Data Analysis and Hypotheses Testing

5.5.4.1. Analysis of Quantitative Data a. Model Specification

Various regression models are established to quantitatively test the research questions in the study. For instance, the model that tests the concentration-performance relationship is framed in a way to incorporate both structural and efficiency measures. The approach used is following the work of (Berger and Hannan, 1998) which directly incorporates efficiency measures so that the four hypotheses can be tested jointly and in way to avoid spurious regression. The four hypotheses are :

1. The SCP hypothesis – which claims that higher profits are the result of anti- competitive price settings in concentrated markets (measured by an industry concentration index like HHI and k-bank ratio)

2. The Relative Market Power hypothesis (RMP) -which states that firms with large market shares are able to exercise market power (measured by market share of

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banks) to earn higher profits. The difference between SCP and RMP is that the latter need not occur in concentrated markets.

3. The X-efficiency hypothesis (ESX)- firms with superior management or production processes operate at lower costs and subsequently reap higher profits. The resulting higher market shares may also lead to higher market concentration. X-efficiency as measured by the result from the DEA scores. 4. The Scale-Efficiency hypothesis (ESS) - firms have similar production and

management technologies but operate at different levels of economies of scale. Firms operating at optimal economies of scale will have the lowest costs and the resulting higher profits will lead to higher market concentrations. Scale efficiency as measured by DEA scores. Both versions of the efficient- structure hypothesis provide an alternative explanation for the positive relationship between profit and market structure.

5. In addition a test for Hicks (1935) 'quiet life' hypothesis- This hypothesis predicts a reverse causation, that is, as firms enjoy greater market power and concentration, inefficiency follows not because of non-competitive pricing but more so because of a relaxed environment that produces no incentives to minimize costs.

6. A test on competiveness of the Banking sector- is done through running a lag of the dependent variables.

7. A test on the control variables - a separate assessment on control variables based on their categories is done through formulating a regression model for the purpose.

Therefore, the model used to test the four hypotheses is set as:

Pit = f( lag Pit, Conct, MSit,XEFFit,SEFFit, Zit) + eit………. (5.1)

where :

• Pit, is a measure of performance of bank i, on time t, • lag Pit – one period lag of the dependent variable

• Conc. is a measure of market concentration for given year

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• XEFF is a measure of cost efficiency, indicating the ability of banks to produce a given level of output at minimum cost combination,

• SEFF is a measure of scale-efficiency, reflecting the ability of banks to produce at optimal output levels (economies of scale) for given similar production and management technology,

• Z is a set of control variables of bank I on time t and

• e is random error term.

Extensions to the basic model and contribution of the research

• Following the divergence in the literature over profit and priced based performance measures, this study considered both aspects of bank performance measures.

• The control variables incorporated diverse factors from the industry, macro- economy, regulatory and the specific banks.

Therefore, the model could be framed in modified form:

Pit = f(lag Pit ,Conct, MSit,XEFFit,SEFFit, EXit, REGt, BSt) + eit ……….(5.2)

Where: ME- external factors (macroeconomic and Industry factors) and BS- Bank specific factors and REG- regulatory factors.

b. Efficiency Measures

The efficiency measures are estimated by using non-parametric technique called Data Envelopment Analysis (DEA). The DEA model is a methodology for analysis of the relative efficiency for multiple inputs and outputs by evaluation of all decision-making units (DMUs) (Charnes et. al., 1978). The DEA measures efficiency performance in respect to the best practice banks, which is called efficient frontier. Some of the most important advantages of the DEA methodology, includes the lack of restrictions on the functional form, the different variables and values (e.g., ratios) which may be used, the possibility of measuring those variables in different units, and the fact that any deviations from the efficiency frontier are noticeable (Thanassoulis, 2001). However, it is sensitive to extreme observations and choice of variables as inputs and outputs.

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The study uses both the CCR and BCC models and their divisional output to compute for the scale effect or scale efficiency. CCR-model was developed by Charnes, Cooper and Rhodes (Charnes et. al. (1978). Its specific assumption is that the DMU operates under constant returns to scale (CRS). BCC-model was defined by Banker et. al., (1984). It estimates the efficiency under the assumption of variable returns to scale (VRS).

The basic DEA problem to estimate the relative efficiency of each bank is given by: θ *= Min θ subject to

∑ λj xij ≤ θxio i= 1,2,….m ∑ λj yrj ≥ yro r= 1,2,….s ∑ λj=1

λj≥ 0 j= 1,2,…….n

Where xio and yro are the i-th input and r-th output of the Bank under evaluation, respectively and θ is a bank-specific scalar that varies between zero and one and conveys the efficiency score of the specific bank. Banks with θi = 1 their input-output mix lies on the efficient frontier. The λ j is an Nx1 vector of bank-specific weights that conveys information on the benchmark comparators for bank i. A modification of the model with addition of the convexity constraint, ∑ λj=1 allows to compute efficiency under variable returns to scale (VRS) and disentangle technical efficiency from scale efficiency. The VRS model thus envelops the data more tightly and provides efficiency scores that are equal or greater than those of the CRS model (Banker et al., 1984).

DEA differs from a simple efficiency ratio in that it accommodates multiple inputs and outputs and provides significant additional information about where efficiency improvements can be achieved and the magnitude of these potential improvements. Moreover, it accomplishes this without the need to know the relative value of the outputs and inputs that were needed for ratio analysis (Cooper, Seiford & Tone, 2000). However, DEA is also subject to few limitations. DEA assumes data to be free of

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measurement error and that it is sensitive to outliers. Coelli et. al., (2005) also point out that having few observations and many inputs and/or outputs will result in many firms appearing on the DEA frontier.

The study uses the DEA to compute the efficiency score of banks and the aggregate industry. The estimated DEA efficiency scores (for both X and scale efficiency) are then used as regressors in a second-stage model in order to observe the relationship between efficiency and profitability. In addition, the scores are used to test whether there is efficiency variation among private and state owned banks.

c. Market Concentration Measures

Literature usually uses the top k-firms concentration ratio (CRk) and the Herfindhal-

Hirschman Index (HHI) to measure the market power.

i. K- firm (bank) Concentration Ratio

The concentration ratio is the percentage of market share held by the largest firms (k) in an industry. It shows the degree to which an industry is dominated by a small number of large firms or made up of many small banks. There is no rule for the determination of the value of k, so that the number of banks included in the concentration index is an arbitrary decision (Al-Muharrami, 2007). The higher the ratio, the more concentration in the banking sector providing the largest market power to big banks in the industry. The index approaches zero for an infinite number of equally sized banks and it equals unity, if the banks included in the calculation of the concentration ratio make up the entire industry. It takes the forms:

CRk= ∑ 𝑀𝑠𝑖 , where Msi is the market share of the k-banks

The concentration ratio indicates the relative size of k-large firms in relation to their industry as a whole. Normally 4-firm and 8- firm concentration ratios are used

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conventionally which assists in determining the market form of the industry. However, it ignores many small banks in the market (Wesman, 2005). In highly concentrated industry even a one bank concentration ratio results in a meaningful result. Based on the interview findings and the situations in the Ethiopian banking system, the study employs a one bank concentration ratio to measure the level of industry concentration.

ii. Herfindahl Hirschman Index (HHI-index)

The index was originally proposed and used in the field of industrial economics by Herfindahl (1950) and Hirschman (1964) independently of each other. The HHI is the most widely treated summary measure of concentration in the theoretical literature (Bikker and Haaf, 2000a). Unlike CRk which only indicates the relative size of the largest k-firms, the HHI accounts for the number of firms in a market, as well as concentration, by incorporating the relative size (that is, market share) of all firms in a market. It is calculated by squaring the market shares of all firms in a market and then summing the squares, as follows:

𝐻𝐻𝐼 = ∑𝑛_𝐾=1(𝑀𝑠𝑖)2

Where n is the number of banks in the banking sector, Msi is the market share of the bank k, k = 1, 2,…,n,.

HHI ranges from a number approaching zero to 10,000. Low concentration is indicated by HHI value of less than 1,000 and HHI of 10,000 implies high concentration, a case of pure monopoly. HHI includes all firms in the calculation. This means that more data needs to be collected. Squaring of the individual market shares of the firms gives proportionately greater weight to the market shares of the larger firms. Lack of information about small firms is not critical because such firms do not affect the HHI significantly (U.S. Department of Justice and Federal trade Commission,1992).

120 d. Variables

The purpose of the quantitative component of this study is to investigate the concentration-performance association as well as interactions among different control variables. The previous similar quantitative empirical studies have utilized most commonly used measures depending on publicly accessible data and the context of the country of study. This study also follows similar approach but additionally supported by the interview experience in selecting more reliable potential indicators. Therefore, during the process of identifying proxies, variables reflecting the unique characteristics of the Ethiopian banking industry have got strong focus. The main variables to be analyzed in the study as explained in the previous section and during the analysis:

1. Those that explain the performance of banks (Return on Assets, Return on Equity and Net Interest Margin) are used as dependent variable in all models. 2. Those related to the market structure applying various measures of market

concentration such as the top k-firms concentration ratio (CRk) and the

Herfindhal-Hirschman Index (HHI). Hence the market shares of banks in either or combined variables such as deposit and loan are utilized.

3. Those related to efficiency- based on the intermediation approach, a DEA is run in three inputs (deposit, branch, fixed asset) and two outputs (loans and other earning assets) with their corresponding prices for both inputs and outputs. Based on the stated inputs cost, revenue and profit efficiencies are computed. 4. Control Variables that fall under the control of the management are set based on

the CAMEL framework. External factors consisted of factors from the macro economy and industry (such as GDP growth, inflation, trade deficit, bank size , market growth, exposure to low cost deposit). Finally regulatory factors that are taken from the currently active regulatory framework are included. These include exchange rate, interest rate, entry capital, bank entry, reserve ratio, liquidity requirement, bill purchases).

The detail discussion on the variable setting is made in the conceptual framework as well as the analysis on each part of the control variables.

121 5.5.4.2. Hypotheses Testing

The research employs the following procedures in order to test the various hypotheses in the study. The procedure is a multi-stage process consisting of:

1. Employing DEA to get efficiency score on XEFF and SEFF 2. Measuring the market concentration and market share variables

3. Running the multiple regression model incorporating the concentration, efficiency extracted from the DEA and control variables.

4. Testing the Hypotheses for concentration-performance, efficiency-performance, quiet life and competitiveness.

5. Test on the control Variables from internal, external and regulatory factors

All the equations are estimated using panel data regression which allows differences in behavior across individual banks or overtime. Various variants of the panel data model: pooled ordinary least square (OLS), fixed effect and random effect are considered and various tests such as the F-test and Lagrange Multiplier (LM test) are applied to test for fixed and random effect, respectively and decide on applying the pooled OLS. A Hausmann test for fixed and random effect is employed to identify the optimal model in case of rejecting the OLS model.

5.6. Reliability and Validity in Quantitative Research