Competition in Banking

- Suryam Babu Dirisam

Abstract:

This paper analyzes the degree of concentration, competition, efficiency and their relationship in

the Indian banking sector over the period 1980-2011. The sample period is divided into three

sub-periods: pre-reforms period (1980-89) liberalization period (reforms 1990-1998), and post-

reform period (1999-2011). The analysis is carried out using bank level balance sheet data by

employing structural measures (CR3, CR5, CR8 and HHI on total assets and deposits), non-

structural measures (Panzar – Rosse model on total revenue and interest revenue as the

dependent variables), Data Envelopment Analysis (DEA), and Granger-causality test. Our results

indicate that Indian banking sector was highly concentrated, efficient and competitive in the pre-

reforms period. However it is not the case in the reforms and post-reforms periods. There is a

significant declining trend in terms of concentration, efficiency and competition. Granger-

causality test reveals that when the competition is measured using total revenue as the dependent

variable then efficiency Granger-causes the competition and competition does not Granger-cause

the efficiency. On the other hand, when the competition is measured using interest revenue as the

dependent variable then competition Granger-causes the efficiency and efficiency does not

Granger-cause the competition. On the whole, Indian banking industry operates under

monopolistic competition.

JEL classification: C22 C23; G21; L12

1. Introduction

Financial deregulation, advance in information technology and financial globalization triggered

fierce competition among banks and necessitated consolidation to reduce risk through business

diversification and take advantage of scale and scope economies. The competition effect would

depend on the degree of concentration, the degree of entry barriers, the heterogeneity of

products, and price differentiation allowed. Depending on the level of competition of the banking

industry, consolidation influences the provision of credit to different customer groups. There are

divergent views on the relationship between concentration and competition. Some argue that

concentration will intensify market power and thereby obstruct competition and efficiency.

Others argue that economies of scale drive bank mergers and acquisitions so that increased

concentration goes hand-in-hand with efficiency improvements. In terms of stability, greater

concentration may augment the size, market power, and profits of banks and thereby enhance

diversification and create greater incentives for secure banks to avoid imprudent risk-taking. The

standard economic argument for the positive influence of competition on firms’ performance is

that the existence of monopoly rents gives managers the potential to capture some of them in the

form of slack or inefficiency. Therefore, deregulation-induced competition should in turn

translate into incentives for managers to improve efficiency and performance. On the other hand,

the aim of prudential re-regulation is to foster stability and minimize excessive risk taking. As a

consequence, it imposes higher costs and could hamper competition, therefore, resulting in a

decrease in firms’ efficiency and performance.

After nationalization of banks in 1969, India did not allow entry of private sector banks until

early 1990s when barriers to entry for private sector banks were removed. India also liberalized

the entry of foreign banks in the post-reform period. These liberalized measures resulted in entry

of many new banks (private and foreign). Accordingly, the number of banks increased during the

initial phase of financial sector reforms. However, the pace of consolidation process gathered

momentum from 1999-2000, leading to a marked decline in the number of private and foreign

banks. The banking sector reforms undertaken in India in the early 1990 were aimed at ensuring

the safety and soundness of financial institutions and at the same time making them efficient,

functionally diverse, and competitive. Reforms also brought about structural changes in the

financial sector by recapitalizing them, allowing profit making banks to access the capital market

and enhancing the competitive element in the market through the entry of new banks. Apart from

achieving greater efficiency by introducing competition through the new private sector banks and

increased operational autonomy to public sector banks, reforms in the banking system were also

aimed at enhancing financial inclusion, funding of economic growth and better customer service

to the public.

Indian financial consolidation has implications not only for competition but also for financial

stability, monetary policy, efficiency of financial institutions, credit flows and payment and

settlement systems. For instance, financial consolidation led to higher concentration in countries

such as US and Japan, though they continue to have much more competitive banking systems as

compared with other countries. However, in several other countries, the process of consolidation

led to decline in banking concentration, reflecting increase in competition. This was mainly

because banks involved in M&As were of relatively small size (RBI, Currency and Finance

report, 2008).

Much of the consolidation activity in France took place during the 1990s among small banks

leading to a large reduction in the total number of banking institutions. Similarly, in Germany

consolidation took place among smaller savings and co-operative banks, thereby leading to

decline in the number of banks by about a third during the 1990s (see currency & finance report

2008 for better reference). Following consolidation, the number of banks in Italy also declined

by more than a third during the same period. A combination of dismantling of restrictions on

inter-state and intra-state banking, removal of interest rate ceilings on small time and savings

deposits and permission on diversification of activities paved the way for mergers between banks

and non-bank financial companies in the US during the 1990s. The consolidation that followed

resulted in substantial growth, in both absolute and relative terms, by the largest institutions. In

the UK, the regulatory reforms during the 1980s and the 1990s removed restrictions on financial

institutions to compete across traditional business lines (RBI, Currency and Finance report,

2008).

In Canada, domestic banks traditionally controlled a large share of the banking sector. Owing to

the dominance of the banking industry by a few banks, consolidation is regulated through a

guideline established in 2000 to ensure that it does not lead to unacceptable level of

concentration and drastic reduction in competition and reduced policy flexibility in addressing

future prudential issues. Thus, not much consolidation took place during the 1990s and the

number of banks did not decline much from the substantial increase observed during the 1980s

due to entry of foreign banks. In Japan also, little consolidation took place during the 1990s and

there was only a modest reduction in the number of banks at the end of the 1990s following some

bank failures. The banking industry in Sweden during the 1990s experienced the merger of co-

operative banks into one commercial bank and transformation of the largest savings banks into

one banking group. Further, there was consolidation among all the major banking groups. While

all the above mergers reduced the number of banks, the total number of banks increased

somewhat due to entry of foreign banks and the establishment of several ‘niche banks’ around

the same time (RBI, Currency and Finance report, 2008).

Given the state of the inconclusive literature on the impact of Concentration, Competition,

Efficiency and their relationship in the banking industry that is discussed above, the current

study aims at examining the impact and degree of concentration, competition, efficiency and

their relationship in the Indian banking industry during the period 1980-2011. To this end, the

present study uses various measures like the structural measures (such as CR3, CR5, CR8 and

HHI on total assets and deposits), non-structural (Panzar – Rosse model on total revenue and

interest revenue as the dependent variables), Data Envelopment Analysis (DEA), and Granger-

causality test.

The present study makes some contributions to banking industry literature. Firstly, to the best of

our knowledge it is only the study that includes pre-liberalization period in the examination,

where as the rest of literature has been focusing on liberalization and post-liberalization periods.

Secondly, it is the first study from the developing world1 that incorporates the efficiency into

competition evaluation and also it is the first study that evaluates the formal relationship between

competition (Panzar - Rosse H-stat) and efficiency2 using Granger-causality test.

Rest of this paper is structured as follows. Section 2 briefs about the Indian banking sector since

1980. Section 3 gives a brief related literature review. Section 4 provides data and methodology,

while Section 4 offers empirical results and discussion. Some conclusions are offered in the final

section.

2. Indian Banking sector at a glance since nationalization

1 2 However there is a study by AP-Podpiera et al (2008) in the literature but this study did not used the standard Granger-causality test and also the competition was measured by Learner index and not by Panzar-Rosse model.

In the pre-independence period and even in the post independence period, failure of banks was a

regular feature. The banking industry at that time was in the hands of private entrepreneurs.

Hence, whenever a bank failed, its customers were simply cheated and their earned money was

forfeited. In order to protect the public interest, nationalization of banks was done in 1969 and

then in 1980. Before nationalization, banks mostly operated in the urban and semi urban areas.

Banking facilities were out of the rural people. With the nationalization of banks, public sector

banks accounted for nearly 90 percent of the banking system of the country. A review of the

performance of banking sector in the early 1990s reveals that despite the overall progress made

by banking system in geographical and functional coverage, its operational efficiency had been

unsatisfactory, characterized by low profitability, high non-performing assets and relatively low

capital base.

Serious inflationary pressures, emerging scarcities of essential commodities and breakdown of

fiscal discipline gave birth to the economic reforms. In 1991, financial sector reforms were

introduced on the basis of Narasimham Committee Report. According to this committee, the

measures recommended were intended to improve the financial health of banks and development

of financial institutions in order to make them more viable and efficient. The recommendations

of the committee regarding the monetary policy issues i.e. reduction of statutory Liquidity Ratio

(SLR) from 38.5 per cent to 25 per cent, reduction in Cash Reserve Ratio (CRR) from a high of

15 per cent and deregulation of the interest rates, were successfully implemented. The reform

process initiated in 1991 has posed many threats and challenges before the bankers as never

before. Bankers who worked with public sector during 1970-90 had their tasks defined for them.

The emergence of new private sector banks and foreign banks with high degree of technology

and automation right from the birth have thrown a real challenge, threat to the continued

profitability of the public sector banks (Uppal, 2005).

In line with the recommendations of the second Narasimham Committee, in October 1999 the

Mid-Term Review of the Monetary and Credit Policy of October 1999 announced a gamut of

measures to strengthen the banking system; The Reserve Bank undertook several measures to

further facilitate the deregulation and flexibility in interest rates. First, the Reserve Bank allowed

banks the freedom to prescribe different prime lending rates (PLRs) for different maturities.

Banks were accorded the freedom to charge interest rates without reference to the PLR in case of

certain specified loans. Banks may also offer fixed rate term-loans in conformity with the ALM

guidelines. Secondly, scheduled commercial banks (excluding regional rural banks), PDs and all-

India financial institutions were allowed to undertake forward rate agreements (FRAs)/interest

rate swaps (IRS) for hedging and market making. Out of the 27 public sector banks (PSBs), 26

PSBs achieved the minimum capital to risk assets ratio (CRAR) of 9 per cent by March 2000. Of

this, 22 PSBs had CRAR exceeding 10 per cent. To enable the PSBs to operate in a more

competitive manner, the Government adopted a policy of providing autonomous status to these

banks, subject to certain benchmarks. As at end-March 1999, 17 PSBs became eligible for

autonomous status (RBI annual Report, 1999).

3. Literature Review

There are many reasons why market conditions in the banking industry deserve particular

attention. According to Bikker (2004), there are at least three reasons forcing us to know the

underlying banking market structure in any country or region. To begin with, competition and

efficiency developments may have powerful effects on the social welfare. Among economic

players, it is especially small and medium sized firms and consumers that are dependent on

banks and have greatest interest in fair market conditions, that is to say a broad supply of bank

services and low prices. High competition and high efficiency may favor that interest. Secondly,

the soundness and stability of the financial sector may, in various ways, be influenced by the

degree of competition and concentration. Thirdly, inefficient banks are an easy takeover prey.

There is well established theoretical literature on the relationships between competition and

banking system performance and stability. The long-existing theory of industrial organization

has shown that the competitiveness of an industry cannot be measured by market structure

indicators alone such as number of institutions, or Herfindahl and other concentration

indexes (Baumol, Panzar, and Willig 1982 and Claessens and Laeven, 2003). Theory also

suggests that performance measures such as the size of the banking margins or profitability

do not necessarily indicate the competitiveness of a banking system. These measures are

influenced by a number of factors; hence we need a comprehensive measure.

One of the problems of all the bank concentration studies is to define the market. Once

we identify the concentration levels in the banking industry, then many questions arise as to what

its consequences are for the bank behavior. The empirical literature on the measurement of

banking concentration and competition is divided into two dimensions: the structural and non-

structural approaches. The traditional industrial organization deals with the structure–conduct-

performance (SCP) paradigm. The SCP paradigm states that increased concentration leads to

collusion and non-competitive practices. It is based on the following assumption that

concentration weakens the competition by fostering the collusive behavior among the banks.

Increased market concentration was found to be associated with higher prices and greater than

normal profits (Bain, 1951). However the existence of the relationship between market structure

and efficiency was first proposed by Hicks (1935) with quite life hypothesis. In which case

monopoly allows mangers relief from competition and therefore increased concentration should

bring about a decrease in efficiency. On the other hand the efficient market hypothesis, posits a

reverse causal relationship between competition and efficiency. According to this hypothesis, if a

bank enjoys a higher degree of efficiency than its rivals, then it can choose the either of the two

opposite strategies to sustain in the market. The first strategy is to maximize the profits by

maintaining the current level of prices and bank size. The second strategy is to maximize the

profits by reducing prices and expand the size, which makes the efficient banks to gain the

market share at the cost of inefficient banks. Hence, concentration does not have to lead to

misuse of the market power as assumed in the SCP paradigm, but may go hand in hand with

lower prices as in the case of higher competition (Bikker, 2004).

In contrast to the structural approach, the non-structural approach, based on the so-called “New

Empirical Industrial Organization literature”, focuses on obtaining estimates of market power

from the observed behavior of banks. The non-structural approach posits that factors other than

market structure and concentration may affect competitive behavior, such as entry or the exit

barriers (Rosse and Panzar, 1977; Baumol et al., 1982; Panzar and Rosse, 1987; Bresnahan,

1989). Among the available non-structural approaches, the methodology proposed by Panzar and

Rosse (1987) is the most widely used method of assessing competition in the banking industry.

This paper will employ the method proposed by Panzar and Rosse (1987) for computation of H

statistic analysis by following the work of Casu and Girardone (2006).

While excessive competition may create an unstable banking environment, insufficient

competition – and contestability – in the banking sector may breed inefficiencies. For these

reasons, policymakers are concerned about commercial bank concentration. The Panzar and

Rosse methodology provides a framework to study the market conditions and has been

extensively applied throughout the world. The below table gives summary of findings of the

studies that are used the P-R methodology to evaluate the competition conditions throughout the

globe.

Table 1: Banking industry competition by Panzar - Rosse method in other studies

Authors Period Studied Countries Results

Nathan and Neave (1989) 1982–1984 Canada In 1982 PC. In 1983, 1984 MC

Shaffer and Disalvo (1994) 1970–1986 Pennsylvania (USA) Duopoly

Molyneux et al. (1994) 1986–1989 Germany, the UK, Italy, France and Spain

MC (Italy 1987, 1989 M)

Niimi (1998) 1989-1996 Japan In 1989-91 M, in 1994–96 MC

Bikker and Groeneveld (2000)

1989–1996 15 countries in Europe MC

De Bandt and Davis (2000) 1992–1996 Four countries Large banks: MC in all countries. Small banks: M (MC in Italy)

Gelos and Roldos (2002) 1994–2000 Europe & Latin America MC

Murjan and Ruza (2002)! 1993–1997 Arab Middle East MC

Bikker and Haaf (2002)! 1988–1998 23 countries MC

Claessens and Laeven (2004)! 1994–2001 50 countries Brazil, Greece, Mexico are highly competitive compare to rest.

Mamatzakis et al (2005)! 1998–2002 South Eastern Europe MC

Günalp and Çelik (2006)! 1990 -2000 Turkey MC

Al-Muharrami et al (2006)! 1993–2002 Arab GCC countries Kuwait, Saudi Arabia & the UAE (PC); Bahrain and Qatar and Oman (MC).

Yuan (2006)! 1996–2000 China PC before financial market reforms.

Kang H. Park (2009)! 1992–2004 Korea MC

Goddard and Wilson (2009)! 1998–2004 Canada,France,Germany, Italy,Japan, UK and US

Italy (PC), Germany and France (MC) and Japan, UK and US (M).

Shin and Kim (2010)! 1992-2007 Korea MC

Rima Turk Ariss (2010)! 2000–2006 13 Islamic countries Islamic banking is less competitive.

Olivero et al. (2011)! 1996-2006 Asia & Latin America MC

.

4. Data and Methodology

The dataset used in the analysis covers all the Indian commercial banks in the period 1980–2011

except for the year 1988, since there was no data compiled for that year. The main source of data

is the Reserve Bank of India (RBI). Balance sheets and profit and loss statements are available

for all banks in a consistent format over the period 1980 through 2011. The banks are classified

into public banks (State Banks of India and its associate banks and nationalized banks), foreign

banks, and Indian private banks that are further split up into “old private” and “new private”

banks.

Our empirical study is conducted in four steps. In the first step we estimate the degree of banking

concentration using CR3, CR5, CR8 and HHI index. In the second step we investigate banking

efficiency using data envelopment analysis (DEA). In the third step we estimate H-statistic based

on the Panzar and Rosse (1987) model by incorporating with and without the efficiency scores as

the bank specific factors. In the final step we analyze the causal direction between competition

and efficiency.

3.1. Measures of Concentration Ratios

The degree of concentration is measured in various ways. Literature generally uses the k-firm

(bank) concentration ratio and Herfindahl-Hirschman Index (HHI) as an exogenous indicator of

market power or an inverse indicator of the intensity of competition. The market shares of all

sizes and types of commercial banks were generally treated equally in computing the

concentration measure (Berger, A.N. and Demirguc-Kunt, A. and Levine, R. and Haubrich, J.G.,

2004). We used C3, C5 and C8 ratios which show the concentration ratios of the biggest 3 and 5

and 8 banks respectively according to the share of their assets in the total assets and deposits of

the banking sector. We also calculated Herfindahl-Hirschman Index (HHI) which is calculated

, where is the bank i’s share and adding up the squares of the market shares of all

banks. This index lies between zero and one (Mkrtchyan, 2005).

a. Efficiency Estimation using Data Envelopment Analysis

The literature on banking provides four major approaches in the efficiency analysis. These

methods differ primarily on basis of their assumptions that they impose on data in terms of - (i)

functional form of the efficient frontier (less restrictive versus more restrictive); (ii)

consideration of the random error; and (iii) if random error is allowed, the probability

distribution assumed for the inefficiencies (e.g. half-normal, truncated) to separate one from

another (Thanassoulis, 2001). The four major approaches are grouped under two broad

approaches on the basis of above three considerations. They are non-parametric linear

programming frontier approach, also known as Data Envelopment Analysis (DEA), and

parametric approach. Under the lattr category, there are three approaches, viz. Stochastic Frontier

Approach (SFA), Thick Frontier Approach (TFA), and Distribution Free Approach (DFA).

In this study we follow the nonparametric method of efficiency measurement using Data

Envelopment Analysis (DEA). DEA is most widely used programming technique that provides a

linear piecewise frontier by enveloping the observed data points and yields a convex production

possibilities set. As such, it does not require the explicit specification of the functional form of

the underlying production relationship. The efficiency frontier of a firm in the context of

production function can be defined as the maximum feasible level of outputs given the input

levels, or alternatively as the minimum feasible level of inputs given the output levels (Casu and

Girardone, 2006).

3.2.1 Specification of Data Envelopment Analysis (DEA):

Charnes, Cooper and Rhodes (CCR, 1978 and 1981) developed the Data Envelopment Analysis

for the efficiency measurement of the decision making units (DMUs) with multiple inputs and

multiple outputs in the absence of the market prices, under the constant returns to scale (Ray

2004). Later Banker, Charnes and Cooper (BCC) developed a method in which variable returns

to scales are allowed. Formally, DEA is a methodology directed to frontiers rather than central

tendencies. Instead of trying to fit a regression plane through the center of the data as in

statistical regression, for example, one ‘floats’ a piecewise linear surface to rest on top of the

observations. Because of this perspective, DEA proves particularly adept at uncovering

relationships that remain hidden from other methodologies. The reason to assess relative rather

than the absolute efficiencies is that in most practical contexts we do not have sufficient

information to derive the superior measures of absolute efficiency (Coelli et al, 1998).

In this study we follow variable returns to scale (BCC) model developed by Banker et al (1984).

The DEA input oriented models are chosen in the present study since cost minimization is

considered (Luo, 2003, Golany and Roll, 1989). If N firms use a vector of inputs to produce a

vector of outputs, the input oriented CCR measure of efficiency of a particular firm is calculated

as

DEA VRS (BCC) Input-Oriented Model

Subject to

(1)

Where is the scalar efficiency score for the ith unit. If the ith firm is efficient as it lies

on the frontier, where as if the firm is inefficient and it needs a reduction in the input

levels to reach the frontier. The additional convexity constraint can be included in the

above equation (1) to allow for variable returns to scale (see Banker, Charnes and Cooper, 1978,

Casu and Girardone, 2006). The approach to output definition used in this study is a variation of

the intermediation approach, which was originally developed by Sealey and Lindley (1977) and

total loans and other income as outputs whereas deposits and operating expenses are inputs.

Specifically, the input variable used in this study is the total costs (operating expenditure +

interest expenditure) whereas the output variables capture both the traditional lending activity of

banks (total loans) and other earnings (other income).

b. Measure of Competition

To assess the degree of competition in the Indian banking sector, we have employed the Panzar

and Rosse (1987) test by following Casu and Girardone (2006) on the market structure. There is

a very large and well established literature on the relationship between market concentration,

prices and market power. According to the Structure–Conduct Performance, there is a positive

relationship between market concentration, which is treated as exogenous, and prices. That

means highly concentrated markets would favor some (implicit or explicit) form of collusion

among banks, which in turn enables them to exploit their market power through huge interest

margins thereby earning above normal profits. But, the Structure efficiency envisages that there

exists a negative relationship between the market concentration, which is endogenous, and

prices. This means that the most efficient banks can offer the intermediation services at lower

cost and expand their market share. On the other hand, the theory of contestable markets

excludes any relationship between the number of operators in a particular market and prices.

That means the simple new entry (contestability) is sufficient to induce the market operators to

set prices at a level which makes the entry of new operators in the market unprofitable (A.

Giustiniani and K Ross, 2008).

Rosse and Panzar (1977) and Panzar and Rosse (1987) formulated models and developed a test

to discriminate among the models of oligopolistic, competitive and monopolistic markets. This

test is based on properties of a reduced-form revenue equation at the bank level and uses a test

statistic H, which under certain assumptions, can serve as a measure of the competitive behavior

of banks (Bikker J.A, 2004). It measures the sum of the elasticities of total revenue of the bank

with respect to the bank’s input prices that can be written as follows3:

(2)

Where Ri refers to revenue of bank i (* indicates equilibrium values) and w i is a vector of m

factor input prices of banks i. Market power is measured by the extent to which a change in

factor input prices is reflected in the equilibrium revenues earned by bank i.

A value of one for H indicates perfect competition, while negative values indicate collusive

oligopoly or a monopoly. In case of monopolistic competition H can take values between zero

and one (0<H<1). In this study, following Bikker and Haff (2002), Mkrtchyan (2005) and Casu

and Girardone (2006) we interpret H as continuous measure of the level of competition, which

lies in between 0 and 1, in the sense that the higher values of H indicates stronger competition

than lower values.

Values of H Competitive environment

3

H≤0 Monopoly equilibrium: each bank operates independently as under monopoly profit

maximization conditions (H is a decreasing function of the perceived demand

elasticity) or perfect cartel

0<H<1 Monopolistic competition free entry equilibrium (H is an increasing function of the

perceived demand elasticity)

H=1 Perfect competition. Free entry equilibrium with full efficient capacity utilization

Source: Bikker (2004).

In the empirical analysis, following Casu and Girardone (2006) the following reduced form

equation is estimated in order to derive the Panzar – Rosse H statistic:

(3)

with t=1…T, where T is the number of periods observed, and i=1… I, where I is the total number

of banks. Subscripts i and t refer to bank i at the time t. The H statistic is the sum of the input

coefficients β1 to β3,

(4)

Where j=1,…,J and J is the number of inputs included in the calculations. The estimated value of

the H- statistic is indicative of a particular market structure with the two extreme cases of perfect

competition and monopoly identified by H=1 and H=0 respectively and an intermediary case

exists if the value H lies between 0 and 1 (0<H<1). According to Vesala (1995) in a perfectly

competitive market, an increase in factor prices would raise both marginal and average costs

without affecting the optimal level of output of any individual firm. As a result, banks should

experience an equivalent increase in revenues and the H statistic should become 1. Whereas if

the market is monopoly market, an increase in the factor prices should raise the marginal cost,

and reduce the equilibrium output and hence revenues, and in this case the H statistic should be

either zero or negative. On the other hand, if the market is monopolistic competition, under the

assumption of free entry and hence of zero profit equilibrium, the H-statistic takes a positive

value lower than 1 (Giustiniani and Ross, 2008).

In estimating equation (3), following Vesala (1995) and (Giustiniani and Ross, 2008) we have

used two definitions of banks’ revenues as the dependent variable. The first one refers to banks

gross total revenue (TR); the reason being that banks have started competing by offering a host

of services to their customers. The second one is banks’ gross interest income (IR), which is

consistent with the idea that the core activity of banking sector is to produce loans and

investments. The dependent variable is divided by total assets in order to account for size

difference.

In line with the intermediation approach and existing literature, we assume that banks use three

factors, labor, deposits and capital: lnINTE is the average cost of deposits and borrowings

(interest expenses/deposits + borrowings), lnPPE is the average cost of labor (personnel

expenses/total assets); and lnPCE is the average cost of capital expenditure (other expenditure

/total fixed assets). The bank specific control variables include lnETA, i.e. the ratio of total

equity to total assets; lnTA, i.e. total assets; lnLOTA, i.e. total loans to total assets; and lnDEA,

i.e. efficiency score of the banks. All variables are in the logarithmic form. The bank specific

variables are included to control for differences in risks, costs, size and structures of banks. The

risk component can be proxied by the ratio equity to total assets (lnETA), the ratio of total loans

to total assets (lnLOTA). The total assets control for the size of the bank and can be considered a

proxy for the scale economies. According to Molyneux et al. (1994) and Casu and Girardone

(2006) the coefficient can be expected to be negatively related to the total revenue/ gross interest

income dependent variable since lower capital ratios should lead to higher bank revenues.

However there is another interpretation that exits in the literature. A high capital ratio may also

suggest high risk portfolio, thus suggesting a positive coefficient (Coccorese, 1998; Bikker and

Groeneveld, 2000, Casu and Girardone, 2006). The coefficient of the ratio of total loans to total

assets is expected to be positively related to dependent variable, as higher proportions of loans on

the banks books are expected to generate more revenue (Bikker, 2004; Casu and Girardone,

2006).

c. Incorporating Banking Efficiency

As per our knowledge, there is a limited empirical literature on the relationship between

competition and efficiency. Given the sparse empirical research on relationship between

efficiency and competition, we follow Casu and Girardone, (2006) work to explore the

relationship between the competition and efficiency. We explore this relationship by including

non-parametric DEA efficiency scores as the bank specific factor in the reduced-form revenue

equation. We have no expectations on the sign of the coefficient on the DEA efficiency variable.

On the other hand, higher cost efficiency may be an indicator of higher revenues as the most

cost-efficient banks are the best managed banks and therefore enjoy the largest market shares. In

order to account for bank efficiency, we include such estimates in the revenue function that is

used to calculate the Panzar-Rosse H statistic. We also calculate the Panzar-Rosse H statistic by

replacing the dependent variable total revenue over total assets with natural log of interest

revenue over total assets.

(5)

d. Granger causality test between Competition and Efficiency

There is no well established direct relationship between competition and efficiency - neither

from the empirical literature nor from the theoretical literature (AP-Podpiera et al, 2008). We

employ this model further to substantiate the relationship between competition (estimates of both

Panzar–Rosse models) and efficiency (estimates of annual average DEA efficiency scores) on

the full sample. The Granger causality test assumes that the information relevant to the

prediction of the respective variables, Competition (H-statistic) and DEA efficiency score is

contained solely in the time series data on these variables (Gujarati, 2003). The test involves

estimating the following pair of regressions:

(6)

(7)

Where it is assumed that the disturbances u1t and u2t are uncorrelated. Equation (6) postulates that

current competition is related to the past values of itself as well as that of DEA efficiency scores

and equation (7) postulates that current DEA efficiency score is related to past values of itself as

well as that of competition. There are four cases possible for causal direction:

1. Unidirectional causality from H-stat to DEA is indicated if the estimated coefficients on

the lagged DEA in (6) are statistically different from zero as a group (i.e., and

the set of estimated coefficients on the lagged H-stat in (7) is not statistically differently

from zero (i.e. .

2. Conversely, unidirectional causality from DEA to H-stat exists if the set of lagged DEA

coefficients (6) is not statistically different from zero (i.e., and the set of the

lagged H coefficients in (7) is statistically different from zero (i.e. .

3. Feedback or bilateral causality, is suggested when the sets of the H-stat and DEA

coefficients are statistically different from zero in both regressions.

4. Finally, independence is suggested when the sets of the H-stat and DEA coefficients are

not statistically significant in both the regressions.

4.0. Empirical Results and Discussion

The study period is divided into three distinctive periods: the nationalization of the banks (pre-

reform period) (1980-89); liberalization period (1990-98); and post-liberalization period (1999-

2011). Each period has its own uniqueness in financial intermediation process. Pre-reforms

period is characterized by deepening the financial intermediation process through the expansion

of the banking network throughout the country by government ownership; liberalization period is

characterized by deregulation of Indian economy (i.e. opening the gates for privatization and

foreign ownership). In this period many foreign and private banks participated in the financial

intermediation process. And the post-liberalization period (1999–2011) is characterized by bank

consolidation and decrease in market concentration.

Table 2: Various concentration, competition and efficiency results from 1980 to 1998.

YEAR

No .of

Banks

TACR3

TACR5

TACR8

DCR3

DCR5

DCR8

THHI

DHHI DEA

1PR1

1PR2

2PR1 2PR2

1980 28 0.428 0.555 0.687 0.387 0.522 0.667 0.112 0.0910.90

30.73

60.69

60.71

3 0.624

1981 28 0.439 0.564 0.696 0.398 0.532 0.677 0.119 0.0960.90

70.68

10.71

20.57

7 0.635

1982 28 0.453 0.579 0.706 0.398 0.532 0.674 0.130 0.0970.89

60.46

30.63

10.32

4 0.629

1983 28 0.442 0.570 0.701 0.409 0.540 0.677 0.121 0.1000.91

50.54

50.52

00.49

7 0.452

1984 28 0.431 0.553 0.691 0.398 0.525 0.667 0.116 0.0960.92

40.49

60.52

10.46

6 0.519

1985 28 0.436 0.559 0.717 0.400 0.529 0.670 0.117 0.0980.93

80.62

60.60

40.60

9 0.568

1986 28 0.430 0.556 0.689 0.387 0.516 0.663 0.112 0.0910.91

40.60

90.55

60.62

9 0.556

1987 27 0.438 0.567 0.698 0.383 0.515 0.664 0.116 0.0880.88

00.75

80.61

40.89

4 0.652

1988-89 27 0.466 0.585 0.710 0.389 0.519 0.668 0.134 0.091

0.880

0.834

0.692

0.999 0.784

1990 73 0.412 0.526 0.643 0.359 0.479 0.611 0.105 0.0770.73

20.90

20.89

70.91

8 0.903

1991 76 0.405 0.515 0.632 0.355 0.473 0.601 0.102 0.0750.72

80.76

10.78

10.75

1 0.728

1992 77 0.409 0.514 0.623 0.371 0.486 0.605 0.101 0.0790.66

10.25

60.29

30.40

4 0.416

1993 76 0.383 0.491 0.602 0.353 0.468 0.585 0.091 0.075 0.67 0.19 0.25 0.29 0.314

0 7 1 2

1994 74 0.377 0.488 0.591 0.348 0.466 0.579 0.089 0.0740.72

70.28

40.35

80.38

0 0.396

1995 86 0.356 0.447 0.567 0.335 0.451 0.564 0.079 0.0690.73

90.70

20.77

20.72

2 0.776

1996 92 0.354 0.458 0.562 0.333 0.449 0.564 0.079 0.0690.68

50.58

10.59

20.58

8 0.579

1997 100 0.345 0.450 0.549 0.325 0.441 0.551 0.075 0.0660.68

90.38

20.43

30.40

9 0.366

1998 103 0.342 0.446 0.544 0.325 0.439 0.546 0.072 0.0650.70

90.16

70.17

30.23

8 0.206

Variable description: TACR3, TACR5, and TACR8 are the concentration ratios based on total assets. DCR3, DCR5, and DCR8 are the concentration ratios based on deposits. THHI and DHHI are the Herfindahl-Hirschman Indices based on total assets and deposits respectively.DEA is the average annual efficiency score. 1PR1 &2PR1are the H-stats calculated total revenue and interest revenue as the dependent variable. 2PR1 &2PR2are the H-stats calculated total revenue and interest revenue as the dependent variable and DEA included as the bank specific factor.

Table 3: Various concentration, competition and efficiency results from 1998 to 2011.

YEAR

No. of

Banks

TA4CR3

TACR5

TACR8

DCR3

DCR5

DCR8

THHI

DHHI DEA

1PR1

1PR2

2PR1 2PR2

1999 104 0.346 0.445 0.541 0.335 0.442 0.547 0.075 0.0700.68

30.20

30.24

90.14

8 0.135

2000 101 0.339 0.437 0.530 0.329 0.435 0.536 0.074 0.0690.66

90.35

50.44

40.49

0 0.454

2001 100 0.344 0.439 0.529 0.339 0.439 0.538 0.077 0.0730.68

40.15

60.28

60.26

8 0.296

2002 97 0.342 0.435 0.544 0.332 0.433 0.532 0.072 0.0710.67

10.31

40.32

90.40

2 0.390

2003 92 0.335 0.429 0.538 0.328 0.424 0.530 0.070 0.0690.64

40.30

60.36

90.33

7 0.279

4 Variable description is same as in the table 1.

2004 90 0.322 0.415 0.520 0.313 0.404 0.515 0.064 0.0620.67

60.54

10.59

90.61

0 0.585

2005 87 0.320 0.407 0.513 0.310 0.407 0.517 0.061 0.0620.50

10.80

20.79

10.64

0 0.642

2006 85 0.320 0.408 0.512 0.307 0.404 0.513 0.057 0.0560.57

30.62

50.63

40.43

6 0.422

2007 82 0.311 0.399 0.500 0.300 0.398 0.505 0.054 0.0530.63

00.52

60.56

50.29

8 0.281

2008 78 0.305 0.388 0.491 0.286 0.378 0.488 0.054 0.0510.67

90.54

20.53

80.48

6 0.427

2009 77 0.304 0.390 0.500 0.288 0.382 0.497 0.057 0.0570.71

90.63

00.62

70.28

3 0.313

2010 79 0.284 0.376 0.496 0.273 0.371 0.484 0.054 0.0530.74

70.58

50.51

60.35

5 0.356

2011 79 0.280 0.378 0.499 0.276 0.382 0.495 0.054 0.0530.76

70.81

30.87

30.42

1 0.421

Figure 1 and 2 are Competition and Concentration

0.2

.4.6

.8

1980 1990 2000 2010YEAR

TACR3 TACR5TACR8 DCR3DCR5 DCR8THHI DHHI

Tables 2 & 3 (from column 2 to 8) and Figure-2 show the concentration indices progress trends.

Concentration indices based on total assets (TACR3, TACR5, TACR8 and THHI) show a little

volatile trend from 1980 to 1988-89 and reached the highest level in 1988-89. From 1990

onwards they show a decreasing trend with little volatility. But it is not the same case with the

deposit concentration (DCR3, DCR5, DCR8 and DHHI) indices. They reach the highest level by

.2.4

.6.8

1

1980 1990 2000 2010YEAR

DEA TRPR1TRPR2 IRPR1IRPR2

1983, and from then onwards they show a decreasing trend with a little volatility in the trend. It

is commonly accepted that Herfindahl indices below 0.100 indicate non-concentrated, between

0.100 and 0.180 moderately concentrated and indices above 0.180 imply concentrated markets.

Based on THHI index, Indian banking sector was moderately concentrated till 1992 and from

1993 it is non-concentrated. While DHHI shows that Indian banking sector can be characterized

as non-concentrated in entire the study period.

The average annual DEA efficiency (BCC model, variable returns to scale) scores are presented

in the tables 2 and 3 (column 9). The average annual efficiency score for the Indian banking

sector over the full sample period is 74.60 percent indicating a 25.40 percent average potential

reduction in the input utilization. The sub-sample averages are as follows: 88.90 for the period

(pre reform) 1980-1988-89 , 69.90 percent for the period 1990-1998 (during liberalization), and

66.30 percent for the period 1999-2011. . So it is evident from the efficiency scores that Indian

banking sector is not efficient either in the pre reform period or during and in the post reform

periods. However Indian banks were better off in the pre-reform period compared to during and

post reform period in terms of efficiency levels.

Tests of competitive conditions (H-stats) are given in Tables 2 and 3 (column 11 to 13). The

estimation results of the P–R model with of lnTR (Total Revenue/Total Assets) as dependent

variable is presented in column 11 (1PR1) without including the annual average DEA efficiency

score as the bank specific factor and in column 12 (1PR2) by including annual average DEA

efficiency score as the bank specific factor. Results with lnIR (Interest Revenue/Total Assets) as

the dependent variable are presented in column 13 (2PR1) without including the annual average

DEA efficiency score as the bank specific factor and in column 13 (2PR2) by including annual

average DEA efficiency score as the bank specific factor in tables 2 and 3, respectively.

It is evident from tables 2 and 3 that both the models show that in the pre-reform period (i.e.

1980-1988-89), competitiveness was high initially, but later the competition decreased.

However, it got momentum by 1985 and reached the highest competitive level by the year 1988-

89 and in the same year (i.e. 1988-89) the results with model interest revenue as dependent

variable without the annual DEA efficiency score as the bank specific factor show perfect

competition in the Indian banking sector. During the liberalization period there was huge

volatility in the competition levels. In the Initial years of the reform period Indian banking sector

shown high competitiveness but it falls down by 1992. However it got the momentum by 1995,

but again it shows a decline trend from 1996 onwards in the competitive levels. Again the post

reform period also exhibits huge volatility in the banking competition levels. In the initial years it

had shown less competitive conditions but it picked up the momentum in the competition levels

by 2004. However banks were showing relatively more competitiveness in the second half of the

post reform period compared to first half of the post reform period. Finally the year 2011 shows

highest competition in the post reform period.

The fixed effects model is used to control for bank-specific characteristics and heterogeneity

among banks. The fixed effects model is usually regarded as more appropriate than random

effects model (Park, 2009). Hence we used the fixed effects model on the three sub sample

periods and the complete sample period.

In Table 4 with lnTR as the dependent variable, the H value, without inclusion of DEA in the

model, decreased significantly from 0.856 for the period 1980–1988-89 to 0.483 for the period

1990-1998, however it increased to 0.517 for the period 1999–2011. For full sample, the H value

is 0.474, which indicates monopolistic competition in Indian banking industry in all sample

periods, except for the period of 1980-1988-89, which was highly competitive (i.e. near to

perfect competition). The Wald test rejects the hypothesis of monopoly market structure (H= 0)

at the 1% level. The Wald test also rejects the hypothesis of perfectly competitive market

structure (H= 1) at the 1% level for all periods.

Table 4: lnTR as the dependent variable

1980-88 1990-98 1999-2011 1980-2011

lnINTE 0.569*** 0.564*** 0.578*** 0.578*** 0.356*** 0.324*** 0.441*** 0.434***

(26.60) (29.44) (31.80) (31.80) (27.35) (25.96) (45.62) (45.08)

lnPPE 0.347*** 0.291*** 0.147*** 0.145*** 0.153*** 0.179*** 0.131*** 0.144***

(19.74) (16.72) (7.39) (7.19) (9.31) (11.58) (12.40) (13.60)

lnPCE -0.003 -0.002 0.041*** 0.041*** 0.021** 0.028*** -0.011** -0.014***

(-0.75) (-0.45) (4.79) (4.78) (2.47) (3.55) (-2.55) (-3.19)

lnETA 0.020*** 0.019*** 0.093*** 0.092*** -0.021 -0.036** 0.057*** 0.060***

(4.41) (4.66) (11.32) (11.10) (-1.38) (-2.50) (9.33) (9.93)

lnLOTA 0.137*** 0.248*** 0.191*** 0.198*** 0.024** 0.024** 0.105*** 0.099***

(4.63) (8.14) (7.37) (7.18) (2.28) (2.48) (11.67) (11.00)

lnTA 0.025*** 0.034*** 0.029** 0.029** -0.049*** -0.057*** -0.001 0.003

(4.25) (6.26) (2.20) (2.22) (-5.76) (-7.26) (-0.16) (0.79)

lnDEA -0.226*** -0.017 0.177*** 0.087***

(7.36) (-0.70) (11.91) (6.63)

H-stat 0.856*** 0.847*** 0.483*** 0.487*** 0.517*** 0.520*** 0.474*** 0.474***

(28.52) (34.35) (22.01) (21.94) (27.96) (31.13) (34.77) (35.20)

Wald:H=0 813.5*** 1179.9*** 484.7*** 481.2*** 781.7*** 969.2*** 1208.7*** 1238.7***

Wald:H=1 22.9*** 38.4*** 555.6*** 533.0*** 680.5*** 827.1*** 1487.8*** 1523.2***

F 27.82 21.99 15.22 15.17 8.29 7.29 13.31 13.25

Adj R2 77.29% 76.70% 41.34% 41.33% 44.51% 54.38% 40.60% 40.80%

N 250 250 730 730 1092 1092 2072 2072

*, **, *** denote an estimate significant at 10%, 5%, and 1% level. Values in the parenthesis are t-values.

Table 5: lnIR as the dependent variable

1980-88 1990-98 1999-2011 1980-2011

lnINTE 0.539*** 0.534*** 0.572*** 0.573*** 0.412*** 0.403*** 0.464*** 0.465***

(22.34) (24.63) (28.00) (28.34) (34.56) (33.13) (47.07) (46.82)

lnPPE 0.389*** 0.327*** 0.113*** 0.100*** 0.124*** 0.132*** 0.087*** 0.086***

(19.65) (16.61) (5.04) (4.47) (8.28) (8.75) (8.11) (7.85)

lnPCE -0.004 -0.003 0.027*** 0.026*** -0.005 -0.003 -0.023*** -0.023***

(-0.95) (-0.67) (2.77) (2.75) (-0.71) (-0.44) (-5.28) (-5.21)

lnETA 0.014*** 0.013*** 0.088*** 0.083*** -0.083*** -0.088*** 0.037*** 0.037***

(2.76) (2.81) (9.51) (8.97) (-5.86) (-6.19) (5.90) (5.84)

lnLOTA 0.230*** 0.354*** 0.240*** 0.278*** 0.103*** 0.103*** 0.183*** 0.183***

(6.89) (10.27) (8.21) (9.09) (10.72) (10.80) (19.88) (19.81)

lnTA 0.048*** 0.057*** -0.003 -0.001 -0.038*** -0.040*** -0.019*** -0.019***

(7.25) (9.49) (-0.22) (-0.10) (-4.88) (-5.22) (-4.50) (-4.53)

lnDEA -0.253*** -0.100*** 0.051*** -0.007

(-7.28) (-3.78) (3.55) (-0.50)

H-stat 0.842*** 0.826*** 0.487*** 0.475*** 0.469*** 0.469*** 0.478*** 0.478***

(19.13) (26.50) (24.01) (23.32) (28.24) (28.25) (39.20) (39.28)

Wald:H=0 366.0*** 702.5*** 576.4*** 544.0*** 797.2*** 798.0*** 1536.9*** 1542.5***

Wald:H=1 12.8*** 31.4*** 641.5*** 665.9*** 1022.9*** 1022.4*** 1833.5*** 1841.0***

F 52.53 29.43 8.35 8.4 7.69 7.85 7.12 7.02

Adj R2 61.35% 65.16% 54.56% 55.84% 58.20% 58.34% 58.57% 58.63%

N 250 250 730 730 1092 1092 2072 2072

*, **, *** denote an estimate significant at 10%, 5%, and 1% level. Values in the parenthesis are t-values.

The H values, with inclusion of annual average DEA efficiency scores in the model, show

similar results. But, a different pattern is found in Table 5 with lnIR as the dependent variable.

The H value, without inclusion of DEA in the model, decreased significantly from 0.842 for the

period 1980–1988-89 to 0.487 for the period 1990-1998, and again it decreased to 0.469 for the

period 1999–2011. For the full sample, the H value is 0.478 which is higher compared to the

value with lnTR as the dependent variable and indicates monopolistic competition in Indian

banking industry in all sample periods, except for the period of 1980-1988-89, which was highly

competitive (i.e. near to perfect competition). The Wald test rejects the hypothesis of monopoly

market structure (H= 0) at the 1% level. The Wald test also rejects the hypothesis of perfectly

competitive market structure (H= 1) at the 1% level for all periods.

The H value, with exclusion of annual average DEA efficiency scores in the model, decreased

significantly from 0.826 for the period of 1980–1988-89 to 0.475 for the period of 1990-1998,

but dropped to 0.619 for the period of 2001–2004. The Wald tests render the same conclusion

about the market structure of the Indian commercial banking market. The H values, with

exclusion and inclusion of annual average DEA efficiency scores in the model as the bank

specific factor, show mixed results. The estimation results of the H values with two different

dependent variables, lnTR and lnIR (without and with inclusion of annual average DEA

efficiency scores as the bank specific factor in the model), are robust as shown by Tables 4 and

5. The empirical results lead us to infer that the Indian commercial banking market was highly

competitive during the pre-reforms period (1980–1988-89) and the competition decreased

significantly during the reforms period (1990-1998) and the post- reforms period (1999–2011).

In the search for a relationship between competition and concentration, correlation tests are run

as alternatives to linear regression. The correlation coefficient between two series (i.e. in both

models with four cases of H (PR11, PR12, PR21 and PR22)) and various measures of

concentration like CR3, CR5, CR8 and HHI for both variable (total assets and deposits) is

calculated. There is no association between concentration and competition (measured total

revenue as the dependent variable, PR11, PR12). However there is a positive and significant

relationship between concentration and competition, when the competition measured interest

revenue as the dependent variable. Table 7, which is presented in the appendix, gives the

correlation coefficients between concentration and competition for the full sample. The

significant result from this analysis is that the choice of the variable is very much important.

The relationship between competition and efficiency is explored using the Granger causality test

on the annual H-statistic and on the annual average DEA efficiency scores for the full sample.

Table 7 gives the granger causality test results based on the estimations of PR11, PR21 and

efficiency scores in two cases. In each case, competition and efficiency becomes dependent and

independent variables alternatively. Only H-stat of the models of PR11, PR21 are considered in

the analysis, since the other two models of competition (PR12, PR22) already include efficiency

as the bank specific factor.

The results show in the first case (where the competition is measured using total revenue as the

dependent variable without including DEA as the bank specific factor in the model) that the

efficiency Granger-causes the competition and competition does not Granger-cause the

efficiency. In the equation explaining competition, the coefficients of the lags of the efficiency

index are jointly different from zero and significant at first lag with F-value being 4.63 (0.046) at

5% level. In the equation explaining the efficiency, the lags of H-stat are not jointly different

from zero, not even at 10% significance level in any of the four lags.

An opposite pattern is found in the second case (where the competition is measured using

interest revenue as the dependent variable without including DEA as the bank specific factor in

the model) where competition Granger-causes the efficiency and efficiency does not Granger-

cause the competition. In the equation explaining efficiency, the coefficients of the lags of the

competition index are jointly different from zero and significant at first lag with F-value being

3.99(0.062) at 10% level. In the equation explaining the competition, the lags of efficiency are

not jointly different from zero, not even at 10% significance level in any of the four lags.

Table 6: Granger-causality test results

Case15: Dependent variable Case26: Dependent variable

H-stat DEA H-stat DEA

Constant -1.531*(-2.06) 0.308(1.13) -0.912(-1.16) 0.180(0.66)

5 H-stat is based on P-R model total revenue as the dependent variable without DEA as the bank specific factor.6 H-stat is based on P-R model interest revenue as the dependent variable without DEA as the bank specific factor.

DEAt-1 1.307**(2.15) 0.660***(2.98) 0.750(1.11) 0.654**(2.81)

DEAt-2 -0.068(-0.08) 0.376(1.29) 0.015(0.02) 0.395(1.37)

DEAt-3 -0.853(-1.06) -0.017(-0.06) -0.748(-0.87) 0.105(0.35)

DEAt-4 1.466(1.89) -0.429(-1.52) 1.462*(1.82) -0.331(-1.19)

H-statt-1 0.801***(3.71) -0.088(-1.12) 0.588**(2.66) -0.153*(-2.00)

H-statt-2 -0.140(-0.53) 0.090(0.94) -0.008(-0.03) 0.031(0.36)

H-statt-3 -0.442(-1.71) 0.021(0.22) -0.422(-1.69) 0.028(0.33)

H-statt-4 0.301(1.51) 0.058(0.80) 0.118(0.54) 0.033(0.43)

F-stat up to lag1 4.63**(0.046) 1.26(0.278) 1.24(0.281) 3.99*(0.062)

F-stat up to lags2 3.45*(0.055) 0.70(0.512) 0.91( 0.422) 2.15(0.147)

F-stat up to lags3 2.31( 0.113) 0.60(0.626) 0.63(0.603) 1.43( 0.268)

F-stat up to lags4 2.66 *(0.069) 1.18(0.356) 1.55 (0.232) 1.37(0.286)

R2 70.57% 81.67% 66.02% 82.29%

N 27 27 27 27

*, **, *** denote an estimate significant at 10%, 5% and 1% level. The values in the parenthesis of the coefficients represent the t-values and of the F-stats represent p-values.

5.0. Conclusions

Competition is generally considered as a positive force, often associated with increased

efficiency and enhanced consumers’ welfare. However, competition in the banking sector is a

more controversial issue (Bikker, 2004). The acceleration in the financial consolidation since

nationalization of the Indian banking sector has been raising many concerns about the level of

concentration, competition and efficiency. Using bank level balance sheet data of Indian

commercial banking sector, this paper aims at analyzing the state of the concentration,

competition, efficiency and the relationship among them since nationalization (1980) of the

Indian banking sector. Various standard measures like concentration ratios, Herfindhal index,

Data Envelopment Analysis (DEA), Panzar–Rosse model and Granger-Causality test are used to

analyze state and the relationship among concentration, competition and efficiency from 1980 to

2011.

An analysis of the structural concentration measures (CR3, CR5, CR8 and HHI) on total assets

and deposits indicate that Indian banking sector was highly concentrated during pre-reform

period (1980-89). However it started decreasing since the liberalization of the banking sector and

same decreasing pattern continued in the post liberalization period (liberalization period, 1990-

1998 and post-liberalization period 1999-2011). The average annual DEA efficiency (BCC

model, variable returns to scale) scores for the Indian banking sector over the full sample period

is 74.60 percent indicating a 25.40 percent average potential reduction in the input utilization.

The sub-sample averages as follows: 88.90 percent for the period (pre reform) 1980-1988-89,

69.90 percent for the period (during liberalization) 1990-1998, and 66.30 percent for the period

1999-2011. So, it is evident from the efficiency scores that Indian banking sector is not efficient

either in the pre-reform period or during and in the post reform periods. However Indian banks

were better off in the pre-reform period compared to during and post reform period in terms of

efficiency levels.

Analysis of the non-structural Panzar–Rosse H-statistic indicates that in the pre-reform period

there was high competitiveness initially, but later on the competition decreased. However it got

momentum by 1985 and reached the highest competitive level by the year 1988-89 and in the

same year (i.e. 1988-89) the model interest revenue as dependent variable without the annual

DEA efficiency score as the bank specific factor shows perfect competition in the Indian banking

sector. During the liberalization period there was huge volatility in the competition levels. In the

Initial years of the reform period, Indian banking sector showed high competitiveness but it fell

down by 1992. However it got the momentum by 1995, but again it shows a declining trend from

1996 onwards in the competitive levels. Again the post reform period also exhibits huge

volatility in the banking competition levels. In the initial years it had shown less competitive

conditions but it picked up the momentum in the competition levels by 2004. However banks

showed relatively more competitiveness in the second half of the post reform period compared to

first half of the post reform period. Finally the year 2011 shows highest competition in the post

reform period. On the whole Indian banking sector is operating under monopolistic competition

and these results corroborate the study of Prasad and Ghosh (2005).

Granger-causality test results indicates in the first case (where the competition is measured using

total revenue as the dependent variable without including DEA as the bank specific factor in the

model) the efficiency Granger-causes the competition and competition does not Granger-cause

the efficiency. An opposite pattern is found in the second case (where the competition is

measured using interest revenue as the dependent variable without including DEA as the bank

specific factor in the model) where competition Granger-causes the efficiency and efficiency

does not Granger-cause the competition.

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Appendix: Table 7: Correlation

TACR3 TACR5 TACR8 DCR3 DCR8 DCR5 THHI DHHI 1PR1 PR12 PR21

TACR50.996**

* 1

(0.000)

TACR80.983**

*0.990**

* 1

(0.000) (0.000)

DCR30.976**

*0.975**

*0.961**

* 1

(0.000) (0.000) (0.000)

DCR80.984**

*0.992**

*0.993**

*0.976**

* 1

(0.000) (0.000) (0.000) (0.000)

DCR50.981**

*0.986**

*0.976**

*0.992**

*0.991**

* 1

(0.000) (0.000) (0.000) (0.000) (0.000)

THHI0.992**

*0.993**

*0.985**

* 0.9660.983**

*0.976**

* 1

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

DHHI0.956**

*0.962**

*0.962**

*0.986**

*0.973**

*0.982**

*0.962**

* 1

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

PR11 0.195 0.200 0.272 0.075 0.228 0.123 0.209 0.103 1

(0.293) (0.282) (0.139) (0.688) (0.217) (0.509) (0.259) (0.581)

PR12 0.132 0.134 0.202 0.030 0.167 0.075 0.153 0.0590.956**

* 1

(0.480) (0.472) (0.277) (0.872) (0.371) (0.689) (0.411) (0.754) (0.000)

PR210.505**

*0.493**

*0.514**

* 0.395**0.479**

* 0.419**0.495**

*0.373*

*0.781**

*0.686**

* 1

(0.004) (0.005) (0.003) (0.028) (0.006) (0.019) (0.005) (0.039) (0.000) (0.000)

PR220.543**

*0.527**

*0.551**

* 0.442**0.520**

*0.468**

*0.546**

*0.431*

*0.775**

*0.778**

*0.909**

*

(0.002) (0.002) (0.001) (0.013) (0.003) (0.008) (0.002) (0.016) (0.000) (0.000) (0.000)

*, **, *** denote an estimate significant at 10%, 5% and 1% level.