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
[email protected] and [email protected]
and
(b)
University of Rome Tor Vergata
PhD Candidate in Money, Banking and Finance
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: Banking Competition, market structure, concentration.
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.
1 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
2
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, PanzarRosse 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 Theoretical 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 StructureConduct-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
3
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 StructureConduct-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:
Π i = px i − ci (xi ) − Fi
(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
p = f ( X ) = f (x1 + x 2 + ... + x n ) . The following first order condition for profit
maximising
 dX 
d Πi
= p + f ′ (X ) 
 − c i′ ( x i ) = 0
dx i
dx
 i
(3)
can be rewritten as:
p + f ′ ( X ) (1 + λi ) x i − c i′ ( x i ) = 0
(4)
4
where λi = d ∑ j ≠ i x j / dxi is the conjectural variation of bank i with respect to all
n
other banks in the market. It allows differentiation between various market
form. In fact, depending on the underlying market form, λi can take values
between —1 and
∑
n
j ≠i
x j / xi . 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:
n
∑i
=1
n
px i + ∑ i =1 f ′ ( X )
 dX  2  x i2 
n

 X  2  − ∑ i =1 c i′ ( x i ) x i = 0
X 
 dx i 
(5)
which can be rewritten as
n
∑i
( px i
− c i′ ( x i ) x i )
pX
=1
= − (1 + γ ) HHI /ηD
(6)
where η D = dXp / dpX = p / f ′(X )X , γ = ∑i =1 λi xi2 / ∑i =1 xi2 , which represents the
n
n
average price-cost margin in terms of η D , the price elasticity of demand, the
Herfindal Hirschman Index (being HHI =
∑
n
2
i =1 i
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 StructureConduct-Performance (SCP) paradigm and the efficiency hypothesis. These
2
dX / dx i = 0 = (1 + λi ) and hence λi = −1
3
dX / dx i = 1 = (1 + λi ) so that λi = 0
4
dX / dx i = X / x i = (1 + λi ) i.e. an increase in output by bank I by one unit leads to an increase in market output by
X / x i units.
5
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
6
Smirlock (1985), Berger (1995), Goldberg and Rai (1996) and Molyneux(2003).
Surveys on empirical studies about SCP are given by Gilbert (1984) and Weiss (1989).
6
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):
n
Π ij = α 0 + α1CR j + α 2 MSij + ∑ α i X i
(7)
i =1
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 priceelastic. 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
7
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 PanzarRosse 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 + d ∑ i ≠ j x j /dx i / n
with
0 ≤ λi ≤ 1
8
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:
Ri ( x i , z iR ) − C i ( x i ,w i , z Ci ) = 0
(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 ith bank, z iR is a m-dimensional vector of exogenous variables shifting the
revenue function, while z Ci a k-dimensional vector of exogenous variables
affecting the cost function. In equilibrium, at individual level marginal revenues
are equal to marginal costs:
Ri′ ( x i , z iR ) = C i′ ( x i ,w i , z Ci )
(9)
9
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:
H =
 ∂Ri

=1 
 ∂w ki
m
∑k
  w ki 

 
  Ri 
(10)
The estimated value of the H-statistic could be included between
−∞ < H ≤ 1 . 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 firmspecific data on revenues and factor prices.
3 Data 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
10
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 bankspecific 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).
4 Empirical 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).
11
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 loglinearity in the specifications of the marginal revenue and cost-functions from
equation (8). Following the demonstration by Gutiérrez de Rozas (2007), we can
rewrite:
R
ln ( Ri′ ) = α 0 + α1 ln ( x i ) + ∑ m =1 γ m ln ( z mi
)
M
(11)
ln (C ′ )i = µ0 + µ1 ln ( x i ) + ∑ n =1 β m ln (w ki ) + ∑ k =1 φk ln ( z Cki )
N
K
(12)
For a profit-maximising bank the equilibrium output results from (8):
R
α 0 + α1 ln ( x i ) + ∑ m =1 γ m ln ( z mi
) = µ0 + µ1 ln ( x i ) +
M
+ ∑ n =1 β m ln (w ki ) + ∑ k =1 φk ln ( z Cki )
N
K
(13)
Rearranging terms:
(λ
λ
1
ln ( x i ) =
0
1
R
+ ∑ n =1 β m ln (w ki ) + ∑ k =1 φk ln ( z Cki ) − ∑ m =1 γ m ln ( z mi
)
N
K
M
)
(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:
ln ( Ri ) = ω + ∑ n =1 β m ln (w ki ) + ∑ s =1 φk ln ( z ki )
N
S
(15)
where zi is a s-dimensional vector of bank-specific variables. According to P-R
H =
N
∑n
=1
βm
(16)
Empirical applications of the Panzar-Rosse test to the European banking
industry have been carried out by several authors. Among others9,
(1995) tested P-R
Vesala
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).
12
(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:
ln y it = β 0 + β1 ln ie it + β2 ln pe it + β 3 ln ce it +
+ γ 1lntait + γ 2 ln cait + δ1 ln tl it + δ1lnde it + uit
(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
13
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.
∆ ln yit = α 0 + α1 ∆ ln yit −1 + β1 ∆ ln ieit + β 2 ∆ ln peit + β3 ∆ ln ceit +
+ γ 1 ∆ ln tait + γ 2 ∆ ln cait + δ1 ∆ ln tlit + δ1 ∆ ln deit + ∆uit
(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
14
banking system (see Table 5). Furthermore Results shown in Table 4 lead to
the conclusion that there is no significant difference between estimated Hstatistic 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.
4 Conclusions
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, Hstatistic 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.
15
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17
TABLES
Table 1.
1 Panzar Rosse studies for the Italian banking system
Authors
Molyneux et al. (1994)
Bikker and Groenveld (2000)
Cross country
studies including
Italy
De Bandt and Davis (2000)
Bikker and Haaf (2002)
studies
15 EU cou.
DE, FR,
IT,US
23 OECD
countries
Period
1986-1989
1989-1996
1992-1996
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
IT
1995-1998
Coccorese (1998)
Italy-specific
Countries
considered
DE, ES, FR,
IT, UK
Coccorese (2009)
Drummond, Maechler and
Marcelino (2007)
Gutiérrez (2008)
IT (local
markets)
IT, FR, DE,
ES
IT
18
1988-2005
1995-2004
1995-2004
Table 2. 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
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
Total Assets
19
Table 3. Regressions on Italian banking system
Within Regression FE
Pooled Least Squares
95-04 96-98 99-01
02-04
95-04
96-98
99-01
02-04
α0
0.2968
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]
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]
0.85
0.8536
0.5953
0.5953
0.5946
0.5946
0.7902
0.7902
0.6771
0.6771
0.5023
0.5023
0.3919
0.3919
0.6764
0.6764
[0.000]
[0.000]
[0.000]
[0.000
[0.000]
00]
[0.000]
[0.00
[0.000]
[0.000]
[0.080]
[0.080]
γ1
0.9759
0.9705
0.8496
0.9749
0.9721
0.9670
0.9756
0.9808
[0.000]
[0.000]
[0.000]
cait
γ2
-0.0938 -0.0785 -0.1485
tlit
δ1
deit
δ2
Variable
Coeff
Constant
ceit
H - st
p(Ftest)
tait
-0.2042
0.0740
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
-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]
-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]
-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)
20
Table 4. Dynamic panel regressions with Arellano and Bond technique
Variable
Coeff
Constant
α0
yit-1
α1
ieit
β1
peit
β2
ceit
β3
H - st
p(Ftest)
tait
γ1
cait
γ2
tlit
δ1
miit
δ2
# observ.
AB test AR(2)
All
banks
CCB
Other
banks
-0.0008
[0.879]
-0.0055
0.0006
[0.868]
-0.0156
0.0113
[0.282]
-0.0028
[0.232]
[0.000]
[0.664]
0.3358
[0.000]
0.2984
[0.000]
0.0882
0.3818
[0.000]
0.2986
[0.000]
0.0074
0.3501
[0.000]
0.3011
[0.000]
0.0950
[0.042]
[0.100]
[0.040]
0.7224
0.6878
0.74
0.7462
7462
[0.000]
[0.000
[0.000]
00]
[0.00
[0.000]
0.9705
[0.000]
0.0785
[0.004]
0.0384
[0.756]
-0.0153
[0.042]
0.9749
[0.000]
0.0695
[0.015]
0.0851
[0.166]
-0.0678
[0.013]
0.9542
[0.000]
0.0807
[0.038]
0.0023
[0.201]
-0.0883
[0.036]
3708
2929
1285
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)
21
Table 5. 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
WRFE
1995-2004
0.85
GPLS
1995-2004
0.68
Arellano-Bond
1995-2004
0.72
This study
22