Competition and market power within the Italian banking system*

preliminary version, not to quote

This draft: November 2009

Juan S. Lopez(a) and Stefano Di Colli(a),(b)

(a)Federcasse Italian Association of Cooperative Banks Economic Research Department

Via Lucrezia Romana 41/47, 00178 Rome, Italy jlopez@federcasse.bcc.it and sdicolli@federcasse.bcc.it

and (b)University of Rome Tor Vergata

PhD Candidate in Money, Banking and Finance

Abstract Abstract Abstract Abstract The aim of this paper is to assess the level of the competition prevailing in the Italian banking system. The current analysis is based on a comprehensive panel dataset of Italian commercial, cooperative and popular banks covering the period 1994-2005. The so-called Panzar Rosse H-statistic is estimated. In particular, Panzar Rosse methology has been applied for the first time with a dynamic panel methodology on Italian data. This is in line with results by Goddard and Wilson (2009) who demonstrated distortions in estimating H-statistic with a panel fixed effects framework. H-statistic estimation over time reveals a hump-shaped profile throughout the time horizon under consideration, suggesting an increasing competition in the Italian banking sector. Furthermore, the empirical analysis shows that cooperative banks seem enjoy a lower degree of market power than commercial banks, in contradiction with evidence shown by Gutiérrez (2008). Key words:Key words:Key words:Key words: Banking Competition, market structure, concentration. JEL classification:JEL classification:JEL classification:JEL classification: D4, G2, G21, G3.

* The authors are grateful to Anna Di Trapano, Giorgio Gobbi and Claudia Guagliano for valuable comments and suggestions. The views expressed in this paper are personal and not necessarily reflect those of Federcasse.

2

1 Introduction1 Introduction1 Introduction1 Introduction

During the last decade, competition has become a recurrent topic in the

banking literature. As a matter of fact, a dynamic process of consolidation

within banking industry starting from Nineties has been fastened by the

deregulation of capital markets, the harmonization of financial legislations and a

reduction of entry barriers. In Europe, the third stage of the Economic and

Monetary Union jointly with the prospect of a common market and the

deregulation of financial services have contributed to important changes in

European banking markets, forcing domestic banks to search for higher levels of

efficiency, offering diversified services to customers and imposing the need of

exploiting scale economies. In other words, banks have been pushed to increase

their size in order to cut costs and gain market share. The wave of mergers and

acquisitions of recent years could be explained in this way. As a consequence,

this process of consolidation affected competitive forces in the banking industry

and enhanced cross-border capital flows. A great deal of empirical work has

estimated different measures for the level of competition and market power of

European banking market (see Table 1).

Concentration and competition are linked to product markets and

geographical areas. Banks provide a multitude of product that do not serve a

unique market, and defining a relevant market involves making a preliminary

decision about potentially relevant structural characteristics, such as

concentration and competition. The relevant market includes al suppliers of

suppliers of a good who are actual or potential competitors, and it has a

product dimension and a geographical dimension. The product definition of a

market requires the determination of a range of products, which can be assigned

to a particular market on the basis of their substitutability in terms of consumer

demand. Likewise, the geographical boundaries of a market are drawn according

to existing and potential contacts between actual and potential market

participants. They are determined from the customer’s point of view and take

into consideration individual consumer as well as product characteristics. The

mobility of banking customers, and therefore the geographic boundaries of the

3

market, depend of the type of customers and their economic size; the local

dimension of a market is relevant for retail banking products and the regional or

international dimension is relevant for corporate banking. Product

characteristics influence the mobility of customers in that commercial borrowers

tend to display greater mobility in their search for financing possibilities than

depositors.

Italy, among other European countries, has followed this path as well. In

the last fifteen years the number of banks declined by a third, and their average

size and their branch network more than doubled. Market structure indicators,

such as the Herfindal-Hirschman Index calculated between 1995 and 2004

suggest a degree of concentration that is larger in Italy than in Germany, and

the UK, but lower than in France, probably due to an increase in concentration

at national level (Drummond, Maechler and Marcelino, 2006). According to the

Italian Central Bank, this development has contributed to greater competition

in provincial and regional markets. The resulting increased concentration might

augment the market power of active banks. In this way, measures of

concentration and competition are essential to investigate the implications of

these developments.

This paper focuses on the relationship between concentration and market

power for Italian banking market using three econometric techniques, Panzar-

Rosse H-statistic, Lerner Index and the Boone Indicator, in order to compare

results. In particular the Panzar-Rosse H—statistic is estimated with a dynamic

panel technique on different governance models for banks: cooperative banks,

popular banks

2 2 2 2 Theoretical frameworkTheoretical frameworkTheoretical frameworkTheoretical framework

The literature on the measurement of competition can be divided into

two major strands: 1) structural models, 2) non-structural models. The

structural approach to measurement of competition involves the Structure-

Conduct-Performance paradigm (SCP) and the efficiency hypothesis (EH). The

SCP paradigm and the EH investigate if a highly concentrate market causes

collusive behaviour among banks increasing their profits or the efficiency of

4

larger banks enhances their performance. Non-structural models, namely the

Iwata model (Iwata, 1974), the Bresnahan model (Bresnahan, 1982; Lau, 1982)

and Panzar-Rosse model (Panzar and Rosse, 1987), are derived from the

Industrial Organization Theory, in particular the so-called New Empirical

Industrial Organization.

2.1 Structural models

Structural measures of competition may be divided in two parts: the

formal and the non formal approaches. In the first part a formal expression for

the competition-concentration relationship, the Herfindal Hirschman Index

(HHI) is proposed. The second paragraph discusses two non-formal approaches

to the market structure-market performance relationship: the Structure-

Conduct-Performance and the Efficiency Hypothesis models, which are called

non formal because they are not derived analytically.

The formal approach to the competition rooted in Industrial

Organization theory. The derivations are based on the maximisation problem

for oligopolistic markets (Cowling, 1976; Cowling and Waterson, 1976). In this

framework, there are n unequally sized banks in the industry producing a

homogeneous product. The profit function for an individual bank take the usual

form:

( ) iiiii Fxcpx −−=Π (2)

where Πi is profit, xi is output, p is output price, ci are the variable costs, Fi are

fixed cost of i-th bank. The inverse demand function is defined as

( )nxxxfXfp +++== ...)( 21 . The following first order condition for profit

maximising

( ) ( ) Π

′ ′= + − = 0i

i ii i

d dXp f X c x

dx dx (3)

can be rewritten as:

( ) ( ) ( )λ′ ′+ + − = 1 0i i i ip f X x c x (4)

5

where i

n

ij ji dxxd /∑ ≠=λ is the conjectural variation of bank i with respect to all

other banks in the market. It allows differentiation between various market

form. In fact, depending on the underlying market form, iλ can take values

between —1 and i

n

ij j xx /∑ ≠. In the case of perfect competition, an increase in

output by one bank has no effect on the market price and quantity2. A bank

operating in a Cournot oligopoly expects other banks to remain inactive in

response to an increase in total industry output by the same amount3. In the

case of perfect collusion, a bank i expects full reaction from its competitors in

order to protect their market share4.

Multiplying equation (4) with xi and summing the result over all banks

yields:

( ) ( )= = =

′ ′+ − =

∑ ∑ ∑

22

21 1 1 0

n n nii i i ii i i

i

dX xpx f X X c x x

dx X (5)

which can be rewritten as

( )( )( )γ η

=

′−= − +∑ 1

1 /n i i i i

Di

px c x xHHI

pX (6)

where ( )XXfpdpXdXpD′== //η , ∑∑ ==

=n

i i

n

i ii xx1

2

1

2 /λγ , which represents the

average price-cost margin in terms of Dη , the price elasticity of demand, the

Herfindal Hirschman Index (being 2

1

n

iiHHI s

==∑ where s is the bank size

measured as a market share)and γ , a term capturing the conjectural variation.

This theoretical derivation is in line with the SCP assumptions that a higher

degree of concentration in an industry results in higher price-cost margins and it

justifies the use of the HHI like a measure of concentration in S-P relationships,

when γ is known and equal for all banks.

The non formal way to structural approach consists of the Structure-

Conduct-Performance (SCP) paradigm and the efficiency hypothesis. These 2 ( )λ= = +/ 0 1i idX dx and hence λ = −1i 3 ( )λ= = +/ 1 1i idX dx so that λ = 0i 4 ( )λ= = +/ / 1i i idX dx X x i.e. an increase in output by bank I by one unit leads to an increase in market output by

/ iX x units.

6

models have been frequently applied in empirical estimations, even though they

lack a formal theoretical derivation.

In its original form, the SCP approach (Mason, 1939; Bain, 1951)

explains market performance assuming a link between market structure,

behaviour of banks and profitability. Structure and performance are positively

related because firms in higher concentrated market are supposed to have

collusive behaviour and greater market power, resulting in better market

performance (Goldberg and Rai, 1996) and increasing profits. In fact, a higher

level of concentration is supposed to fester collusion among the active banks and

to reduce the degree of concentration.

The SCP has been criticised by various authors, as Gilbert (1984), Reid

(1987), Vesala (1995) and Bos (2002). They noted the fact that an higher level

of efficiency for banks can increase profits is not necessarily related to market

concentration. The one-way causality — from market structure to market

performance — implies a positive link between market structure and profitability

which may be not a correct signal of the SCP hypothesis5.

Empirical studies on SCP for the banking industry don’t find

unambiguous evidence supporting the theory. If on one side the results by

Berger and Hannan (1989), Hannan and Berger (1991) and Pilloff and Rhoades

(2002) are in line with the SCP predictions, on the other side Jackson (1992),

Rhoades (1995) and Hannan (1997) are not6.

The efficiency hypothesis (EH) were developed by Demsetz (1973) and

Peltzman (1977). It postulates that efficient banks are able to maximise profits

and gain market share by reducing prices. Consequently, market concentration

increases automatically, being a result of the superior efficiency of the leading

banks. In fact, a bank with a higher degree of efficiency than its competitors can

adopt two different strategies: a) to maximise profits by maintaining the present

levels of prices and company size, b) to maximise profits by reducing prices and

expanding the size of the company. In the latter case, the most efficient banks

will gain market share and bank efficiency will be the driving force behind the

5 Smirlock (1985), Berger (1995), Goldberg and Rai (1996) and Molyneux(2003). 6 Surveys on empirical studies about SCP are given by Gilbert (1984) and Weiss (1989).

7

process of market concentration without necessarily reducing the

competitiveness.

The difference between the SCP paradigm and the efficiency hypothesis

can be demonstrated by the following equation (Bikker and Haaf, 2002):

∑=

+++=Πn

i

iiijjij XMSCR1

210 αααα (7)

where Πij represents a measure of performance of company i in the j’s market.

CRj is a measure of concentration and MSij is the market share. Both CRj and

MSij are proxies for the market structure. Xi is a vector of control variables

included to account for company as well as market specific characteristics. The

traditional SCP relationship holds if α1 > 0 and α2 = 0. The efficiency

hypothesis is supported by the data when α1=0 and α2 > 0.

2.2 Non structural models

Non structural models do not infer the competitive conduct of banks

through the analysis of market structure, but rather recognize that banks

behave differently depending on the market structure in which they operate.

Under this framework, the “Contestable Markets Theory” (CMT), first

developed by Baumol (1982), stresses that a concentrated industry can behave

competitively if the barriers for new entrants to the market are nonexistent or

low. In a perfectly contestable market, entry is absolutely free, exit is

completely without cost and the demands for industry outputs are highly price-

elastic. In practice, entering banking markets demands considerable investments

in terms of sunk costs. Moreover, regulation poses a justifiable entry barrier

from a financial stability perspective. However, in contrast to Canoy et al.

(2001), we expect that the potential negative consequences of a concentrated

banking sector will be largely offset by free entry. Incumbents offer a wide range

of products and services via various channels at the same time whereas new

financial players can easily focus on a particular customer or product market

with limited distribution channels. They are always vulnerable to hit-and-run

entry when they try to exercise their potential market power. In this framework

a concentrated banking market can be effectively competitive even if it is

8

dominated by large banks. Therefore, policymakers should be relatively less

concerned about the market dominance of some types of financial intermediaries

in a country’s financial system, if the financial markets are contestable.

The New Empirical Industrial Organisation (NEIO) approach tries to

test conduct of banks directly addressing by firms’ behaviour in three ways: i)

the Iwata model, ii) the Bresnahan model and iii) the Panzar-Rosse model.

The Iwata model estimates conjectural variations for individual banks

supplying homogeneous product in an oligopolistic market (Iwata, 1974). This

measure has been applied to the banking industry (in a two-banks market

framework) by Shaffer and Di Salvo (1994).

Bresnahan (1982) and Lau (1982) present a short-run model for the

empirical determination of the market power of an average bank. Based on

time-series of industry data, they estimate a parameter which can be interpreted

as a conjectural variation coefficient or the perceived marginal revenue. This

parameter represents the behaviour of firms and the degree of their market

power (Breshanan 1982, 1989; Lau, 1982; Alexander, 1988), being determined by

simultaneous estimations on market demand and supply curves7. Empirical

application of the Bresnahan model have been given by Shaffer (1989 and 1993,

for, respectively, the US and the Canadian banking industry). Suominen (1994)

applied it to the Finnish loan market, Swank (1995) to the Dutch loan and

deposit markets (finding that both over the period 1957-1990 were significantly

more oligopolistic than in Cournot equilibrium), while Bikker (2002) tested nine

different deposit and loan banking markets, being not able to reject perfect

competition.

The Panzar and Rosse (P-R) model is based on the evaluation of the

impact of input price variations on firm revenue through an index (the Panzar-

Rosse H-statistic) calculated the sum of elasticities of the reduced-form revenue

with respect to all the factor prices (Rosse and Panzar, 1977; Panzar and Rosse,

1987). Its value depends on the price elasticity of demand faced by bank i.

7 ( )λ ≠= + ∑1 / /i j j id x dx n with λ≤ ≤0 1i

9

The application of P-R model to banking requires to assumes banks as

single-product companies, using deposits and other funding costs as inputs to

produce merely loans and other interest-earning assets. This is consistent with

the intermediation approach where banks are considered mainly as financial

intermediaries. In theory, a natural monopoly will eventually emerge if only one

producer is able to produce all products at minimum cost. If, however, there is

space for more than one producer, an oligopoly will obviously develop.

Moreover, if the banking market is characterised by increasing returns to scale,

the optimum size of an individual bank will constantly increase with expanding

demand. In this situation, consolidation process is the result of a dynamic

market process. This natural tendency to concentrate activities would

ultimately lead to the survival of only one viable bank and a concentration ratio

of one. On the other hand, in the absence of economies of scale and scope for all

products and services, it would be possible for several banks to operate in a

highly competitive market under certain circumstances.

In particular, Panzar and Rosse show that banks need to have operated

in a long-term equilibrium while their performance are influenced by the actions

of other market participants. Following Bikker and Haaf (2002), the model

assumes price elasticity of demand greater than unity and homogeneous cost

structure. Bank i maximises profits where marginal revenue equals marginal

cost:

( ) ( ), , , 0R Ci i i i i i iR x z C x w z− = (8)

where R(•) and C(•) are the revenue and cost function for bank i, xi is the

output of the i-th bank, wi is a n-dimensional vector of factor input prices of i-

th bank, Riz is a m-dimensional vector of exogenous variables shifting the

revenue function, while Ciz a k-dimensional vector of exogenous variables

affecting the cost function. In equilibrium, at individual level marginal revenues

are equal to marginal costs:

( ) ( ), , ,R Ci i i i i i iR x z C x w z′ ′= (9)

10

Under this assumptions, a change in factor input may be reflected in the

equilibrium revenues earned by bank i. The H-statistic is a measure of

competition given by the sum of the elasticities of the reduced form revenues

with respect to factor prices:

=

∂= ∂ ∑ 1

i

i

m kik

k i

wRH

w R (10)

The estimated value of the H-statistic could be included between

−∞ < ≤ 1H . H < 0 means that underlying market is a monopoly, 0<H <1 for

monopolistic competition and H = 1 in case of perfect competition

This technique analyses directly firms’ conduct avoiding indirect

inferences about market power based on indicators of concentration, but it need

detailed informations on costs and demand. The H-statistic consists of a

comparative static analysis and its main advantage is the need only of firm-

specific data on revenues and factor prices.

3 3 3 3 Data descriptionData descriptionData descriptionData description

Detailed dataset used in this work is obtained directly from the

information contained in balance sheets of Italian Banks, reported to the Italian

supervisory authority during the years 1995 — 2004. Taking into account

problems related to different accounting standards, 1995 was chosen as the

earliest observation. Another point was making our results comparable with the

estimation results proposed by with Gutierrez (2008).

The balance sheets and income statements are reported on a monthly,

quarterly and half-yearly basis. End-of-year (December) aggregates have been

considered in order to transform accounting information into yearly data.

The data are consolidated data from the commercial, cooperative and

saving banks. Observations pertaining to other types of financial institutions

have been removed. Data from banks in special circumstances, like holding

companies, banks in their start-up periods in ending part of the sample were not

considered. Following Gutiérrez de Rozas (2007), mergers and acquisitions were

taken into account, contrasting with several previous works. Each transaction is

11

considered to generate an entirely new institution, named like the final

recipient. In this way, structural breaks in the data are avoid.

Other general consistency checks have been undertaken, excluding all

observations where banks report missing values and adjusting data for outliers.

The resulting dataset is a balanced panel composed by 6015 observations.

The dependent variable is explained by factor prices and other bank-

specific variables that affect long-run equilibrium bank revenues for the years

1995 through 2004. In particular, the dependent variable (yit) is total interest

revenue (or total revenue), ieit represents interest expenses to total funds, peit

personnel expenses to total assets, ceit capital expenses and other administrative

expenses to fixed assets.

The intermediation approach defines banks as financial intermediaries

that create output only in terms of their assets, using their liabilities, labor and

capital. Deposits are considered as inputs that are intermediated into banks’

outputs (loans and investments) and interest on deposits is a component of total

cost, together with labor and capital costs. The production approach, views

banks as firms that use capital and labor to produce loans and deposits. Since

deposits are considered as output, the interest expense on deposits is not

included in the costs8, interest expenses to deposits and other liabilities, the

ratio of personnel expenses to total assets, and the ratio of non interest

expenses to fixed assets. A number of control variables, included to account for

size, risk, and deposit mix differences, are introduced: total assets (tait), capital

to total assets (cait), total loans on total assets(tlit), deposits on total assets

(deit).

4444 Empirical frameworkEmpirical frameworkEmpirical frameworkEmpirical framework

4.2 PR H-statistic

Panzar-Rosse H-statistic, as shown above, is calculated as the sum of

elasticities of the reduced-form revenue with respect to all the factor prices. In

8 Berger et al. (1987).

12

practice, it is usually computed summing elasticity coefficients from fixed effect

regressions to panel data for individual firms.

The empirical application of the P-R approach usually assumes log-

linearity in the specifications of the marginal revenue and cost-functions from

equation (8). Following the demonstration by Gutiérrez de Rozas (2007), we can

rewrite:

( ) ( ) ( )0 1 1ln ln ln

M Ri i m mim

R x zα α γ=

′ = + +∑ (11)

( ) ( ) ( ) ( )0 1 1 1ln ln ln ln

N K Ci m ki k kii n k

C x w zµ µ β φ= =

′ = + + +∑ ∑ (12)

For a profit-maximising bank the equilibrium output results from (8):

( ) ( ) ( )

( ) ( )0 1 0 11

1 1

ln ln ln

ln ln

M Ri m mi im

N K Cm ki k kin k

x z x

w z

α α γ µ µ

β φ

=

= =

+ + = + +

+ +

∑∑ ∑

(13)

Rearranging terms:

( ) ( ) ( ) ( )( )0 1 1 11

1ln ln ln ln

N K MC Ri m ki k ki m min k m

x w z zλ β φ γλ = = =

= + + −∑ ∑ ∑ (14)

where λ µ α= − . From the product of the equilibrium output of bank i and

common bank level, given by the inverse demand equation, it is possible to

derive the reduced form equation for revenues of the representative bank:

( ) ( ) ( )1 1ln ln ln

N S

i m ki k kin sR w zω β φ

= == + +∑ ∑ (15)

where zi is a s-dimensional vector of bank-specific variables. According to P-R

1

N

mnH β

== ∑ (16)

Empirical applications of the Panzar-Rosse test to the European banking

industry have been carried out by several authors. Among others9, Vesala

(1995) tested P-R method for Finland finding evidence of monopolistic

competition; Molyneux et al. (1996) for Japan; Rime (1999) for Switzerland

9 Shaffer (1982, 2002, 2004), Nathan and Neave (1989), Bikker and Groeneveld (2000), Molyneux et al. (1994, 1996), Coccorese (1998, 2004, 2009), Hondroyiannis et al. (1999), De Bandt and Davis (2000), Bikker and Haaf (2002), Gelos and Roldos (2004), Gutièrrez (2008), Al-Muharrami et al. (2006), Casu and Girardone (2006), Matthews et al. (2007), Vesala (1995).

13

(monopolistic competition); Gutiérrez de Rozas (2007) for Spain (monopolistic

competition).

Cross-country studies including Italy have been proposed by Molyneux et

al. (1994) for France, Germany, Italy, Spain, United Kingdom finding evidence

of monopoly for Italy and of monopolistic competition for France, Germany,

Spain, United Kingdom; Bikker and Groeneveld (2000) for EU-15 countries

(monopolistic competition); De Bandt and Davis (2000) for France, Germany

and Italy (large banks: monopolistic competition in all countries; small banks:

monopolistic competition in Italy, monopoly in France, Germany); Bikker and

Haaf (2002) for 23 OECD countries (monopolistic competition); Claessens and

Laeven (2004) for 50 countries; Staikouras and Koutsomanoli-Fillipaki (2006)

for Germany, Spain, France, United Kingdom and Italy; Bikker, Spierdijk and

Finnie (2007) for bank competition across 76 countries.

Italian-specific studies have been conducted by Coccorese (1998, 2009) for

Italy (monopolistic competition) and for Italian local banks (monopolistic

competition); Gutiérrez (2008) for Italian banks distinguishing different banking

governance structures for ownership; Drummond, Maechler and Marcelino

(2007) (monopolistic competition).

On the basis of (15), we estimated the following bank revenue equation

for the Italian banking system:

0 1 2 3

1 2 1 1

ln ln ln ln

ln ln ln lnit it it it

it it it it it

y ie pe ceta ca tl de u

β β β β

γ γ δ δ

= + + + +

+ + + + + (17)

Results on equation (17) are presented in Table (3), showing that

monopolistic competition hypothesis is accepted for the complete sample and for

all the subsamples (1996-98, 1999-2001, 2002-2004). In particular H-statistic

increased over time.

Differently with respect to all studies presented above, with the exception

of Drummond, Maechler and Marcelino (2007), we used also dynamic panel

regression technique by Arellano and Bond with multiple instruments. A crucial

point of P-R approach, as a matter of fact, is that the correct identification of

the H-statistic is based on the assumption that markets are in long run

14

equilibrium at each point in time. On the other hand, the micro theory

underlying the Panzar Rosse test relies upon a static equilibrium framework.

But the adjustment towards equilibrium sometimes could be less than

instantaneous and, in that case, market is temporary out of equilibrium. In

such a situation, with partial not instantaneous adjustment, misspecification

bias arises, necessitating a dynamic structure with the inclusion of a lagged

dependent variable among the covariates. Goddard and Wilson (2008)

investigated the implications for the estimation of H statistic of this form of

misspecification bias in the revenue equation. They demonstrated in a Monte

Carlo simulation exercise that FE estimation for PR of a static revenue

equation produces a measured H-statistic which is biased towards zero, reducing

the ability for the researcher to distinguish between the three theoretical market

structure. On the contrary, dynamic panel estimation permits unbiased

estimation of the H-static.

ititititit

ititititit

udetlcata

cepeieyy

∆+∆+∆+∆+∆+

+∆+∆+∆+∆+=∆ −

ln ln ln ln

ln ln ln ln ln

1121

321110

δδγγ

βββαα (18)

In particular, Gutiérrez (2008) computed the H-statistic estimating fixed

effects regressions for Italian banks (distinguishing between all banks,

cooperative banks, saving banks, commercial banks). She found evidence in

favour of monopolistic competition hypothesis, concluding that cooperative and

saving banks enjoy higher degree of monopoly power than commercial banks.

The first point against this conclusion is that H-statistic is not able to capture

changes in banking structure and could be used only to test the three

theoretical hypothesis shown above and not to compare monopolistic degree

(Boone, 2000). Furthermore, fixed effects regression produces, as shown before,

a measured H-statistic that is severely biased towards zero.

Here, equation (18) has been estimated for the Italian banking system,

for the Italian cooperative banks and for the Italian banking system without the

Italian cooperative banks using Arellano and Bond estimators with multiple

instruments. First of all, AR results are in favour of monopolistic competition

hypothesis, in line with the main literature on market power within Italian

15

banking system (see Table 5). Furthermore Results shown in Table 4 lead to

the conclusion that there is no significant difference between estimated H-

statistic for Italian banking system and cooperative banks. In other words,

hypothesis of the existence of a sort of market power “niche” of cooperative

banks can be rejected.

4444 ConclusionsConclusionsConclusionsConclusions

Many studies have attempted to determine the degree of competition in

banking markets. This paper has applied one of the most popular econometric

technique to a sample of Italian banks, the Panzar-Rosse H-statistic, estimated

with a dynamic panel methodology in order to avoid the misspecification bias in

the revenue equation identified by Goddard and Wilson (2009). In particular, H-

statistic has been estimated for the entire banking system, for cooperative

banks, for popular banks and for saving banks. Results are not in line with

Gutiérrez (2008), showing no significant differences between banking system and

cooperative banks. Main important results are that monopolistic competition

hypothesis is accepted for Italian banking system during the period 1995-2004,

level of competition increased in the same period, while cooperative credit banks

didn’t hold an higher level of market power.

16

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18

TTTTABLESABLESABLESABLES

Table 1Table 1Table 1Table 1. Panzar Rosse studies for the Italian banking system

Authors Countries

considered Period

Cross country

studies including

Italy

Molyneux et al. (1994) DE, ES, FR, IT, UK 1986-1989

Bikker and Groenveld (2000) 15 EU cou. 1989-1996

De Bandt and Davis (2000) DE, FR, IT,US 1992-1996

Bikker and Haaf (2002) 23 OECD countries 1990-1998

Claessens and Laeven (2004) 50 countries 1994-2001

Casu and Girardone (2006) 15 EU cou. 1997-2003

Staikouras et al. (2006) DE, ES, FR,

IT, UK 1998-2002

Bikker, Spierdijk and Finnie (2007) 101 countries 1986-2005

Italy-specific

studies

Coccorese (1998) IT 1995-1998

Coccorese (2009) IT (local markets) 1988-2005

Drummond, Maechler and Marcelino (2007)

IT, FR, DE, ES 1995-2004

Gutiérrez (2008) IT 1995-2004

19

Table Table Table Table 2222. Descriptive Statistics

Variables Min Max Mean STD

Total interest revenue 70.24 1.0e+07 115349.9 541015.5

Interest expenses to total funds 0.0013 0.9863 0.0438 0.0395

Personal expenses to total assets

0.0002 0.2564 0.0177 0.0091

Capital expenses to fixed assets

6.3e-06 4.0224 0.1723 0.1458

Total Assets 1.94e+08 0.1013 1573420 8242706

Capital to total assets 0.0122 1.3898 0.1139 0.0694

Total loans on total assets

0.0001 5.3064 0.5264 0.2297

Deposits on total assets 0.0000 10.0491 0.7864 0.3434

20

TableTableTableTable 3333. Regressions on Italian banking system

Within Regression FEWithin Regression FEWithin Regression FEWithin Regression FE Pooled Least SquaresPooled Least SquaresPooled Least SquaresPooled Least Squares

Variable Coeff 95-04 96-98 99-01 02-04 95-04 96-98 99-01 02-04

Constant α0 0.2968 -0.2042 0.0740 2.8264 -0.5379 0.0113 -1.0077 -0.3685

[0.000] [0.016] [0.000] [0.000] [0.000] [0.282] [0.000] [0.024]

ieit β1 0.2859 0.2908 0.2711 0.0858 0.2742 0.2288 0.1246 0.0817

[0.000] [0.000] [0.000] [0.002] [0.000] [0.000] [0.000] [0.001]

peit β2 0.3576 0.2936 0.2977 0.0211 0.3081 0.2523 0.2625 0.3046

[0.000] [0.000] [0.000] [0.726] [0.000] [0.000] [0.000] [0.000]

ceit β3 0.2101 0.0109 0.0258 0.6833 0.0948 -0.0212 0.0048 0.2901

[0.000] [0.042] [0.008] [0.000] [0.000] [0.000] [0.403] [0.000]

H H H H ---- stststst 0.80.80.80.8555536363636 0.0.0.0.5953595359535953 0.0.0.0.5946594659465946 0.0.0.0.7902790279027902 0.0.0.0.6771677167716771 0.0.0.0.5023502350235023 0.0.0.0.3919391939193919 0.0.0.0.6764676467646764

p(Ftest)

[0.000][0.000][0.000][0.000] [0.000][0.000][0.000][0.000] [0.000][0.000][0.000][0.000] [0.0[0.0[0.0[0.000000000]]]] [0.000][0.000][0.000][0.000] [0.0[0.0[0.0[0.000000]0]0]0] [0.000][0.000][0.000][0.000] [0.[0.[0.[0.080]080]080]080]

tait γ1 0.9759 0.9705 0.8496 0.9749 0.9721 0.9670 0.9756 0.9808

[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

cait γ2 -0.0938 -0.0785 -0.1485 -0.0695 -0.0811 -0.0940 -0.0917 -0.0112

[0.000] [0.004] [0.000] [0.015] [0.000] [0.038] [0.000] [0.548]

tlit δ1 -0.0775 0.0384 0.0637 0.0851 -0.0772 -0.0322 -0.0186 -0.0145

[0.000] [0.056] [0.003] [0.008] [0.000] [0.001] [0.000] [0.542]

deit δ2 -0.1274 0.0153 -0.0655 -0.0678 -0.1080 -0.0867 -0.0683 -0.0260

[0.000] [0.742] [0.007] [0.013] [0.000] [0.166] [0.000] [0.024]

# observ.

6002 1855 1835 1726 6002 1855 1835 1726

R2

0.92 0.99 0.84 0.74 0.98 0.98 0.99 0.97

The dependent variable (yit) is the ratio of total interest revenue to total assets, ieit represents interest expenses to total funds, peit is personnel expenses to total assets, ceit is capital expenses to fixed assets, while control variables are total assets (tait), total capital (cait), total loans on total assets(tlit), deposits on total assets (deit)

21

TableTableTableTable 4444. Dynamic panel regressions with Arellano and Bond technique

Variable Coeff All

banks CCB Other banks

Constant α0 -0.0008 0.0006 0.0113

[0.879] [0.868] [0.282]

yit-1 α1 -0.0055 -0.0156 -0.0028

[0.232] [0.000] [0.664]

ieit β1 0.3358 0.3818 0.3501

[0.000] [0.000] [0.000]

peit β2 0.2984 0.2986 0.3011

[0.000] [0.000] [0.000]

ceit β3 0.0882 0.0074 0.0950

[0.042] [0.100] [0.040]

H H H H ---- stststst 0.72240.72240.72240.7224 0.68780.68780.68780.6878 0.0.0.0.7474747462626262

p(Ftest)

[0.000][0.000][0.000][0.000] [0.0[0.0[0.0[0.000000000]]]] [0.0[0.0[0.0[0.000000]0]0]0]

tait γ1 0.9705 0.9749 0.9542

[0.000] [0.000] [0.000]

cait γ2 0.0785 0.0695 0.0807

[0.004] [0.015] [0.038]

tlit δ1 0.0384 0.0851 0.0023

[0.756] [0.166] [0.201]

miit δ2 -0.0153 -0.0678 -0.0883

[0.042] [0.013] [0.036]

# observ.

3708 2929 1285

AB test AR(2) 0.537 0.243 0.641

The dependent variable (yit) is the ratio of total interest revenue to total assets, ieit represents interest expenses to total funds, peit is personnel expenses to total assets, ceit is capital expenses to fixed assets, while control variables are total assets (tait), total capital (cait), total loans on total assets(tlit), deposits on total assets (deit)

22

Table Table Table Table 5555. Estimated H-Statistics for Italian Banking system

Papers Period

Molyneux et al. (1994) 1986-1989 -0.61

Bikker and Groenveld (2000) 1989-1996 0.91

De Bandt and Davis (2000) 1992-1996 0.51

Bikker and Haaf (2002) 1990-1998 0.82

Weill (2004) 1994-1999 0.62

Claessens and Laeven (2004) 1994-2001 0.60

Casu and Girardone (2006) 1997-2003 0.41

Staikouras et al. (2006) 1998-2002 0.67

Drummond, Maechler and Marcelino (2007) 1998-2004 0.71

Gutiérrez (2008) 1995-2004 0.55

This studyThis studyThis studyThis study

WRFE 1995-2004 0.850.850.850.85

GPLS 1995-2004 0.680.680.680.68

Arellano-Bond 1995-2004 0.720.720.720.72