The Impact of Financial Liberalization
on Bank Efficiency:
Evidence in Developing Countries
Vu Thi Hong Nhung
MScBA Specialization Finance
The Impact of Financial Liberalization on
Bank Efficiency:
Evidence in Developing Countries
Vu Thi Hong Nhung
Program MScBA Specialization Finance
Faculty of Management and Organization, University of Groningen
Supervisor: Dr. Niels Hermes
Supervisor: MSc. Aljar Meesters
The Impact of Financial Liberalization on
Bank Efficiency
Evidence in Developing Countries
∗Abstract
The paper investigates the impact of financial liberalization on bank efficiency in developing countries from 1991-2000. In the first stage, the DEA results show that bank efficiency across countries fluctuates wildly. Latin-American banks have higher overall technical efficiency than those of Asian banks. The pure technical inefficiency rather than scale inefficiency is the main cause of overall technical inefficiency in developing countries. The panel least square fixed effects model analysis in the second stage provides stronger evidence that financial liberalization has a positive and significant impact on bank performance.
∗ I am grateful to my thesis supervisors Dr. Niels Hermes and MSc. Aljar Meesters. Many thank
Table of Content
1. INTRODUCTION ... 5
2. LITERATURE REVIEW ... 8
2.1 Treatment of Efficiency in Theory ... 8
2.2 Financial Liberalization and Bank Efficiency ... 11
3. METHODOLOGY ... 15
3.1. Methodology to Measure Bank Efficiency... 16
3.2. Second Stage Econometric Framework... 22
4. DATA AND VARIABLE SELECTION... 24
4.1 Definition of the Sample, Data Sources and Descriptive Information ... 24
4.2 Specification of Inputs and Outputs... 26
4.3 The Relevance of Control Variables... 30
5. EMPIRICAL RESULTS AND DISCUSSIONS ... 32
5.1 Results of Efficiency Analysis... 32
5.2 Regression Outcomes and Discussions... 37
6. CONCLUSION... 46
REFERENCES ... 49
List of tables and figures
Table 1. Number of sample bank observations by countries, 1991-2000... 24
Table 2. Value of total assets of sample banks and as a percent of total banking system assets, 1991-2000... 25
Table 3. Summaries the choice of inputs and outputs in previous research ... 27
Table 4. Correlation matrix of the variables ... 32
Table 5. Summary statistics ... 32
Table 6. Average bank efficiency by counties... 35
Table 7. Average bank efficiency by regions ... 37
Table 8. The panel least square regression of OTE and LIBER and control variables ... 41
Table 9. The panel least square regression of PTE and LIBER and control variables ... 44
Table 10. The panel least square regression of SE and LIBER and control variables ... 45
Figure 1.Technical and Allocative Efficiencies... 10
Figure 2. Pure Technical Efficiency and Scale Efficiency ... 20
Figure 3. Average Efficiency Results by Country... 55
Figure 4. Average Efficiency Results Compared across Countries... 57
Figure 5. Average Efficiency Results by Regions... 58
1. INTRODUCTION
Over the last two decades, the implementation of financial liberalization has brought substantial changes to the banking sector. The main goals of sector liberalization aim at enhancing competition, improving resource allocation, and acquiring more administrative efficiency in financial institutions through making them less state-directed and opening to foreign banks and non-bank financial institutions (Barajas and Steiner, 2000). Consequently, the implementation of financial liberalization programs puts financial institutions in more dynamic and competitive environment (Denizer et al., 2000). In order to survive and flourish in such a competitive environment, performance evaluation plays as an important role to encourage financial institutions to improve their performance. Wheelock and Wilson (1995) indicate that there are at least two important reasons for investigating banks efficiency. Firstly, efficiency measures are considered as indicators of success. Through efficiency evaluation, one can reveal the strengths and weaknesses of bank operations. During the 1980s high-cost banks had higher rates of failure than more efficient banks (Berger and Humphrey, 1991). Secondly, the potential impact of government policies on efficiency is another important reason for examining bank efficiency.
Although the main goal of financial liberalization and deregulation is to improve efficiency, Denizer et al. (2000) caution that the consequences of deregulation may differ across countries and may depend on the country’s characteristics prior to deregulation. While Leightner and Lovell (1998) and Barajas and Steiner (2000) find that financial liberalization has a beneficial impact on bank behavior, Hao and Hunter (2001) conclude that sector liberalization does not have a significant effect on bank efficiency. Moreover, it should be noted that while there are a number of researches considering the impact of liberalization programs in industrialized and transition countries (Berger and Humphrey, 1997, Denizer et
most of aforementioned researches focus on investigating the impact of financial liberalization in individual countries. To my knowledge, no literature so far has investigated the impact of financial liberalization on bank efficiency across developing countries especially in Asian and Latin American (LA) banks.
This study investigates the effect of financial liberalization on developing countries, especially for Asian and Latin American banking systems for several reasons. First, financial liberalization has been substantially implemented across Asian and LA regions during the early 1990s. By eliminating financing constraints, entry restrictions, interest control, etc., these policies aim at increasing competitiveness of national banking sectors (Williams and Nguyen, 2005, Aizenman, 2005). Second, financial liberalization is expected to have a large impact on the bank performance since these polices directly influence the bank’s operations. Third, the majority of studies undertaken in Asia and LA cover the period after or during deregulation without covering the period before liberalization programs. This may alter the real impact of liberalization programs. This study enhances the established literature because it analyses the data in the long period covering pre- and post-liberalization period.
efficiency score results from non-parametric mathematical programming, Data Envelopment Analysis (DEA), I relate these efficiency score with measures of financial liberalization (see Laeven, 2003) in the econometric model in order to obtain econometric evidence of the effect of financial liberalization on bank performance. I also control for other potential factors that affect bank performance such as bank specific features and the macroeconomic environment.
2. LITERATURE REVIEW
There are two main parts of literature that are relevant for this study: the theoretical efficiency concept and studies dealing with financial liberalization and bank efficiency.
2.1 Treatment of Efficiency in Theory
The theory of production considers the production process in which a firm utilizes
various resources (inputs) and produces goods and services (outputs) to satisfy the desires of its customers. Efficiency is an important characteristic of producer performance. Efficiency is considered as the degree to which the observed use of physical resources to produce outputs of a given quantity matches the optimal use of physical resources to produce outputs of a given quantity (Debreu, 1951).
Conventional microeconomic theory explicitly assumes that the producers are
successful in operating in an efficient manner. In particular, from a technical or engineering perspective, it is assumed that producers are optimizing their behavior i.e. they do not wasting resources. From economic perspective, producers are assumed to optimize by successfully operating at minimum cost (Fare et al., 1994). However, in recent years, an increasing number of writers have turned their attention to the possibility of inefficiency in production since they have realized that the existence of productive inefficiency can not be denied (Fare et al., 1985).
Farrell (1957), inspired by the work of Debreu (1951) and Koopmans (1951), is one of the most influential writers dealing with measuring firm efficiency. Farrell (1957) recommended that the efficiency of the firm consists of two components.
Overall technical efficiency reflects a firm’s ability to achieve maximal outputs
technical efficiency to measure economic efficiency. Some of Farrell’s terminology differ from recent literature terminology; for example he uses price efficiency instead of allocative efficiency and the term overall efficiency instead of economic efficiency.
In order to measure overall technical efficiency, focusing on input orientation, Farrell exemplified his ideas by using a simple example. In this case, a firm uses two inputs (x1 and x2) to produce a single output (y) under the assumption of
Figure 1.Technical and Allocative Efficiencies
Although the concepts of the different types of efficiencies are not too difficult to understand, researchers encounter substantial difficulties to measure them. The majority challenges is that efficiency measure is based on the best practice frontier which includes all fully efficient firms. However, the best practice frontier is not known in practice. Therefore the main task of estimating efficiency is to estimate the best practice frontier. In practice, the best practice frontier is defined in each case based on a set of observations and the purposes of each research. In particular, the alternative of production, cost, profit or other frontiers can be used. First, the production frontier is associated with the maximum attainable level of output from given a level of inputs, or the minimum level of inputs required to produce a given output. Second, cost frontier refers to the minimization level of cost (input prices) needed to produce a particular level of output. Thirdly, the profit frontier is associated with the maximum level of profits that can be obtained from a given set of output and input prices. These three types of frontiers have the same characteristic of optimality, derived from maximum or minimum of technology and prices. Efficiency levels are measured as the
distance from each observation to such a frontier. There are several techniques to estimate those frontiers (to be introduced in this thesis).
We have just investigated the concept of efficiency in the production theory. The
theory of the firm considers that the managers’ main aim is to manage firms to
operate in an efficient manner by maximizing firms’ profits and shareholders’ wealth (Isik and Hassan, 2003). The theory of production for financial firms emphasizes that financial firms, including commercial and saving banks, savings and loans associations, are also profit maximizing entities. They involve the production of intermediation services between borrowers and lenders (Hancock, 1990).
Efficiency and productivity of financial institutions have become more and more important in growing competitive business environments. Performance evaluation is considered as a necessary tool for financial firms to improve their business in order to survive and prosper.
2.2 Financial Liberalization and Bank Efficiency
in creditworthiness. The significance of the financial sector and its influence on other sectors demanded that the liberalization programs were undertaken. Starting in the 1980s, a number of countries implemented liberalization in banking sectors to aim at improving their efficiency and productivity. For instance, the United States ended a long trend toward stricter regulation in the late 1970s and began a deregulation era in the early 1980s (Brooks and Oh, 1999). Most European countries agreed to harmonize and unify the banking market under the objectives of the European Union in the 1990s. Asian and Latin American countries carried out financial liberalization during the early 1990s.
explained by the fact that the presence of foreign banks puts pressure on domestic banks to become more efficient by reducing costs. Moreover, domestic banks are likely to copy technology from foreign banks, so the technology spill-overs may help the formers to lower costs (Claessens et al., 2001).
effects on banks and they show that profitability tends to decline after 1997 due to the deterioration of business conditions.
Examining the effects of liberalization on bank performance across countries is sparse in the literature. Williams and Nguyen (2005) consider the impact of changes in bank governance on bank performance for countries in South East Asia from 1990 to 2003, the period of financial deregulation, the Asian crisis and bank restructuring programs. In particular, they investigate the empirical relationship between profit efficiency, technical change, productivity and commercial bank ownership. Their findings suggest that privatization policy encourages improving bank efficiency and productivity in South East Asia. Compared to private-owned banks, state-owned banks have operated less efficiently. In this paper, they do not have clear conclusion related to foreign acquisition. Consequently, they suggest that the benefit of foreign governance may take longer time to recognize. Pastor et al. (1997), by utilizing DEA approach, examine the productive efficiency across European banking markets from 1993 to 1997. The period was characterized by the process of European Union (EU) legislative management to create the Single Internal Market. Their main results show that the Single Internal Market Program has little influence on improving and converging bank efficiency levels. Country-specific factors are the main determinants of differences in European bank efficiency levels.
3. METHODOLOGY
3.1. Methodology to Measure Bank Efficiency
As discussed in section 2.1, the main task of measuring efficiency is to estimate the best practice frontier such as the (maximum) production frontier, the (minimum) cost frontier, or the (maximum) profit frontier, etc. The efficiency level is defined as the distance of each financial institution to such a frontier. Since the purpose of this research is to analyze the technical efficiency, the production frontier will be used. In this study, I do not focus on allocative efficiency, cost efficiency or profit efficiency since we need to have the input prices for cost frontier and both input and output prices for estimating the profit frontier. Data constraints of input and output prices make this study focus on technical efficiency and its components. It should be reminded that technical efficiency is related to degree of the production of outputs given some inputs. A firm is technically efficient if it produces optimal outputs with given inputs (output-orientation) or produces a given outputs with minimum inputs (input-orientation) (Favero and Papi, 1995). Firms are technically efficient if they operate on the production frontier and technically inefficient if they perform below the frontier. Technical efficiency is measured by the difference between the observed ratio of a firm’s output to input to the ratio achieved by the best practices on the production frontier.
Researchers find that which method is best to estimate the frontier is a sensitive matter. The parametric methods require specifying a particular functional form which shapes the form of frontier. The measure of efficiency may be biased due to specification errors if the functional form is misspecified. In contrast, the nonparametric methods do not require specifying the functional form for the frontier. The nonparametric approach assumes that measurement errors and other noises do not exist. If random errors exist, they may influence the shape and position of the frontier. In contrast, the random errors are taken into account in the function of the parametric approach. Therefore, the parametric approaches are likely to be more appropriate when the data are heavily influenced by random errors which account for measurement error and other random factors. However, when random influences are less, multiple-output is important to analyze, and prices are difficult to collect, etc. the nonparametric may be the optimal choice. Thus, the selection of the appropriate method should be made case-by- case. (Coelli et al., 1999).
Following Aly and Grabowski (1990), Ferrier and Lovell (1990), Berg et al. (1993), Bhattacharyya et al. (1997), Wheelock and Wilson (1995), and among the others, I use the nonparametric method, and especially the Data Envelopment Analysis (DEA), to calculate technical efficiency. DEA is the appropriate approach since in this paper technical efficiency is estimated based on multiple outputs of banks. Moreover, regulations and other market imperfections in developing countries may distort prices, and therefore it may be difficult to measure cost, revenue or profit functions when using parametric approach. (Bhattacharyya et al., 1997)
Farrell (1957), but was considered by only a few authors later. In the paper by Charnes, Cooper and Rhodes (1978) the data envelopment analysis was first used. Since then this approach has been applied by many researchers. Charnes, Cooper and Rhodes (1978) provide the basis for all subsequent developments in the nonparametric approach to calculate technical efficiency.
The DEA model
The following DEA model includes the assumptions of constant returns to scale and accounts for the objective of minimizing inputs for a given level of output (an input-orientated version of DEA). It proceeds by solving a linear programming model:
Subject to :
In this problem, assume there are N banks in the sample producing I different outputs (yin denotes the observed amount of output i for bank n) and using K
different inputs (xkn denotes the observed amount of input k for bank n). The λj
are weights applied across the N banks. θ is scalar (0≤θ ≤ 1).
It should be emphasized that the linear program must be solved N times, once for each bank in the sample. When the nth linear program (equation 1) is solved, the efficiency score for the nth bank,
n
θ *, is the smallest number ofθn that satisfies
the three sets of constraints listed above (equations 2, 3 and 4). θn will satisfy
0≤θn≤ 1, with a value of one indicating that this bank is on the production
frontier and hence it is a technically efficient bank. 1-θn gives the proportion by
which input can reduce to move the bank from the interior of the production frontier to the piece-wise linear boundary of the production set, with the quantity of output is held constant. In figure 1, θn corresponds with the ratio OQ/OP.
In order to minimizeθn, we base on the various changes of the weights λj and the
score θn itself. The weights λjdescribe the percentage of the other best practice
banks to construct the virtual bank (also lie on the frontier) that will be compared with the analyzed bank. The first constraint (equation 2) forces the weighted average of the other banks (the best practices) to produce at least as much of each output, as does analyzed bank nth. The second constraint (equation 3) finds out
how much less input the best practice banks would need. The third set of constraint (equation 4) simply limits the weights to being either zero or positive.
In the above linear programming problem, constant returns to scale (CRS) production technology is assumed, and the efficiency θn gives the “overall
market, managerial competence or other reasons may cause banks to be not operating at optimal scale. For some banks, when there are economies of scale (increasing return to scale), the amount increases all inputs will lead to more amount increases of outputs. In contrast, for the other banks, the size of those banks may become too large and diseconomies of scale (decreasing return to scale) may happen. In this case the amount increases all inputs will lead to less amount increases of outputs. Banker, Charnes and Copper (1984) suggest an extension of the CRS DEA model of Charnes, Cooper and Rhodes (1978) to account for variable returns to scale (VRS). Then, the overall technical efficiency can be further decomposed into its scale and pure technical efficiency components.
Figure 2. Pure technical efficiency and Scale efficiency
This is illustrated in figure 2 for the one input (x) and one output (y) case, and three banks A, B and C. A constant return to scale production frontier is illustrated by Og which measures the optimal level of output produced with a given level of input. Banks can lie on or below this frontier. For example for bank C, the overall technical efficiency (OTE) is measured by the ratio FE/EC, which corresponds to the ratio OQ/OP in Farrell’s definition in Figure 1. In order to calculate scale efficiency, the constant returns to scale is not appropriate; the
Under the variable returns to scale frontier, the pure technical efficiency (PTE) of bank C is determined by the ratio FD/FC. The difference between OTE and PTE, which is illustrated by the distance ED, is due to scale inefficiency. Thus, the scale efficiency is derived by the ratio FE/FD. The scale efficiency measure can be interpreted as the proportional reduction of input use to be obtained if the bank operates at the optimal scale (constant returns to scale).
In order to determine pure technical efficiency (PTE) the above linear programming problem is solved with the following additional constraint:
This additional constraint allows for variable returns to scale (VRS) production technology. This constraint ensures that the inefficient bank is only benchmarked against banks with similar size. This restriction is not imposed in the CRS case. Thus, in the CRS DEA model, a bank may be benchmarked against banks which are substantially larger or smaller than it is itself. The value PTE ranges from zero to one, one implying pure technical efficiency. The degree of scale efficiency ( SE ) is found by dividing the efficiency measure under the CRS assumption by the efficiency measure under the VRS assumption:
3.2. Second Stage Econometric Framework
The dependent variable yit is the average efficiency index DEA calculated above
of country ith at time t.
i differ across cross-sectional units to capture specific country effects.
LIBERit is the vector of dummy variables. Following Galindo et al. (2005), LIBER
variable equals one (zero), when the financial liberalization index, based on Laeven (2003) equals or exceeds five (is less than five) in country i at time t.
The financial liberalization index, based on Laeven (2003) (see Table 2 in his research p.17) is based on data reflecting the implementation of financial liberalization in six different areas. The financial liberalization index is the sum of six dummy variables which are each associated with one of six reform measures. Each dummy variable takes the value one in the year the liberalized regime was implemented. Therefore, the financial liberalization index may range from 0 to 6. The six dimensions of liberalization Laeven (2003) focuses on are following interest rates deregulation (both lending and deposit rates), reduction of entry barriers (both for domestic and foreign banks), reduction of reserve requirements, reduction of credit controls (such as directed credit, credit ceilings), privatization of state banks and strengthening of prudential regulation.
Xit is a vector of control variables including bank specific features and the
macroeconomic environment in country i at time t (to be introduced in the next sections).
4. DATA AND VARIABLE SELECTION
4.1 Definition of the Sample, Data Sources and Descriptive Information
This study is based on a data set of bank financial statement data taken from BankScope (CD-ROM version 1999 and 2002). The geographical coverage of this paper is ten countries: Argentina, Brazil, India, Indonesia, Korea (Republic), Mexico, Pakistan, Peru, Philippines and Thailand. The distribution of sample banks from 1991 to 2000 is presented in Table 1. I use an unbalanced panel data set to calculate efficiency scores for each bank using the DEA approach in the first stage, but I require at least three years observations of each bank. Table 2 contains the total asset value (thUS$) of selected banks and shares of assets of sample banks in total assets of the banking system in each country. As one can see, the dataset is representative for most of countries except in the case of the banking system for Indonesia, where the shares sample banks are lower than 40 percent in some years. The macroeconomic data were obtained from World Development Indicators (WDI) from World Bank.
Table 1. Number of sample bank observations by countries, 1991-2000
Regardless of which efficiency measure techniques are applied, the list of inputs and outputs of bank performance needs to be specified. The choice of variables in efficiency research considerably affects the results. According to Favero and Papi (1995), there are five common approaches to specify input and output of bank activities. Three of these approaches are related with bank’s functions such as the production approach, the intermediation approach, and the asset approach. The two other methods are not related to the microeconomics of bank functions such as the user-cost approach and the value added approach. Table 3 provides a summary of input and output approaches used in previous research. According to the production approach, banks play a role to produce services to depositors and borrowers. Traditional production factors such as land, capital and labor, but not including interest cost, are used as inputs to produce outputs specified by the number of accounts serviced or transactions processed (see Ferrier and Lovell (1990) for a detailed discussion).
has been widely used in banking studies (Aly and Grabowski, 1990, Berger and Humphey, 1991, Hunter and Timme, 1995, etc.)
According to the user-cost approach, to determine a particular asset or liability item is an input or an output, we have to consider its net contribution to bank revenue. Hancock (1991) who was among the first to apply the user cost approach to banking shows that if for a certain asset the financial returns exceed the opportunity cost of funds (or if for a liability the financial costs are less than the opportunity cost), the instrument is considered a financial output, otherwise it is identified as an input. Under Hancock’s rule, demand deposits are specified as outputs while time deposits are considered as inputs. Favero and Papi (1995) and Grigorian and Manole (2002) also reveal the shortcomings of this approach. They state that the difficulties are in collecting accurate data since interest rate fluctuations lead to changes in the user cost. Moreover, marginal revenues and costs for each individual liability are difficult to measure.
Finally, according to the value added approach, both liability and assets that have substantial value added are considered as outputs, while the others are treated as inputs or intermediate products. This approach has been used in Berg et al. (1993), Grigorian and Manole(2002), etc.
Table 3. Summaries the choice of inputs and outputs in previous research
Study Inputs Outputs Approach
Aly and Grabowski (1990) Labor, physical capital, and loanable funds
Demand deposits, real estate loans, commercial loans, consumer loans and other loans.
Table 3. (continued)
Ferrier and Lovell (1990)
Labor (employees), occupancy costs and expenditure on furniture and equipment, expenditure on materials. Number of demand deposit accounts, number of time deposit accounts, number of real estate loans, number of
installment loans, number of commercial loans. Production approach Berger and Humphrey (1991) Labor, physical capital, and purchased funds
Demand deposits, time and purchased funds savings deposits, real estate loans, commercial and industrial loans, installation loans Intermediation approach Berg et al. (1993)
Labor (man-hours per year), capital
Loans, deposits, services (number of branches)
Value added approach
English (1993)
interest bearing small deposits (deposits less than $100,000), labor, occupancy expense and purchased or borrowed funds
real estate loans, commercial loans, consumer installment loans and investments in US securities, federal funds sold and assets in trading accounts.
Table 3. (continued) Hunter and Timme (1995) Labor, physical capital, and purchased funds, transaction accounts and non-transaction accounts under $100,000
Security and commercial loans, consumer loans, other loans, and non interest income Intermediation approach Grigorian and Manole (2002)
Labor, fixed asset and investment
expenditure
(1) Revenues, net loans, and liquid assets
(2) Deposits, net loans and liquid assets
Value added approach
In the light of these considerations, this paper utilizes the intermediation approach employed by Aly and Grabowski (1990) and Berger and Humphrey (1991), etc. Due the data availability, this paper modifies the standard intermediation approach since the number of employees and details on loan accounts such as real estate loans, commercial loans, installation loans, etc. can not be collected. The inputs used to calculate various efficiency measures are labor, physical capital and loanable funds. Labor is measured by personnel expenses. Physical capital is the total book value of fixed assets, other earning assets and non-earning assets. Loanable funds include time and savings deposits, commercial deposits, bank deposits and certificates of deposits. All of these inputs are measured in thousands of dollars. Two outputs used in this study (also measured in thousands of dollars) are demand deposits and net loans1.
4.3 The Relevance of Control Variables
Apart from financial liberalization, banking sector efficiency may also be influenced by bank specific features and the macroeconomic environment of a country. Therefore, to control for different effects of the macroeconomic environment and bank industry features, I include a set of variables that reflect other factors affecting bank efficiency, see vector X in equation (7).
I choose variables to control for country conditions since I analyze national banking efficiency. This group of variables consists of the density of demand, GDP growth, and inflation rate. The density of demand (DD) defined as the ratio of total value of deposits (measured in US$) per square kilometer is considered a relevant feature to affect bank efficiency (Dietsch et al., 2000, Lozano-Vivas et
To allow for the effect of banking technology and service quality, a set of variables such as the average capital ratios, the profitability ratios and the intermediation ratios are included to characterize the structure of the banking industry. The average capital asset ratio is measured by equity over total assets (ETOA) which is considered as a proxy for regulatory conditions (Dietsch et al., 2000, Lozano-Vivas et al., 2001, 2002). There are both negative and positive influences of the capital asset ratio on efficiency levels. Berger and DeYoung (1997) suggest that the higher the capital ratios, the lower the bad loan levels, which leads to lower additional costs to recover these loans. Therefore, these banks appear more efficient. Dietsch et al. (2000) and Lozano-Vivas et al. (2001) also support the positive relationship between bank efficiency and the capital ratio by arguing that a lower capital ratio leads to lower efficiency levels since it involves higher risk taken and greater levels of leverage. However, there is also theoretical argument to support the opposite. A low capital ratio causes moral hazard behavior, which encourages banks to undertake risky business by investing in highly profitable projects. This may help banks obtain efficiency in the short term. However, they will be likely to pay for the negative consequences of their risky behavior in the long term. (Lozano-Vivas et al., 2002). Therefore, the sign of ETOA is indeterminate. Next, the profitability ratio (ROE), used as a proxy of competitiveness conditions in banking industry, is defined as average return over equity. This ratio is expected to have a positive impact on efficiency in case of competitive markets (see, e.g., Lozano-Vivas et al. 2001, 2002). Finally, the intermediation ratio (LTD), measured by average total loans to total deposits, is used as an indicator to reflect differences among banking sectors in term of the extent to which deposits are converted into loans. (Dietsch et al., 2000 and Fries
et al., 2005). The higher the intermediation ratio, the more efficient levels are since banks they incur lower expenses.
variables except for ETOA and LIBER. However, ETOA and LIBER have different characteristics. LIBER is a dummy variable. Therefore, I decide to include all independent variables as a set of exploratory variables in the model.
Table 4. Correlation matrix of the variables
LIBER DD GDPGRO INFLA ETOA LTD ROE
LIBER 1 DD 0.03019 1 GDPGRO -0.22963 0.129313 1 INFLA -0.25853 -0.07449 -0.01498 1 ETOA 0.504903 -0.17389 0.01157 -0.15973 1 LTD 0.109256 -0.03667 0.046845 -0.0263 0.062394 1 ROE -0.12039 -0.08059 0.097081 -0.01717 -0.06378 -0.00339 1
Before analyzing the findings, I perform explorative data analysis (EDA) of the variables used in the model. The descriptive statistics are given in Table 5. Skewness and kurtosis statistics show to what extent bank characteristics and country variables fluctuate across the sample.
Table 5. Summary statistics
Mean Median Maximum Minimum Standard Deviation Skewness Kurtosis Observations CRSE 0.72567 0.74935 0.99209 0.39798 0.139181 -0.37772 2.403385 100 VRSE 0.819483 0.827152 1 0.502778 0.110901 -0.63334 3.095246 100 SE 0.879522 0.891295 1 0.67011 0.070715 -0.7621 3.127522 100 LIBER 0.61 1 1 0 0.490207 -0.45105 1.203447 100 ETOA 0.06816 0.067283 0.191284 -0.03521 0.042869 0.397191 2.821412 100 DD 729.3121 55.41324 9079.175 2.997803 2096.204 3.212154 11.80162 100 INFLA 70.01115 8.221141 2075.887 -1.1669 299.0328 5.784586 36.67487 100 GDPGRO 4.272985 4.805157 12.8224 -13.1267 4.268432 -1.27519 6.123957 100 LTD 1.643234 0.572685 87.81599 0.042804 8.77871 9.611907 94.74289 100 ROE 0.119207 0.030374 10.97628 -1.51443 1.137106 8.805106 84.90987 100
5. EMPIRICAL RESULTS AND DISCUSSIONS 5.1 Results of Efficiency Analysis
consist of three sets of efficiency measures, are presented in Table 6. They are the overall technical efficiency (OTE), which is generated using the constant returns to scale model (CRS), the pure technical efficiency (PTE), which is produced using the variable returns to scale model (VRS) and the overall scale efficiency (SE), which is derived as the OTE to PTE ratio. It should be reminded that OTE is affected by the size of banks (SE) and managerial practices (PTE). In this study, following the intermediation approach, I use labor, physical capital, loanable funds as bank’s inputs and demand deposits, net loans as bank’s outputs. Thus, in this case, the PTE refers the degree of bank’s managerial and marketing skills in using their resources (physical capital), personnel expenses and purchased funds to maximize demand deposits and transform deposits into loans. Banks are managerially efficient if they can invest rationally into practical assets, control operating expenses such as salary expense, etc. and manage loanable funds in attracting a large amount of demand deposits and having good loan plans which differentiate creditworthy borrowers and those of defaults. Thus, the pure technical inefficiency directly results from under control of banks themselves and relates to management incompetence. Besides, SE refers to the size of banks which influences bank’s ability to produce services efficiently. Banks in the developing countries can take advantage of spreading their fixed costs in investing practical assets. If the economies of scale exist, this leads to the increases in the bank’s outcomes to be more than the increases in investing physical capital. Particularly, investing in new products and technology such as ATM networks, credit cards, internet banking, etc. are expected to improve the quality services of the banking system and hence generate higher efficiency.
these countries. The national frontiers do not follow this assumption. Moreover, the common frontier may not capture cross-country differences (Berger and Humphrey, 1997). Based on these arguments, I argue that applying the national frontier is appropriate in this thesis since the developing countries in my study may not have the same technology and I want to capture the differences of bank efficiency across countries.
Remember that the main hypothesis of this thesis is that financial liberalization policies have a positive impact on bank performance. I expect annual average bank efficiency scores of these countries will increase over time.
(1999, 2000). The banking system of Pakistan (1991), Mexico (1992, 1998, 2000), Peru (1993), Argentina (1994), Peru (1995 to 1997) and Philippines (1999) have smaller scale inefficiencies.
Table 6. Average bank efficiency by counties
Table 6 (continued) Philippines OTE 0.84 0.83 0.81 0.69 0.80 0.73 0.67 0.71 0.75 0.70 PTE 0.94 0.89 0.84 0.79 0.89 0.86 0.80 0.79 0.81 0.81 SE 0.89 0.94 0.97 0.88 0.91 0.85 0.83 0.90 0.93 0.87 Thailand OTE 0.87 0.80 0.87 0.74 0.74 0.54 0.81 0.60 0.54 0.52 PTE 0.97 0.87 0.92 0.83 0.81 0.68 0.87 0.73 0.72 0.74 SE 0.90 0.91 0.94 0.89 0.91 0.79 0.93 0.83 0.75 0.71
Next, the banking systems in the countries are grouped into two regions, Asia and Latin America (LA). In general, Table 7 and Figure 5 present the findings of overall technical efficiency, pure technical efficiency and scale efficiency for both regions. In general, Asian banks have considerably lower overall technical efficiency than LA banks. The pure technical efficiency of the Asian banks and LA banks are not substantially different. The major source of lower OTE in Asian banks than those in LA banks is due to low SE. The scale efficiency ranges from 82% to 93% for Asia banking systems and from 83% to 97% for LA banking systems. This means that the Asian banks suffer from 7% to 18% efficiency losses due to scale effects while this percentage in LA banking systems only ranges from 3% to 17%.
managers should have loan policies that evaluate prudently creditworthiness of borrowers.
Table 7. Average bank efficiency by regions
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Asia OTE 0.70 0.68 0.67 0.64 0.62 0.60 0.62 0.55 0.53 0.52 PTE 0.92 0.84 0.86 0.79 0.80 0.76 0.82 0.78 0.77 0.79 SE 0.93 0.88 0.91 0.88 0.85 0.82 0.88 0.83 0.86 0.82 Latin America OTE 0.87 0.81 0.90 0.76 0.74 0.68 0.80 0.63 0.67 0.65 PTE 0.92 0.87 0.92 0.82 0.83 0.80 0.88 0.74 0.77 0.74 SE 0.94 0.93 0.97 0.91 0.89 0.83 0.91 0.84 0.86 0.87
In general, the DEA results show that overall technical efficiency, pure technical efficiency and scale efficiency across countries fluctuated wildly during the period 1991-2000. I can not see a clear trend of increasing or decreasing efficiency in the selected countries during 1991-2000. One possible explanation for this result is the effect of the financial crisis in Asian and LA countries which may have distorted the effect of financial liberalization programs. Moreover, as mentioned by Berger and Humphrey (1997), the regulatory and economic environment faced by financial institutions and the level and quality of services related to deposits and loans are likely to differ across nations. Thus, these factors may influence efficiency results. In order to examine further the effect of financial liberalization on bank efficiency, and control for other factors which seem to influence bank performance, I continue to investigate econometric model outcomes.
5.2 Regression Outcomes and Discussions
the specific-to-general approach. I start by estimating the base line model which investigates only the effects of financial liberalization on efficiency. Next, I include each control variable to the base line model one by one. In this way, I want to test the stability of the main independent variable LIBER. In addition, I adjust for cross-section heteroskedasticity to make robust estimates of standard errors by using White cross-section tests since the cross-sectional units (countries in this case) may have varying sizes and characteristics which may lead different variations in regression disturbances (Baltagi, 1995). I also consider the limited nature of the dependent variable since the DEA efficiency scores range between 0 and 1 by examining the consistent estimates of regression coefficients.
Column 1 in Table 8 presents the base line model, which regresses the overall technical efficiency on LIBER. The result shows that the coefficient of LIBER is positive and significant at the 1% level. This result is supportive to the hypothesis that financial liberalization leads to an improvement in bank efficiency.
for resurrection” by undertaking risky business which appears efficient in the short term.
The second control variable related to bank specific features is the intermediation ratio (LTD) added in column 3. The findings reveal that the intermediation ratio do not affect overall technical efficiency since the coefficients of LTD are statistically insignificant. This result differs from Fries et al. (2005), who find a negatively significant association between the intermediation ratio and bank efficiency in terms of cost efficiency.
Next, the profitability ratios (ROE) are added in column 4 as a proxy of competitiveness conditions in banking industry. The results support our expectation that the lager the profits, the higher the efficiency. This is evident from the coefficient of ROE which is positive and significant at the 1% level. Interestingly, when more control variables are added in column 3 and 4 such as
LTD and ROE, the coefficient of LIBER is still positive and significant at the 1% level and the value of coefficient of LIBER is stable when we add more variables.
We now turn to the second group control variables concerning the macroeconomic environment. The first variable in this group is the density of demand (DD), included in column 5. Based on previous studies, we argue that banks operating in markets with lower density of demand incur higher costs in mobilizing deposits and making loans, which may cause lower efficiency. Opposite to what is expected, the coefficient of DD is negative and significant at the 1% level. Although the coefficient of DD is small, the value of DD (see Table 5) is quite high, DD may influence significantly on the overall technical efficiency.
rate is not necessarily related to higher inefficiencies which were argued earlier since the coefficients of INFLA are statistically insignificant.
Finally, the GDP growth rate (GDPGRO), considered as a proxy of overall economic development is added in column 7. The coefficient of GDPGRO is positive and significant at the 1% level. The results indicate that banks operating in countries with higher GDP growth are more efficient due to corresponding quality and skill improvements of financial institutions. It should be noted that, when more control variables concerning the microeconomic environment are added, the coefficient of LIBER is still positive and significant at the 1% level. Moreover, the value of coefficient of LIBER is stable when including more control variables in the regression.
sign C 0.678878*** (0.011968) 0.731331*** (0.018271) 0.731351*** (0.018309) 0.731038*** (0.018622) 0.756960***(0.025583) 0.752142*** (0.025017) 0.749224*** (0.019988) LIBER + 0.076708*** (0.019619) 0.079890*** (0.020077) 0.079729*** (0.020227) 0.082723*** (0.022316) 0.083733*** (0.021729) 0.084360*** (0.020669) 0.088283*** (0.020085) ETOA +/- -0.798030*** (0.233328) -0.799775*** (0.233424) -0.841675*** (0.264635) -1.006926*** (0.338042) -0.959937*** (0.318266) -1.129506*** (0.274026) LTD + 0.000120
(0.000443) 6.25E-05 (0.000430) 4.57E-05 (0.000405) 2.69E-05 (0.000402) -0.000265 (0.000467)
ROE + 0.012061***
(0.004448) 0.012323** (0.004902) 0.012307** (0.004941) 0.011742** (0.004989)
DD + -2.09E-05***
(6.64E-06) -2.10E-05*** (6.66E-06) -2.28E-05*** (6.81E-06)
INFLA - 1.89E-05
(3.87E-05) 1.54E-05 (3.80E-05)
GDPGRO + 0.003307*** (0.001487) R2 0.706565 0.728522 0.728568 0.736487 0.749572 0.750538 0.756439 Adjusted R2 0.636874 0.659794 0.655490 0.661197 0.673785 0.670710 0.674154 Observations 100 100 100 100 100 100 100 F-statistic 10.13857 10.60000 9.969762 9.782063 9.890451 9.401937 9.192988 Prob(F-statistic) 0 0 0 0 0 0 0
and control variables. In Table 9, column 1 to column 7, coefficients of LIBER are positive and significant at the 1% level. The capital ratio (ETOA) and the density of demand (DD), similar to the results in Table 8, continue to have negative coefficients which are significant at the 1% level. In addition, the coefficients of the profitability ratios (ROE) are positively significant at least at the 5% level. Surprisingly, the coefficient of the GDP growth rate (GDPGRO), which was positively significant when examining the regression with overall technical efficiency, turns statistically insignificant in Table 9. I also find no evidence for the relationship between pure technical efficiency and the remaining control variables such as the inflation rate (INFLA) and the intermediation ratio (LTD). The explanatory power of these regressions is still high. The R-squared ranges from 68% to 74% and the adjusted R-squared varies from 60% to 65%. The F-statistics for all estimations are significant at the 1% level.
sign C 0.791080*** (0.009504) 0.841529*** (0.015333) 0.841576*** (0.015381) 0.841370*** (0.015017) 0.866748*** (0.016228) 0.865003*** (0.015839) 0.863864*** (0.013615) LIBER + 0.046562*** (0.015580) 0.049623*** (0.016252) 0.049242*** (0.016085) 0.051214*** (0.017514) 0.052203*** (0.016315) 0.052430*** (0.016233) 0.053962*** (0.015562) ETOA +/- -0.767553*** (0.160891) -0.771678*** (0.160811) -0.799270*** (0.172838) -0.961054*** (0.222293) -0.944042*** (0.197281) -1.010262*** (0.177310) LTD + 0.000284 (0.000358) 0.000246 (0.000334) 0.000230 (0.000307) 0.000223 (0.000296) 0.000109 (0.000346) ROE + 0.007943*** (0.002987) 0.008199** (0.003551) 0.008193** (0.003562) 0.007972** (0.003635) DD + -2.05E-05***
(6.12E-06) -2.05E-05*** (6.15E-06) -2.12E-05*** (6.06E-06)
INFLA - 6.83E-06
(3.57E-05) 5.48E-06 (3.58E-05)
GDPGRO + 0.001291 (0.001187) R2 0.680803 0.712795 0.713200 0.718608 0.738362 0.738561 0.739979 Adjusted R2 0.604994 0.640085 0.635984 0.638210 0.659182 0.654901 0.652134 Observations 100 100 100 100 100 100 100 F-statistic 8.980467 9.803261 9.236481 8.938162 9.325121 8.828096 8.423692 Prob(F-statistic) 0 0 0 0 0 0 0
Table 10. The panel least square regression of SE and LIBER and control variables Variables Expected sign (1) (2) (3) (4) (5) (6) (7) C 0.851268*** (0.012670) 0.863026*** (0.019582) 0.862984*** (0.019845) 0.862815*** (0.020370) 0.864973*** (0.021835) 0.857832*** (0.021998) 0.855571*** (0.020336) LIBER + 0.046318** (0.020771) 0.047031** (0.020946) 0.047373** (0.021186) 0.048985** (0.022084) 0.049069** (0.022302) 0.049997** (0.021075) 0.053038** (0.020892) ETOA +/- -0.178881 (0.224432) -0.175176 (0.225386) -0.197740 (0.238407) -0.211495 (0.252890) -0.141863 (0.252890) -0.273306 (0.242260) LTD + -0.000255 (0.000307) -0.000286 (0.000319) -0.000288 (0.000321) -0.000315 (0.000318) -0.000541 (0.000336) ROE + 0.006495* (0.003348) 0.006517* (0.003397) 0.006493* (0.003467) 0.006055* (0.003460) DD + -1.74E-06
(3.09E-06) -1.85E-06 (3.08E-06) -3.19E-06 (3.44E-06)
INFLA - 2.80E-05
(1.69E-05) 2.53E-05 (1.59E-05)
GDPGRO + 0.002563*** (0.000934) R2 0.557933 0.562207 0.563009 0.571905 0.572256 0.580471 0.594206 Adjusted R2 0.452943 0.451373 0.445358 0.449592 0.442807 0.446221 0.457113 Observations 100 100 100 100 100 100 100 F-statistic 5.314116 5.072531 4.785401 4.675755 4.420715 4.323826 4.334342 Prob(F-statistic) 0 0 0 0 0 0 0
First of all, the findings show that financial liberalization programs have a positive and significant impact on bank efficiency across developing countries. These outcomes contribute to fill the gap of mixed results of specific country studies in the literature. Generally speaking, this result is supportive to the initial hypothesis. Interestingly, financial liberalization has stronger influences on technical efficiency than on scale efficiency. Secondly, the results show that both the capital ratios and the density of demand have a negative and significant impact on efficiency (except for scale efficiency). This result contradicts with Grigorian and Manole (2002). They argue that well capitalized banks are likely to generate better efficiency. The possible explanation for our results is that liberalization policies of removing entry barriers, privatization of state banks, etc., put banks under pressure of high competition. Several previous studies use moral hazard behavior theory to argue that banks in distress with less equity are likely to pursue risky projects providing high profits. This may result in efficiency in the short term but ignores the negative consequences in the long term. Finally, the results confirm the significant and positive relationship between the profitability ratio and the GDP growth rate, and bank efficiency in the earlier hypotheses. Taken together, the positive effect of the GDP growth rate and the profitability ratio suggest that, banks operating in higher economic development conditions and having higher profit ratio are likely to generate better technical efficiency and take advantage of efficient scale size.
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APPENDIX
Appendix 1: Summary of the variables in the regression Dependent variable
OTE average overall technical efficiency, calculated in the CRS DEA model
PTE average pure technical efficiency, measured in the VRS DEA model
SE average overall scale efficiency, defined as OTE/PTE
Independent variables
LIBER is the dummy variables. LIBER = 1 (0) when financial liberalization index, based on Laeven (2003) equals or exceeds five (is less than five) in country i at year t. Financial liberalization index, based on Laeven (2003) ( see Table 2 in his research) is calculated as the sum of zero-one dummies representing six dimensions of liberalization (interest rate deregulation, removal of entry barriers, reduction of reserve requirements, reduction of credit controls, privatization of state banks, strengthening of prudential regulation). One (zero) denotes the post (pre) reform regime.
Bank level variables
ETOA capital ratio, defined as total equity/total assets
LTD intermediation ratio, calculated as total loans/total deposits
ROE profitability ratio, measured as returns/equity
Country level variables
DD density of demand, calculated as total value of deposits (US$)/ square kilometer
GDPGRO annual real growth rate of GDP
FIGURES
Figure 3. Average efficiency results by country
Figure 4. Average efficiency results compared across countries Overall technical efficiency
0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1.2000 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year O TE
Argentina Brazil India Indonesia Korea, Rep. Mexico Pakistan Peru Philippines Thailand
Pure technical efficiency
0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1.2000 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year P TE
Scale efficiency 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1.2000 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year S E
Argentina Brazil India Indonesia Korea, Rep. Mexico Pakistan Peru Philippines Thailand
Figure 5. Average efficiency results by regions Overall technical efficiency
0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year O TE
Pure technical efficiency 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year P TE
Asia Latin America
Scale efficiency 0.7000 0.7500 0.8000 0.8500 0.9000 0.9500 1.0000 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year S E
Figure 6. Average efficiency results in each regions Bank efficiency in Asia
0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year E ff ic ie n cy s co re CRSE VRSE SE
Bank efficiency in Latin America
