Worst performers in terms of a service-based<sup>indexare:Belarus</sup> (1995 and 1996), Romania (1997) and Ukraine (1998).

To further check the consistency of the analysis, the countries<sup>were grouped together in</sup> three relatively homogenous clusters (sub-regions).'<sup>8</sup> Figures 1<sup>and 2 summarizethe findings</sup> across sub-regions. A number of interesting points are worth stressing<sup>here.</sup> First of all, if looked at through the prism of medium term development,<sup>results vary</sup> across functions that banks perform. It is interesting to note that while<sup>the catch-uprate19of a</sup> revenue-based index of the entire sample was less than 1 percent<sup>everyyear between1995 and</sup> 18 The first cluster, Central Europe, includes Czech Republic, Hungary,<sup>Poland, Slovak Republic</sup> and Slovenia. The second cluster, Southem and Eastem Europe and the<sup>Baltic Republics,</sup> consists of Bulgaria, Croatia, Romania, Estonia, Latvia and Lithuania.<sup>Finally, thethird cluster,</sup> the Commonwealth of Independent States, includes Armenia, Belarus,<sup>Kazakhstan,Moldova,the</sup> Russian Federation and Ukraine.

19 Since the DEA index is a relative measure of efficiency vis-a-vis<sup>the most efficient bank, the</sup> term _catch-up rate_ (symbolizing the reduction of distance between<sup>the bankin question and the</sup> most efficient bank) is a more appropriate one to use than the term _growth_<sup>_rate._</sup> 14
1998, the average catch-up rate of a service-based<sup>index reached a level of 16.5 percentper</sup> annum. This suggests that even though an average<sup>bank in the region might have reacheda</sup> satiation point in terms of its ability to improve<sup>upon revenue performance(either because it</sup> knew a lot to start with or further improvements<sup>are outside of their control, or both), it had a lot</sup> to leam in terms of reaching out and delivering<sup>transactionservices to its clients.</sup> Second, although experiencing less of a lag compared<sup>to banks in CE and SEEin terms</sup> of revenue generating capacity, banks in CIS<sup>are clearly in a more disadvantageousposition if</sup> judged by the service-based index. However, it<sup>tums out there are dramatic differences between</sup> the rate of catch-up in a service-based index<sup>acrosssub-regions:the DEA2 index for CIS</sup> countries grew on average 30 percent per<sup>annum,followed by almost16 percentin SEE</sup> countries, and only 11 percent in CE countries.

Third, with the exception of 1997, banks in Central<sup>Europeare more efficient in terms of</sup> both revenue generating capacity as well<sup>as theability to provide services to their clients.</sup> A skeptical reader should note that the presence of<sup>the sample bias -resulting because</sup> countries with more advanced banking systems<sup>may have a largershare of their banks (i.e.,both</sup> strong and weak banks) presented in the sample<sup>-does not run infavor of CE banks.On the</sup> contrary, eliminating the bias (by ensuring equal<sup>representation from allcountries)is likely to</sup> make the difference between CE and CIS look<sup>larger.</sup> Overall, we conclude that the trends described<sup>aboveare largelyconsistent with one's</sup> expectations about banking sectors in various<sup>transition countries,thereby indicating that the</sup> DEA analysis could be successfully applied<sup>in studies of banking in transition economies.</sup> Despite concems about the quality of data, the<sup>analysis providesa somewhat plausible outcome</sup> in terms of the relative ranking of countries<sup>and sub-regions.Thus confident in the quality of the</sup> indicators, we proceed to the next chapter where<sup>we evaluatethe effectsof various institutional</sup> and policy settings on these indicators.

# Chapter 4. Second Stage Regression Results

The purpose of this section is to take a step further<sup>in analyzing banking sector</sup> productivity indicators by looking at their<sup>potentialdeterminants. In doing so let us assume that</sup> 15
the provision of banking services could be presented, in a simplified setting, by the following function:

# _Ya = f,(By,MRj,Ej)_

where yij measures output or efficiency of **_eth_** commercial bank operating in countryj, B1j denotes bank specific variables, Mj describes the macroeconomic environment in countryj, and Rj and Ej define the regulatory and general business environment respectively. Assuming that the above factors affect the bank efficiency and productivity in additive fashion, the coefficients of interest could be estimated using the following specification: The dependent variable is the efficiency index, DEA, calculated earlier in Chapter 4.

To control for bank-specific features, we included (1) equity as a share of total assets, (2) a bank's assets as a share of total assets of the banking system of its country of origin (i.e., market concentration), (3) a dummy variable for foreign controlled banks,<sup>20</sup> and (4) a dummy variable to account for new vs. old banks.<sup>21</sup> The following are indicators describing the underlying macroeconomic environment: GDP per capita, annual average rate of inflation, and monetary depth and size of the financial sector (measured by the ratio of broad money to GDP)<sup>22</sup> . It should be noted that including inflation in Equation 6 is intended to capture the

> 20 This variable takes the value of 1 if a bank is more than 30 percent foreign owned, and 0 otherwise.

21 This variable takes the value of 1 if a bank is newly established, and 0 if it was established before 1990.

> 22 Controlling for crisis in the region proved to be quite a challenging task, which we were unable to complete. The primary reason for giving up the idea of controlling for crisis is the mere definition of it. Although, it is the case that some events that affected transition countries in the course of the 90's had little or no cross-boarder repercussions (examples of these events would be those in Estonia and Latvia in 1995, Bulgaria in 1996, Czech Republic and Romania in 1997), at least one event in the history of the region had major sub-regional, if not regional, implications. It is the Russian crisis of August 1998. While this is clearly a major adverse shock to the banking sector and the economy of Russia as a whole (which one ideally would like to be

16
potential inefficiencies, which could<sup>take theform of both price-(e.g., high interest margins)and</sup> non-price (e.g., excessive branching) behavior,<sup>common for high inflationaryenvironments.2 3</sup> Because of difficulties with obtaining<sup>a consistentserieson a broad range of prudential</sup> requireInents, the choice of indicators<sup>describing the regulatory environmentwas the most</sup> difficult one to make. We, nevertheless,<sup>were able to construct a data series on (1)capital</sup> adequacy, (2) maximum exposure<sup>to a single borrower, and (3)a limit on the foreign exchange</sup> open position for four consecutive years,<sup>1995-1998.</sup> Differences relating to the institutional and<sup>generalbusiness environment are captured in</sup> the regressions by including the EBRD<sup>indexes of legal/institutionalquality and enterprise</sup> restructuring. Developments in capital<sup>marketsand non-bank financial institutionsand their</sup> effect on commercial bank performance<sup>are captured by (1)stock market capitalization,and (2)</sup> EBRD index of securities markets and<sup>non-bank financialinstitutionsdevelopment.</sup> Finally, sub-regional dummy variables<sup>are used (distinguishing betweenCentral</sup> European, South-East European/Baltic<sup>andCIS countries) to capture any differences in</sup> efficiency indicators not accounted for<sup>by the above measures.</sup>

# _Regression Outcome_

Due to the limited nature of the dependent<sup>variable(note that the DEA indexranges between 0</sup> and 1), a censored Tobit regression model<sup>wasused to estimate Equation 6.Unlike a</sup> conventional Ordinary Least Squares<sup>estimation,in cases with limited dependent variables, Tobit</sup> models are known to generate consistent<sup>estimatesof regressioncoefficients.The results of</sup> estimation are presented in Table 9.<sup>Overall, the regressions providea reasonably good fit and</sup> are robust with respect to the inclusion of<sup>new variables.The outcome is definitely worth paying</sup> some attention to!

able to control), it also had adverse effects<sup>on economiesof the entire region (theFSU countries</sup> in particular). It is a well documented fact<sup>that a number of countries in the region,(Armenia,</sup> Hungary, Kyrgyz Republic, Ukraine,<sup>to name a few)were perhaps hit almost as hard as Russia</sup> itself in terms of the effect on quality of<sup>bankassets,the stock markets,purchasing power of</sup> economic agents, etc. Now, the question<sup>is, do these countries qualify to get a value of 1 as far</sup> as the dummny variable for crises is concerned?<sup>Where does one draw the line?Yet, this said,</sup> we assume that the regression will still<sup>capture the events of the crisis since relevant information</sup> is likely to be contained in the rate of inflation,<sup>stock market capitalization,and per capita</sup> income.

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First, the results suggest that well capitalized banks are ranked<sup>higher in terms of their</sup> ability to collect deposits than their poorly capitalized counterparts.<sup>Thisis in linewith the</sup> conventional wisdom of capital playing a role of implicit deposit<sup>insurance,which in turn</sup> encourages more deposits. The rationale behind the link between capitalization<sup>and revenues is,</sup> however, less evident. Although the causality might run both ways,<sup>most studies found that well</sup> capitalized banks are more efficient (see, for example, Berger and<sup>Mester(1997)).A possible</sup> explanation for this could be based on the theory of moral hazard:<sup>the managersof banks that are</sup> closer to bankruptcy will be more inclined to pursue their own goals<sup>(knowingthe end is near),</sup> which are not necessarily in line with the owners' objectives.

Second, it appears that banks with a larger share of a given country's<sup>market are likelyto</sup> be more efficient than those with a smaller share. This might be<sup>thecase if banks wereto take</sup> advantage of economies of scale (or at least a wider array of borrowers),<sup>or play the role of a</sup> market maker on the loanable funds market. As far as the deposit-based<sup>performanceis</sup> concerned (DEA2), larger banks are likely to be viewed as too-big-to-fail,<sup>and thereforeenjoy</sup> higher credibility than their smaller counterparts. This phenomenon<sup>couldsubsequently show</sup> itself in lower premiums the depositors charge banks for their willingness<sup>to deposit their savings</sup> with them, hence lowering the banks' interest expenditures. In any case,<sup>this outcome is</sup> consistent with Demirguc-Kunt and Detragiache (1998), who find<sup>that financial liberalization</sup> (defined as removal of barriers to competition) makes a financial<sup>system weaker and a crisis</sup> more likely.<sup>24</sup> Therefore, we argue that given a level of institutional<sup>development -especiallyin</sup> a fragile transition environment - lower concentration, and therefore,<sup>morecompetition is likely</sup> to be associated with lower efficiency. As economic environment<sup>becomes stronger (and</sup> hopefully so is the institutional framework surrounding the banking<sup>system), the banksshould be</sup> able to handle more competitive pressures by responding in the<sup>usual efficiency-enhancing</sup> manner. The presence of this non-linearity in the behavior of competition-performance<sup>link</sup> becomes compelling if one compares banking systems in countries<sup>that undertook the path to</sup> consolidation (e.g., Estonia) with those where banking sectors<sup>remained relatively dispersed</sup> (e.g., Latvia and Lithuania). Getting off to a somewhat similar start<sup>with neighboring Latvia and</sup>

> 2 3 Examples where high inflationary environment led banks to build excessive branch networks include Argentina, Brazil, Turkey, to name a few.

24 The authors do show, however, that the impact of liberalization on<sup>banking sector fragility is</sup> weaker in countries with a more developed institutional environment.

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Lithuania, the banking system in<sup>Estonia was able to consolidateandweather most of the shocks</sup> which hit the banking system in the<sup>1990's and becomeone of thesuccess stories.2 5</sup> Third, banks with controlling foreign<sup>ownershipare likely to be more efficient than their</sup> domestically owned counterparts<sup>(includingstate-ownedand privatedomestic).Thisshould</sup> come as no surprise because of the<sup>abilityof foreignowned banksto capitalizeon their accessto</sup> better risk management and operational<sup>techniques,which is usually made available through</sup> their parent banks abroad. In<sup>addition, as foreign ownership is likely to be concentrated,foreign</sup> owned banks are less prone to typical<sup>corporate governanceconflict between (dispersed) owners</sup> and the management. The anecdotal<sup>evidencealso shows that well capitalizedforeign owned</sup> banks are more likely to cherry-pick<sup>the best borrowersavailableon the market(especially those</sup> from their own countries of origin),<sup>thereby improving the quality of their portfolioand</sup> increasing _expost_ returns. On the<sup>deposit side, owing to a popular perceptionthat,if necessary,</sup> a foreign owned bank will be<sup>bailed out by its more powerful parent institutionsabroad,foreign</sup> ownership plays a role of implicit<sup>deposit insurance. Therefore,foreign-ownedbanks would be</sup> likely to attract deposits by paying lower<sup>ratesthan their domestically owned counterparts. 26</sup> Fourth, on another bank specific variable<sup>included in the regressions (i.e.,dummy</sup> variable for new vs. old banks) the outcome<sup>is just as intriguing.It turns outthat after</sup> controlling for ownership, newly<sup>established banks are not necessarily moreefficientthan those</sup> that existed prior to 1990. Note<sup>that if viewedas a proxy for overall private ownership2 7(since</sup> banks that were established after<sup>1990 are likely to havea larger share of private ownership-</sup> both domestic and foreign - than<sup>banks that existed prior to1990), the resultsshed some light on</sup> the effect of domestic private ownership<sup>on efficiency.It appearsthat although positive,its</sup> effect is not statistically significant.<sup>If viewed together with the point made in the previous</sup> 25 The crisis of 1992 was caused by<sup>freezing by Russian authorities of accountsof 2 of the largest</sup> Estonian banks in Moscow, partly<sup>coupled with severe liquidity crunch imposed by theBank of</sup> Estonia (see Fleming _et al._ 1996).<sup>Yet this was relatively mild compared to the crises which hit</sup> Latvia and Lithuania in early and<sup>late1995 respectively.</sup> 26 A notable example of this taking<sup>place in a transition environmentis Yerevan-based HSBC-</sup> Armenia bank (originally set up<sup>as Midland-Armeniabank).For years being theonly foreign</sup> owned bank in Armenia and offering<sup>deposit rates,whichare 3-4times lower than those offered</sup> by its domestic counterparts, the bank<sup>has managed to increaseits share of the deposits over</sup> time.

27 It should be noted that the dataset<sup>did not contain information on state ownershipof the banks,</sup> and therefore it was impossible for<sup>us to explicitlycontrolfor domestic privateownership.</sup> 19
paragraph, this suggests that unless privatization leads to a controlling foreign ownership, privatization of banks does not lead to statistically significant improvements in efficiency. This finding reiterates the importance of factors that are usually associated with foreign ownership for banking operations in a transition environment: transfer of technology, better management, and favorable (transition country) public opinion.

Fifth, although there seems to be some indication that prudential regulations have impact on the efficiency of banks, the effects are not uniform across different prudential norms. Tighter minimum capital adequacy ratios seem to be associated with improved revenue generating capacity (DEAl) and more aggressive deposit taking behavior (DEA2).<sup>28</sup> On the other hand, the impact of a single borrower limit on efficiency is insignificant.<sup>29</sup> This is perhaps explained by banks' own increasing interest in diversifying away from large credit exposures, which is in turn facilitated by the availability of more advanced market instruments (including wider use of Treasury securities). This suggests that diversification objectives of regulatory authorities are generally in line with those of the banks, and that further tightening of single borrower exposure limits should come at no cost to banks. This is consistent with Barth _et al._ (2001) who find no link between diversification guidelines and banking development and efficiency.<sup>30</sup> Finally, banks in countries with relatively lax foreign exchange exposure regulations seem to be doing' better than those in countries with tighter policies. This could be explained by the fact that in some countries in transition, foreign exchange earnings constitute a big share of non-interest income, and tightening limits on foreign exposure effectively limits banks' ability to make extra income on foreign exchange transactions. Tighter standards on foreign exchange positions seem to affect banks' intermediation function as well (DEA2). This could be explained by the fact that most depositors in the region, perhaps rationally so, still prefer foreign currency denominated instruments to those denominated in domestic currency. Of course, should this be the case, depending on their asset position, banks will be restricted in their ability to borrow in foreign 28 This result runs contrary to Barth _et al._ (2001) who find that there is no relationship between stringency of capital requirements and bank performance.

29 Note that, unlike capital adequacy ratio, higher values of single borrower and foreign exchange exposure limits imply less stringent control.

30 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.

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currency. Overall, this indicates that further limiting<sup>foreign exchangerelatedexposure is likely</sup> to come at a cost.

Sixth, the coefficient on GDP per capita indicates<sup>that banks in higher per capita income</sup> countries are more efficient in terms of attracting<sup>more depositsand generatingstrongercash</sup> flows than banks in low income countries. This<sup>should not be surprisingas countries with higher</sup> per capita income (i.e., more developed countries) tend<sup>to generatemore savings,and hence</sup> more deposits.<sup>3</sup> Concerning other elements of the macro environment,<sup>our findingson the effect</sup> (or precisely lack thereof) of macro financial structure<sup>are consistentwith those of Demirguc-</sup> Kunt and Huizinga (2000) who report no effect of macro<sup>financial structure on commercial bank</sup> performance. An interesting point to note in this<sup>regard is that unlike theexperience of some</sup> countries (notably in Latin America), high inflation<sup>is not necessarilyassociated with large-scale</sup> inefficiencies, which could take the form of price- and non-price<sup>behavior,as hypothesized</sup> earlier.

Seventh, the insignificance of the coefficient on the quality<sup>of institutions is surprising,</sup> but by no means unexplainable. The effect of a developed<sup>legaland regulatory system is likely</sup> to be captured by other factors, such as foreign ownership<sup>or per capita GDP.It could be argued</sup> that a developed legal and regulatory regime is conducive<sup>to foreign entry, as it is likely to</sup> reduce the transaction costs (associated with entry)<sup>and the likelihoodof expropriation(of</sup> foreign assets by the state). In addition, it has been<sup>demonstratedthat a developedlegal</sup> framework and the rule of law are positively correlated<sup>with economicgrowth(see, for instance,</sup> Knack and Keefer, 1995). Should this be the case, of<sup>course,foreignownershipand per capita</sup> GDP indicators would be stealing some explanatory<sup>power away from the indicatorsof</sup> institutional quality, possibly making their effect<sup>on bankingefficiencyinsignificant.Finally,</sup> since countries grouped in the same sub-region are<sup>likely to be similar at leastin termsof the</sup> _enforcement_ of laws (i.e., a _defacto_ indicator of rule of<sup>law), if not in termsof the development</sup> of the legal system (i.e., a _dejure_ measure), the<sup>effect of institutional quality indicators on</sup> banking efficiency are likely to be captured also by sub-regional<sup>dummies.</sup> Eighth, as one should have expected, the EBRD indicators<sup>of enterprise restructuring</sup> have strong explanatory power in determining bank<sup>efficiency across countries. This carriesa</sup> 31 The link between the level of a country's development<sup>and revenue-basedefficiencyis less</sup> clear, as higher risks of investment projects may end<sup>up being outweighed by higher marginal</sup> returns on investments.

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strong message that privatization of state-owned enterprises, enterprise competition, and corporate governance related improvements (i.e., to name a few criteria covered by the EBRD enterprise restructuring index) are important in boosting commercial bank efficiency. This could obviously work through (i) improved efficiency of enterprises themselves (which then results in improved portfolio quality and revenue generation ability of banks), but also through (ii) intensified corporate governance pressures exerted by the newly emerging and stronger private sector on commercial banks.

Finally, there is some evidence that securities market and non-banik financial institutions development hinders the efficiency of banks. The results indicate that higher securities market capitalization reduces revenue-based efficiency, while more developed non-bank financial institutions hinder deposit taking. As unusual as these links may sound at first, they are not counter-intuitive. On the one hand, it is relatively straightforward to see that an opportunity to raise funds on the stock market would reduce the demand for bank loans by the best borrowers on the market. Less credits extended to the _cream de la cream_ would then manifest itself into lower _ex post_ returns and result in lower efficiency of banking operations. In that sense, this outcome supports the one on the competition-performance link made above. On the other hand, more developed non-bank financial institutions (i.e., pension funds, insurance companies, brokerage firms, etc.) would in all likelihood produce a greater demand for household savings, thereby reducing the amount of bank deposits available to the banks and eventually transaction services rendered by the banks.

# Chapter 5. Conclusion

This paper argues that to fully assess the efficiency of commercial bank operations it is necessary to model various types of functions performed by banks, and control for the inputs necessary to provide a certain level of utility to owners (i.e., profits) and depositors (i.e., services) while performing those functions. We therefore use Data Envelopment Analysis (DEA) to measure bank efficiency by stressing profit maximization and provision of transaction services as banks' primary objectives.

One of the contributions of this paper is that it demonstrates that DEA could be successfully applied to banking systems in transition countries. Despite certain concems about 22
the quality of data in less advanced transition<sup>countriesand its incompatibilitywith that of more</sup> advanced reformers, the analysis provides a plausible<sup>outcome in terms of the relativeranking of</sup> countries and sub-regions.

This analysis is the first attempt to consider differences<sup>in commercial bank efficiency</sup> across transition countries and a number of years against<sup>a wide array of variables describing</sup> macro environment, regulatory regime, institutional<sup>quality, enterpriserestructuring and non-</sup> bank financial sector development. The paper is, therefore,<sup>able to provide some answersto a</sup> range of fundamental questions of financial sector<sup>policy making in transition.</sup> First of all, it sheds some light on the question of the<sup>optimal architectureof a banking</sup> system. If combined together, the positive effects<sup>of capitalizationand market concentrationon</sup> DEA indicators suggest that banking sectors<sup>with few large, well capitalized banks are likely to</sup> generate better efficiency and higher rates of intermediation.

Second, interestingly enough, our findings on<sup>the effectsof prudentialtightening on the</sup> efficiency of banks vary across different prudential<sup>norms.Although policy actions in this</sup> regard may require deeper case-by-case analyses,<sup>we concludethat contrary to conventional</sup> wisdom, not all prudential requirements are detrimental<sup>to efficiency. Specifically,we found</sup> that (1) tighter minimum capital adequacy ratios<sup>are associatedwith stronger revenuegenerating</sup> capacity and more aggressive deposit taking<sup>behavior,(2) banks in countries with relativelylax</sup> foreign exchange exposure limits are doing better than<sup>those in countries with tighter policies,</sup> and (3) the single borrower-related limits<sup>do not effectbank performancein a statistically</sup> significant manner.

Finally, the results provide a useful lesson about<sup>the effect of privateshareownership on</sup> bank efficiency. They suggest that privatization<sup>of banks,beyond those involvinga transferof</sup> controlling share to foreign owners, does not results<sup>in statistically significant improvementsin</sup> efficiency. It would, however, be interesting to<sup>study whetherthe efficiencygains introduced by</sup> foreign ownership are due to better managerial<sup>control or to the transferof technologyfrom the</sup> banks' foreign owners.

The paper has a number of drawbacks. First, as indicated<sup>above,the analysismay suffer</sup> from a sample selection bias resulting from the fact<sup>that some countries (especially those in CIS)</sup> are poorly represented in the dataset. Since it is<sup>morelikely that only better performingCIS</sup> banks are in the sample, the bias is likely to underestimate<sup>the gap betweenaverage bank</sup> efficiency between CIS and other sub-regions. Second,<sup>some coefficients in the regression</sup> 23
(more than others) may suffer from endogeneity bias and, therefore, require proper<sup>treatment</sup> (such as Instrumental Variable approach) to account for it. An example of this is<sup>the coefficient</sup> measuring the impact of equity-to-asset ratio on performance. Clearly, as pointed<sup>out above, the</sup> causality could run both ways, so a further treatment of this link may be<sup>necessary.Yet this</sup> outcome is consistent with empirical findings of others, a fact that gave us sufficient<sup>confidence</sup> to report it without unduly complicating the analysis. Third, on a more conceptual<sup>level, the</sup> paper fails to account for any potential complementarity between a regulatory<sup>environmenton</sup> the one hand, and country- and individual bank-specific indicators on the other<sup>hand.Doing so</sup> might have shed some light on differences between the effect of regulatory measures<sup>on</sup> efficiency across countries with different levels of institutional and/or economic<sup>development.</sup> Nevertheless, we acknowledge these shortcomings and leave it for further research<sup>to properly</sup> address them.

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