A new appraisal of the Structure of European Banking Systems

Very preliminary version

Cristina Ruzaa

Rebeca de Juanb

UNEDc

Abstract

This paper aimed at the analysis of the main determinants of banking system structure for European Union countries (EU-25 countries) for the period 1999-2007. For this purpose we firstly propose to perform a cluster analysis in terms of financial structure of EU countries, and separately estimate the model for each of the cluster groups obtained. Those estimated coefficients are applied to a number of transition economies (new EU member states), in order to determine the most appropriate benchmark for the efficient structure of their banking systems. These benchmarks are compared to the actual data for assessing the state of their banking system development and to appraise the degree of banking system convergence.

Keywords: banking system, financial structure, new EU members, integration. JEL Classification: G21, O16, P34.

a Departamento de Economía Aplicada e Historia Económica, Phone number: 0034-913986354. Fax: 0034-913987822. Email address: cruza@cee.uned.es. b Departamento de Análisis Económico II, Phone number: 0034-913987816. Email address: rdejuan@cee.uned.esc Facultad de CC. Económicas y Empresariales, UNED, Paseo Senda del Rey, nº 11, 28040 Madrid, Spain.

1. INTRODUCTION

The accession of the ten Central and Eastern European countries to the

European Union (EU) has posed an additional difficulty for the convergence

process towards the single European financial system. The official accession

candidates, most of them with underdeveloped financial systems, have made

some reforms in order to become more market-orientated, which in turn will

foster their economic growth and inter alia they will also encourage their

convergence towards a more developed financial system.

There is a longstanding debate in the economic literature about the existing

relationship between financial development and economic growth. The pioneer

study who devoted attention to this issue was The Theory of Economic

Development by Schumpeter in 1911 arguing that financial intermediaries play

a pivotal role in economic development because they choose which firms get to

use society’s savings and hence they increase the efficiency of the allocation of

capital.

Since then, a vast number of studies have been focused on deepening into this

relationship and determine which the direction of causality between these two

components is. Even though those studies applied different methodologies,

countries of study, time scope for analysis and variable’s definition, we can

conclude that there is an overwhelming consensus about the positive interaction

between the financial and the real sector of an economy.

The traditional theoretical reasoning for that link is based on the role played by

financial intermediaries in mobilizing funds among surplus and deficit sectors,

their capability for evaluating financing projects and monitoring their

performance and, lastly but not least important, in facilitating any financial

transaction through a well structured payment system. Therefore, the financial

intermediary sector alters the path of economic progress by affecting the

allocation of savings under investment efficiency conditions, but not necessarily

altering the nation’s saving rate.

More recent studies had widened their scope of analysis focusing their attention

not only on the financial intermediaries but on the financial system on the whole,

which means taking into consideration the role played by capital markets. By

distinguishing a bank-based structure (German system) vis-à-vis a market-

dominated approach (American system), some academics have tried to

determine which structure is more efficient in channelling funds among sectors.

However, those studies had not reached a consensus, because other factors

like the regulatory framework or the degree of development of the country

should also be considered for identifying the optimal financial structure for each

case of study.

At this point it appears clear that no general conclusion can be stated about an

optimal financial structure, because in the end it depends upon the country

analysed and its own characteristics. However, once it had been identified the

structure towards which a financial system tends to approximate over time, we

will be better capable of understanding the path of economic development and

growth.

According to that, the aim of this paper is to provide some empirical evidence

on the main determinants of the two aforementioned financial system structures

for a set of European Union countries (EU countries). The data covers the EU-

25 countries to the period 1999-2007.

Therefore, this paper contributes to existing empirical evidence by carrying out

a two step procedure: i) classifying EU-15 countries according to their financial

system structure and ii) applying panel data techniques to each of the cluster

groups obtained.

To do this, we focus attention on analysing the main determinants of financial

structure for two groups of European Union countries: on the one hand, those

based on a bank-dominated financial structure and, on the other hand, those

more close to a market-based structure. To group the countries according to

one of the two financial structure considered, we proceed to identified them

using cluster analyses. Subsequently, we estimated the main determinants of

financial structure for each of the cluster groups obtained using panel data

techniques. Afterwards, we apply those estimated coefficients to the new EU-10

members states in order to determine the benchmark for the efficient structure

of their banking system. Finally, we compare these benchmarks to the actual

data in each new EU member to measure their relative inefficiency and

appraise their banking system converge across time.

Therefore, this paper will produce empirical evidence that (1) identify the

benchmark towards which new EU members are evolving and (2) help policy

makers in designing appropriate financial sector reform strategies.

The rest of the paper is organised as follows. Section 2 reviews the main

literature contributions to the link between financial system and economic

growth, and the main determinants of different financial structures. Section 3

introduces the specification of the model and the data we used. Section 4

presents the empirical results and in Section 5 some concluding remarks and

lines for further research are outlined.

2. LITERATURE REVIEW

Whether financial structure influences economic growth is a crucial policy issue,

and its relative importance has been the focus of a theoretical academic debate

for over a century. There is a bulk of economic literature dealing with this issue

and many theories note that financial intermediaries and financial markets arise

to ameliorate particular information asymmetry problems, however models do

not find consensus about the fundamental channel through which financial

intermediaries are connected to growth.

The pioneer study in this field was The Theory of Economic Development by

Schumpeter in 1911, who stated that the wide variety of services provided by

financial intermediaries are contributing to foster technical innovation and, thus,

economic growth. Later, Hicks (1969) and Levine (1997) refined the argument

by arguing that the key factor behind the rate of England’s economic growth

was financial innovation, rather than technical innovation itself.

Other group of studies (i.e. McKinnon, 1973; Shaw, 1973; Kapur, 1976; Galbis,

1977, among others) focused the attention on the importance of liberalising the

financial system as means of promoting economic growth by allowing financial

intermediaries to allocate the scarce capital resources to the more productive

uses, and hence, increase the volume and productivity of physical capital.

Even the endogenous growth paradigm had reinforced the importance of

financial intermediaries to economic growth, focusing the attention on the

virtuous circle of changes in the saving rate, investment decisions and technical

innovations (Pagano, 1993).

In a different vein, we found sceptical academics denying such a major role of

the financial system arguing that the direction of effects flows actually on the

opposite direction. Therefore, the economic growth the key element that

promotes the financial system to develop once the economy has reached

maturity. The most significant papers on the subject, among others, are

Robinson (1952) and Kuznets (1955).

Lewis (1955) goes beyond by arguing a two-way relationship. He found that

financial system develop as a consequence of economic growth, which in turn

feed back as an incentive for real growth. Likewise, a number of studies

applying endogeneous growth models arrive to the same conclusions

(Greenwood and Jovanovic, 1990 and Bethelemy and Varoudakis, 1997), and

the work of Luintel and Khan (1999) and Rother (1999) found a bi-directional

causality whose explanation is relatively plain: financial intermediation facilitates

growth through their investment efficiency capability, while at the same time the

increased demand for financial services in growing economy induces

subsequent growth in the financial sector.

A different piece of work had focused on modelling the possible simultaneity

bias of the finance-growth relationship by using instrumental variables that

explain cross-country differences in financial development but are uncorrelated

with economic growth (i.e. Levine, 1998 and 1999, and Levine et al, 2000).

All this competing hypothesis previously outlined had set the origin of a

longstanding controversy surrounding the finance- growth nexus. There is a

wide number of review articles like Thakor (1996), or more recently Demirgüc-

Kunt and Levine (2008) which surveyed the main research on the finance-

growth link from different literature perspectives: cross-country studies, models

using instrumental variables, panel data studies, microeconomic studies and

country case analysis. In general terms they concluded that theory provides

ambiguous predictions regarding whether financial development exerts a

positive, causative impact on long-run economic growth, while on the other

hand the consistency of existing empirical results motivates vigorous inquiry into

the policy determinants of financial development as a mechanism for promoting

growth in countries around the world.

A different perspective of analysis consists of determining the main factors

behind the two alternative views of financial development: the bank-based and

the market-based financial structures. Theoretically, bank-based financial

systems such as Germany and Japan, are those where banks play a leading

role in mobilizing savings, allocating capital, overseeing the investment

decisions of corporate managers, and in providing risk management vehicles,

particularly during the early stages of economic development and in weak

institutional environments (Levine, 2002). In market-based financial systems like

the UK and the United States, securities markets share centre stage with banks

in terms of getting society’s savings to firms, exerting corporate control, and

easing risk management.

One of the pioneer and seminal studies that measured the degree of financial

system development was Goldsmith (1969), which measured it as the value of

financial intermediary assets divided by the GNP, and found a strong

relationship between financial and economic development, but that does not

necesarily imply a causality effect.

While King and Levine (1992, 1993) noted that the level of financial

intermediary development precedes and can be interpreted as a good predictor

of economic growth, other studies like Rajan and Zingales (1998), Demirgüc,

Kunt and Maksinovic (1998) demonstrated a causal impact of financial

intermediary development on real per capita GDP growth using firm-level data.

Levine (1997) argues that the size of financial intermediaries is not an adequate

proxy for financial development, and subsequent papers of King and Levine

have proposed different alternatives for measuring it. In all those studies they

found a positive and strong correlation between financial development and

economic growth rates.

The next step in appraising the financial- growth link consists of introducing the

role played by capital markets like in the study of Demirgúc-Kunt and Levine

(1996) that introduced and defined indexes considering aspects like market

liquidity, market concentration and so forth. They found empirical evidence on a

positive correspondence between per capita income and stock market

development, and also between this and financial intermediary development,

which reveals that stock markets and financial intermediaries in fact are acting

as complementary incentives for economic growth. Also, Levine and Zervos

(1998) construct numerous measures of stock market development to assess

the relationship between stock market development and economic growth,

capital accumulation, and productivity within a panel context.

More recent studies like Levine (2002) found that neither bank-based nor

market-based financial systems are particularly effective at promoting growth,

consistent with the so-called financial services view. However, when applying

different estimation techniques (i.e. dynamic heterogeneous panels) Arestis et

al (2004) confirm the significance of financial structure, even in the long run.

A different approach of study had been adopted by Gurley and Shaw (1955,

1960), and Goldsmith (1969), who suggested an evolutionary path of financial

systems and they show that as the economies develop the process can be

summarised: the self- finance process leads to a structure of intermediate debt

financing, and later to the emergence of incipient capital markets. According to

that, the study of Boot and Thakor (1996) described that at an initial phase the

financial system tend to be bank-based, and as long as it evolves the capital

market weight is progressively increasing. For instance, Boyd and Smith (1998)

and Demirgüc-Kunt and Levine (1999), found evidence of a trend of financial

systems to become more market oriented, as they become richer, while Rajan

and Zingales (1998) argue that bank-based systems are better at promoting

growth in countries with poor legal systems, while market-based systems have

advantages as legal systems improve. Indeed, they study the mechanisms

through financial development may influence economic growth and to deal

rigorously with causality issues, arguing that better-developed financial systems

ameliorate market frictions that make it difficult for firms to obtain external

finance.

If we analyse the main factors driving financial intermediation as the previous of

step in understanding the link between financial development and economic

growth, we need to refer to Rother (1999) that classify them into two main

groups: from the supply and the demand side. However, the empirical evidence

reveals that a limited number of variables possess a significant impact on

financial intermediation; nonperforming loans, structure of the market for

financial intermediation and the expected inflation. One of the more recent

studies on the topic by Demirgüc-Kunt and Levine (2008) emphasised the role

of historical determinants (i.e. the legal system origin and religion among

others), the political and macroeconomic stability, the information structure, the

regulation and supervision framework, the degree of contestability and

effciciency, the proportion of government ownership of financial intermediaries,

and the degree of access to financials services

3. DATA AND TESTING STRATEGIES Studies like Demirgüc-Kunt and Levine (1999), in order to analyse financial

structure, classified countries as either market-based or bank-based by

constructing a conglomerate index of financial structure based on measures of

size, activity and efficiency. Then, countries with larger ratios are classified as

bank-based while countries where the conglomerate ratio of banking sector

development to stock market development is below the mean are classified as

market-based. In addition, the study considered a third group of countries with

highly underdeveloped financial systems (if it has below median values of both

bank and market development). Therefore, this grouping system produces two

categories of countries: bank-based and market-based. However in this paper

we use cluster analysis technique. It allow us to specify different groups of

countries in terms of financial structure.

The two main data sources used in this paper are provided by European

Central Bank (report on EU Banking Structure) and by Eurostat. As a

complementary source, we have also used the Annual Reports of the EU New

Member States provided by the Central Banks of each of these countries. The

data cover EU-25 countries, consisting of EU-15 countries and EU-10 new

members states. The data refers to the period 1999-2007. The total number of

observations are 225.

The analysis will proceed as follow. First of all, we will identify the countries

according to one of the two financial structure (bank-based structure and

market-based structure). To do so, we propose a cluster analysis. In the second

place, we will estimate the main determinants for financial development for each

of the cluster groups obtained using panel data techniques. Consequently, in

the third place, we will apply the estimated coefficients to the new EU-10

members states in order to determine the benchmark for the efficient structure

of their banking system. Finally, we will compare these benchmark to the actual

data in each new EU member to measure their relative inefficiency and

appraise their banking system converge across time.

It is important to remark that we have balanced panel data. In comparison to

purely cross-country approaches, the panel approach has three important

advantages and one particular disadvantage. The first benefit from moving to a

panel is the ability to exploit the time-series and cross sectional variation in the

data. A second benefit is that panel data techniques does not include the

unobserved country-specific effect as part of the error term, and therefore

coefficient estimates are not biased1. The third benefit from using a panel is that

the panel estimator uses instruments based on previous realizations of the

explanatory variables to consider the potential endogeneity of the other

regressors.

An important disadvantage from to moving to panel data is that it employs data

averaged over nine-year periods. Therefore, the panel methods may be less

precise in assessing the finance growth relationship than methods based on

lower frequency data.

To estimated the main for financial development for each of the cluster groups,

an empirical form has to provide to the financial structure of each country. Thus,

we specify the financial structure per country in each year as a function of

observed and unobserved characteristics given by

vCXy +η+β=

where y=[y11…y1T….yN1… yNT] is a vector NT*1 (N=1…15, T=1…9) and

represents the financial structure, X=[x11…x1T….xN1… xNT] is a matrix NT*k (k

number of variables) and includes the set of variables that explain the financial

structure of the countries, C=IN ι⊗ ., ι is a vector of ones and η is the

unobservable individual effect. β is the parameters to be estimates and v is the

error term.

Once the β parameters are estimated, we replace them with their estimates in

the last function considering to the new EU-10 members states in order to

determine the benchmark for the efficient structure of their banking system.

Then, we compare these benchmark to the actual data in each new EU member

to measure their relative inefficiency and appraise their banking system

converge across time.

1 To control for the presence of unobserved country-specific effects, Arellano and Bond (1991) propose to first-difference the regression equation to eliminate the country-specific effect and then use instrumental variables to control for endogeneity. This approach eliminates biases due to country-specific omitted variables.

In what follows, we specify the definitions of the alternative dependent variables

and the explanatory variables that constitute the vector X, of equation (**)

(Table A1 summarizes the variables and gives some statistics). The dependent

variables are the following:

Number of credit institutions (CIs) per country.

Number of branches of CIs per country.

Number of employees of CIs per country.

Total assets of CIs per country.

Vector Y consists of the following variables:

population: De jure population of each country.

density: population density, measured by the number of inhabitants per

square kilometre.

GDP: Gross Domestic Product at constant prices.

4. EMPIRICAL RESULTS 4.1. Cluster analysis Now we apply cluster analysis2 to identify countries with similar financial

structure. We carry out this analysis for the EU-15 countries for the year 2004.

This is the year when the ten new member states joined the EU.

As we noted above, we are considering heterogeneous countries with different

financial structures (i.e., United Kingdom and Germany). The levels of banking

intermediation and levels of stock market capitalisation will be used to group the

countries that presents the highly similar financial structures.

The method we will use to form clusters is the agglomerative hierarchical

clustering3, using as criteria the average linkage between groups method4 and

2 See Chatfield and Collins (1980) and Aldenderfer and Blashfield (1984). 3 See the chapter on cluster analysis in STATA 10.0 manual. 4 The average linkage between groups method calculates the distance between two clusters as the average of the distances between all the pairs of cases in which one member of the pair is from each of the clusters.

the centroid method5. In both methods, the distance between cases is

measured by means of the squared Euclidean distance. We choose these

methods because they can be said ‘to maintain the nature of the original

space’6 and use more information than other criteria to form the clusters.

Therefore, two agglomerative hierarchical clustering methods will be applied to

the matrix of squared Euclidean distances among countries in a two-

dimensional space defined by the following variables: 1) the ratio of total assets

of credit institutions (henceforth CIs7) to GDP, 2) the ratio of stock market

capitalization to GDP. A way of visualizing the results of the clusters analysis is

the dendogram. More details on the cluster analysis are given in Appendix B.

Next, we apply this analysis to EU-15 countries. It is important to remark that we

have dropped Luxembourg from the sample due to the fact that its considerable

taxes advantages leads to a high size of banking sector jointly with a low GDP.

The rate obtained it can not be compared with the banking sector of the rest of

countries. We obtain the dendogram using the average linkage between groups

method. Figure 1 shows the dendogram. The dendogram obtained with the

centroid method is very similar, but it is not reported due to space limitations.

Both methods confirm that natural groupings exist.

FIGURE 1 ABOUT HERE

The nested tree structure of the dendogram suggests that there are many

different possible groupings, and the issue is where to ‘cut’ the tree so the most

reasonable number of groups is found. Unfortunately this question is still

unsolved for cluster analysis, although there are some tests8. From the point of

view of the present discussion, we have used the Duda & Hart index, which

were singled out as one of the best rule in determining the number of clusters.9

Table 1 reports the results of the Duda & Hart index.

TABLE 1 ABOUT HERE

5 The centroid method calculates the distance between two clusters as the distance between the means of the variables. 6 See Aldenderfer and Blashfield (1984). 7 CIs are banks, savings banks and loan undertakings (cooperative banks). 8 See Aldenderfer and Blashfield (1984), Everitt, Landau and Leese (2001), Gordon (2000) and Milligan and Cooper (1985) 9 See the chapter on cluster analysis in STATA 10 manual and Duda and Hart (1973).

The index indicates that the third-group solution is the most distinct from this

hierarchical cluster analysis. Therefore, the number of cluster is set as 3. One of

them are formed by 11 countries (Belgium, Denmark, Germany, Greece, Spain,

France, Italy, Austria, Portugal, Finland, Sweden), the second cluster is formed

by 2 countries (United Kingdom and Netherlands) and the third by 1 country

(Ireland). From this analysis we can argue that there are different financial

structure among countries: 11 countries present a bank-based structure, while 3

countries presents a market-based structure with different level of banking and

stock exchange capitalisation. Within this last group, United Kingdom and

Netherlands have similar financial structure.

REFERENCES

Arestis, P.; Luintel, A.D. and Luintel,K.B. (2004), Does Financial Structure

Matter?, Working Paper nº 399, Levy Economics Institute, p. 1-30.

Bardhan, A.D. y Edelstein, R.H. (2006), “Financiación de la vivienda en Rusia,

India y China: benchmarking, comparación, contraste”, Papeles de

Economía Española, nº 109, p. 27-42

Beck, T.; Demirguc-Kunt, A.; Levine, R. y Maksimovic, V. (2000), Financial

structure and economic development: Firm, industry, and country evidence,

Working Paper nº 2423, World Bank Policy Research.

Beck, T.; Levine, R. and Loayza, N. (1999), “Financial intermediation and

growth: Causality and causes”, Working paper nº 56, Banco Central de

Chile.

Beck, T.; Levine, R. and Loayza, N. (1999), “Finance and the sources of

growth”, Journal of financial economics, vol. 58, nº 1-2, p. 261-300.

Bethelemy, J.C. and Varoudakis, A. (1997), Economic growth,convergence

clubs, and the role of financial development, Oxford Economic Papers, nº

48, p. 300-328.

Boot, A.W and Thakor, A.V. (1996), Financial System Architecture, Mimeo,

Indiana Universtiy.

Boyd, J.H.; Levine, R. y Smith, B.D. (2001), “The impact of inflation on financial

sector performance”, Journal of Monetary Economics, nº 47, p: 221-248.

Carbo Valverde, S. (1998), "El Papel Del Sistema Financiero en la Economía

Real", Perspectivas del Sistema Financiero, nº 63-64, p:61-70.

Carroll, C.D. y Weil, N.D. (1994), “Saving and Growth: A Reinterpretation”,

Caenegie- Rochester Conference Series on Public Policy, nº. 40, p: 133-

192.

Choe, Ch y Moosa, I.A. (1999), “Financial System and Economic Growth: The

Korean Experience”, World Development, Vol 27, nº. 6, p: 1069-1082

Cuadro, L.; Gallego, S. y García-Herrero, A. (2002), "Why Do Countries

Develop More Financially than Others: The Role of the Central Bank and

Financial Supervision", Proyecto de Investigación del Banco de España,

Septiembre.

Cuesta, M. y García.Verdugo, J. (2001), Proyecto: Sistema Financiero y

Desarrollo Regional: Un Análisis para Fundamentar la Influencia de la

Banca Regional en el Desarrollo de las Regiones.

Cuesta, M. y García.Verdugo, J. (2001), “Contribución del sistema bancario al

crecimiento de las regiones españolas”, Perspectivas del Sistema

Financiero, nº 73, p. 87-98.

Cuesta, M. y García.Verdugo, J. (2002), “Efectos del sistema financiero en las

disparidades regionales. Aplicación de un modelo de desarrollo regional

postkeynesiano al caso europeo”, Perspectivas del Sistema Financiero, nº

75, p.39-52.

Delgado, B. y Liñares, J.M. (2006), “Indicadores de la arquitectura financiera,

ambiente institucional y legal de los sistemas financieros: Una perspectiva

mundial”, Papeles de Economía Española, nº 110, p. 290-325.

Demetriades, P.O. y Luintel, K.B. (1996). “Financial Development, Economic

Growth and Banking Sector Controls: Evidence from India”, The Economic

Journal, nº 106, Marzo, p: 359-374.

Demirgüc-Kunt, A. and Levine, R. (1996), “Stock market development and

financial intermediaries: Stylyzed facts”, The Workd Bank Economic Review,

nº 10, vol. 2, p. 291-321.

⎯ (1999), Bank-Based and Market-Based Financial Systems: Cross-Country

Comparisons, Policy Research Working Paper Series, The World Bank, nº

2143, p. 1-72.

⎯ (2008), Finance, Financial Sector Policies, and Long-Run Growth, Policy

Research Working Paper, nº 4469, p- 1-82.

Demirgüc, Kunt, A. and Maksinovic, V. (1998), “Law, finance and firm growth”,

Journal of Finance, vol. 53, nº 6, p. 2107-2137.

Dermine, J. (2005), “European Banking Integration: Don’t Put the Cart before

the Horse”, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=824704

Dinger, V. (2002), “The banking sector of the EU accession candidate countries

from Central and Eastern Eruope: Size and Development”, ZEI Center for

European Integration Studies, University of Bonn.

Fohlin, C.M. (2000), Economic, Political and Legal Factors in Financial System

Development: International Patterns in Historical Perspective, Social

Science Working Paper, nº 1089, p. 1-37.

Galbis, V. (1977), “Financial intermediation and economic growth in less

developed countries: A theoretical approach”, Journal of Development

Studies, nº 13, p. 58-72.

Gallego, S.; Herrero, A. y Saurina, J. (2002). "The Asian and European Banking

Systems: The Case of Spain in the Quest for Development and Stability",

Proyecto de Investigación Kobe, Julio.

Gallego, S. y Herrero, A. (2006). “El desarrollo de los sistemas financieros en

América Latina en perspectiva”, Papeles de Economía Española, nº 110, p.

103-115.

García-Herreo, A.; Santillán, J.; Gallego, S.; Cuadro, L. y Egea, C. (2002).

“Latin America Financial Development in Perspective”, Presentación En El

Seminario De Eurosystems And Latin American Central Banks.

Goldsmith, R.W. (1969), Financial Structure an Development, Yale University

Press.

Greenwood, J. and Jovanovic, B. (1990), “Financial development, growth, and

the distribution of income”, Journal of Political Economy, nº 98, p. 1076-

1107.

Gurley, J.G. and Shaw, E.S. (1955)”Financial aspects of economic

development”, American Economic Review, nº 45, p. 515-538.

Gurley, J.G. and Shaw, E.S. (1960) Money in a Theory of Finance, Brooking

Institution, Washington D.C.

Hicks, J. (1969), A Theory of Economic History,Clarendon Press, Oxford.

Jaffee, D. y Levonian, M. (2001), “The structure of banking systems in

developed and transition economies”, European Financial Management, vol.

7, nº 2, p: 161-181.

Kapur, B.K. (1976), “Alternative stabilization policies for less developed

countries”, Journal of Political Economy, nº 84, p. 777-795.

King, R. and Levine, R. (1992), Financial Indicators and Growth in a Cross

Country Section of Countries, World Bank Working Paper, nº 819, p. 52.

⎯ (1993), “Finance and growth: Schumpeter might be right”, Quaterly Journal

of Economics, vol. 108, nº 3, p. 717-738.

Kuznets, S. (1955), “Economic growth and income inequality”, American

Economic Review, nº 45, p. 1-28.

Levine, R. (1997), “Financial development and economic growth: Views and

agenda”, Journal of Economic Literature, nº 35, vol. 2, p. 688-726.

⎯ (1998), “The Legal Environment, Banks, and Long-Run Economic Growth”,

Journal of Money, Credit, and Banking, nº 30, p. 596-613.

⎯ (1999), “Law, Finance, and Economic Growth”, Journal of Financial

Intermediation, nº 8, p. 36-67.

⎯ (2002), Bank-Based or Market-Based Financial Systems: Which is Better?,

William Davidson Working Paper Number 442, p. 1-45.

Levine, R.; Loayza, N. y Beck, T. (2000), “Financial intermediation and growth:

Causality and causes”, Journal of Monetary Economics, nº 46, p: 31-77.

Levine, R. and Zervos, S. (1996), “Stock market development and long-run

growth”, Policy Research Working Paper, nº 1582, p. 1-32.

Lewis, W.A. (1955), The Theory of Economic Growth, George allen and Unwin,

London.

Luintel, K.B. y Khan, M. (1999). “A Quantitative Reassessment of The Finance-

Growth Nexus: Evidence from a Multivariate Var”, Journal of Development

Economics, nº 60, p: 381- 405.

Luintel, K.B.; Khan, M.; Arestis, P. y Theodoridis, K. (2008). Financial Structure

and Economic Growth, Cardiff Economics Working Papers, E2008/3, p. 43.

Masciandro, D. (2005), Financial Intermediation in the New Europe, Edward

Elgar, Northampton.

McKinnon, R.I. (1973), Money and Capital in Economic Development, Brooking

Institution, Washington D.C.

Milligan, G. W., and M. C. Cooper. 1984. An examination of procedures for

determining the number of clusters in a dataset. Psychometrika nº 50,

p.159-179.

Nguyen, N. y Williams, J. (2006). “El sistema financiero en Asia y Australia”,

Papeles de Economía Española, nº 110, p. 116-144.

Pagano, M. (1993), “Financial markets and growth: An overview”, European

Economic Review, nº 37, p. 613-622.

Piloková, A. y Pätoprstý, V. (2004), “Deepining financial intermediation in the

new Europe: The role of banks”, en Financial Intermediation in the New

Europe, editado por Masciandaro. D., Edward Elgar, Cheltenham, Reino

Unido.

Rajan, R.G. and Zingales, L. (1998), “Financial dependence and growth”,

American Economic Review, vol. 88, nº 3, p. 559-586.

Riess, A.; Wagenvoort, R. and Zajc, P. (2002), “Practice makes perfect. A

review of banking in Central and Eastern Europe”, EIB Papers, vol 7, nº 1, p.

31-53.

Robinson, J. (1952), The Rate of Interest and Other Essays, Ms Millan, London.

Rother, P.C. (1999), Explaining the behavior of financial intermediation:

Evidence from transition economies, IMF Working Paper, n1 99/36, p. 32

Shaw, E.S. (1973), Financial Deepening in Economic Development, Oxford

University Press, New York.

Solow, R. (1998a), "Las Fuentes del Crecimiento y el Papel de las Finanzas",

Perspectivas del Sistema Financiero, nº 63-64, p:1-8.

Schumpeter (1911), The Theory of Economic Development: An inquiry into

profits, capital, credit, interest and the business cycle, Springer USA.

Thakor, A.V. (1996), “The design of financial systems: An overview”, Journal of

Banking and Finance, nº 20, p. 917-948.

Von Hagen, J.y Dinger, V. (2005), Banking Sector (under?) development in

Central and Eastern Europe, Working Paper B06, ZEI Centre for European

Integration Studies, p.58.

Wooldridge, J.M. (2006), “Cluster-sample methods in applied econometrics: An extended análisis”, https://www.msu.edu/~ec/faculty/wooldridge/current%20research/clus1aea.pdf

APPENDIX A: DATA DESCRIPTION

The two main data sources used in this paper are provided by European

Central Bank (report on EU Banking Structure) and by Eurostat. As a

complementary source, we have also used the Annual Reports of the EU New

Member States provided by the Central Banks of each of these countries. The

data cover EU-25 countries, consisting of EU-15 countries as Belgium,

Denmark, Germany, Ireland, Greece, Spain, France, Italy, Luxembourg,

Netherlands, Austria, Portugal, Finland, Sweden, United Kingdom, and EU-10

new members states as Czech Republic, Estonia, Cyprus, Latvia, Lithuania,

Hungary, Malta, Poland, Slovenia, Slovakia. The data covers to the period

1999-2007. The total number of observations are 225.

In what follows, we specify the definitions of the alternative dependent variables

and the explanatory variables that constitute the vector X, of equation (**)

(Table A1 summarizes the variables and gives some statistics).

The dependent variables are the following:

Number of credit institutions (CIs) per country.

Number of branches of CIs per country.

Number of employees of CIs per country.

Total assets of CIs per country.

Vector Y consists of the following variables:

population: De jure population of each country.

density: population density, measured by the number of inhabitants per

square kilometre.

GDP: Gross Domestic Product at constant prices.

TABLE A1 ABOUT HERE

APPENDIX B. CLUSTER ANALYSIS

‘Cluster Analysis’ is the generic name for a wide variety of procedures that can

be used to create a classification. The aim of these procedures is to form

‘clusters’ or groups of highly similar cases or countries. More formally, a

clustering method is a multivariate statistical procedure that allows us to

reorganize the sample of countries into homogeneous groups in terms of some

characteristics10. For example, cluster analysis is used to classify animals or

plants in biology, and to identify diseases and their stages in medicine.

In cluster analysis, distance is a generic measure of how far apart two objects

fall. There are many different definitions of distance. The choice between the

measures depends on which characteristics of the data are important for the

particular application. The widely used distance measure between two countries

is the squared Euclidean distance, computed from the vectors of values of their

characteristics.

In cluster analysis, the selection of variables determines the characteristics that

will be used to identify subgroups. In this paper, we apply this analysis for the

identification of countries with similar financial structure defined in terms of the

ratio of total assets of CIs to GDP and the ratio of stock market capitalization to

GDP. Therefore, distance measures the relative closeness of groups of

countries.

There are many methods for forming clusters. The most applied are the

agglomerative hierarchical clustering, and the divisive hierarchical clustering11.

In agglomerative hierarchical clustering, the clusters are formed by grouping

cases, starting with groups of just one country and ending up with all countries

gathered into a single group. In divisive hierarchical clustering, the clusters are

formed by splitting clusters, starting with all countries gathered into a single

group and ending up with as many groups as there are countries.

10 See Aldenderfer and Blashfield (1984). 11 See, for example, the chapter on cluster analysis in STATA 10 manual.

Under agglomerative hierarchical clustering, there are many criteria for deciding

which clusters should be combined at each step, but these criteria are invariably

based on a matrix of distances. They differ in how the distances between

clusters at successive stages are estimated. In general, clustering methods are

the following: linkage methods (e.g., the average linkage between groups

method that we employ in this study), error sums of squares or variance

methods, and centroid methods (we have also employed this method to check

robustness).

As long as there are many methods for calculating distances and for combining

objects into clusters, there are many ways of visualizing the results of cluster

analysis (e.g., icicle plot, agglomeration schedule, dendogram). In this study, we

employ the dendogram that shows the clusters being combined, and the actual

distances rescaled to numbers between 0 and 25.

FIGURE 1. Dendogram using average linkage

TABLE 1. Duda & Hart Index

10 0.1505 5.64 9 0.1982 4.05 8 0.4706 2.25 7 0.4110 5.73 6 0.1993 12.05 5 0.0000 . 4 0.3147 19.60 3 0.5529 8.89 2 0.5077 11.64 1 0.0316 398.07 clusters Je(2)/Je(1) T-squared Number of pseudo Duda/Hart