Productivity Change, Consolidation, and

Privatization in Italian and German Banking

Markets∗

A. De Vincenzo†, E. Fiorentino‡, F. Heid§, A. Karmann¶and M. Koetter‖

Abstract

We compare total factor productivity (TFP) changes of the Italian and Ger-man banking systems and relate them to both privatization and consolidationdevelopments between 1994 and 2004. While many studies concentrate onlyon e�ciency measures, we estimate the di�erent components of TFP changes(economies of scale, technological progress, and cost e�ciency) with stochasticfrontier analysis (SFA). We use balance sheet and pro�t and loss account datareported to the Banca d'Italia and the Bundesbank, respectively, and �nd thatTFP improvements in both countries are mainly due to technological progress.Cost e�ciency changes play a marginal role and Italian and German banksseem to face diseconomies of scale. Moreover, we estimate the impact of theprivatization and consolidation processes on TFP changes. Our results showthat, �rst, mergers and acquisitions per se have not in�uenced TFP changeseither in Italy or Germany and, second, that the Italian privatization processclearly contributed to a positive evolution of TFP. We believe this result canbe useful to better clarify costs and bene�ts of alternative development pathsof banking systems.

Keywords: Banking market integration, Deregulation, Total Factor Produc-tivity, Italy, Germany.

∗This paper represents the authors' personal opinions and does neither re�ect the views of theDeutsche Bundesbank nor the Banca d'Italia.

†Banca d'Italia, Banking and Financial Supervision, Roma, Italy,alessio.devincenzo@bancaditalia.it

‡Dresden University of Technology, Faculty of Business Management and Economics Chairfor Economics, esp. Monetary Economics, Münchner Platz 1/3, 01062 Dresden, Germany,Elisabetta.Fiorentino@mailbox.tu-dresden.de

§Deutsche Bundesbank, Financial Market and Banking Supervision Department, Wilhelm-Epstein-Str. 14, 60431 Frankfurt am Main, Germany, Frank.Heid@bundesbank.de

¶Dresden University of Technology, Faculty of Business Management and Economics Chairfor Economics, esp. Monetary Economics, Münchner Platz 1/3, 01062 Dresden, Germany,gkw@mailbox.tu-dresden.de

‖Deutsche Bundesbank and University of Groningen, Faculty of Economics, P.O. Box 800, 9700AV Groningen, The Netherlands, m.koetter@rug.nl

1

1 Introduction

Banking industries throughout the world changed dramatically during the last twodecades. Both in Europe [Berger and Mester, 2003] and the U.S. [Berger, 2003]technical progress paired with deregulation exposed banks to increased competi-tive pressure [Amel et al., 2004]. In fact, increasing competition is one of the mainobjectives of creating a single European market for �nancial services. Enhancedcompetitive conditions are expected to improve the e�ciency of providing �nancialservices and, thus, to bene�t consumers.

Two of the largest banking systems in the European Union share similar experi-ences in the wake of ongoing �nancial integration: Italy and Germany. Both bankingsystems witnessed unprecedented bank merger waves during the 1990's [Berger et al.,1999]. The OECD [2000] attributes this consolidation trend in both banking mar-kets to ongoing technical progress and increased competition that forces banks tocontinuously optimize their operations and performance, i.e. their e�ciency.

At the same time, both of these banking systems juxtapose each other in termsof their respective approaches to (de)regulation. Germany is still the role modelof a three pillar banking system with a considerable share of state owning savingsbanks. In fact, all of the observed consolidation occurred within each pillar only. Incontrast, Italy decided in the late 1980's to privatize its public banking sector. Thesubsequent three-step process was laid down in a series of laws: the Amato law of1990, the Dini law of 1994 and the Ciampi law of 1999.

In this paper we investigate which consequences these fundamentally di�er-ent strategies, maintenance of the public presence versus privatization, implied forbank's total factor productivity in Italy and Germany. We employ a unique datasetprovided by the central banks of Italy and Germany, respectively, and estimatethree di�erent components of total factor productivity (TFP) changes: (i) e�ciencychanges, (ii) scale economy changes and (iii) technical changes. Numerous studiesanalyze individual components for both banking markets.1 But only few Europeanstudies exist that seek to explain the tripartite decomposition of bank's e�orts toimprove TFP.

The second contribution of our paper is to explain productivity change di�eren-tials in the two countries by relating them to the two di�erent regulation strategiespursued in Italy and Germany. We hypothesize that TFP (components) of privatizedversus non-privatized banks on the one hand and of German versus Italian banks onthe other are signi�cantly di�erent. For example, in the immediate aftermath of aprivatization the e�ciency of an Italian bank may deteriorate. But we expect it torecover above the average of other banks once a former state bank got accustomedto market habits. We structure the remainder of this paper as follows. In section2 we compare both industries in terms of simple structural and performance indi-cators. We review the relevant bank productivity literature in section 3. In section4 we introduce our two-stage methodology. First, we discuss how to estimate totalfactor productivity changes from industry cost functions and how to subsequentlydisentangle three TFP change components: e�ciency, scale and technical changes.

1For example, Lang and Welzel (1996, 1998), Altunbas et al. [2001], Koetter [2005], Maudoset al. [2002] or Casu et al. [2004].

2

Second, we regress measures of privatization and consolidation on TFP changes toexplain di�erences in productivity growth between the two banking systems. Afterpresenting our data in section 5, we discuss our results in section 6. We concludewith some �nal thoughts in section 7.

2 The German and the Italian Banking Systems

2.1 Italy

At the beginning of the 1990's the structure of the Italian banking system was highlyfragmented, with a large number of relatively small banks and a signi�cant presenceof the State in the industry. 2

In 1990 there were 1,064 banks (with 17,721 banking o�ces) operating in theItalian banking sector.3 At the time almost 70 percent of the banking system's totalassets were in fact under public control if special credit institutions, which werepredominantly publicly owned or a�liated with public banks, are included. Directlyor indirectly publicly owned banks comprised six public-sector banks ("istituti dicredito di diritto pubblico"), three banks of national interest ("banche di interessenazionale") and 84 among savings banks ("casse di risparmio") and institutions spe-cialized in credit against collateral ("monti di credito su pegno"). In addition to thepublic banks there were also 106 private commercial banks ("banche di credito ordi-nario"), 108 cooperative banks ("banche popolari"), 715 mutual banks ("casse ruralied artigiane"), 37 branches of foreign banks and 5 group-speci�c central institutions(see Table 5 in the appendix).

At the beginning of the nineties the regional spread and business activities ofbanks were strictly regulated and there were no universal banks. One important prin-ciple of the 1936 legislation, still in force, was the mandatory business specialization.It implied the classi�cation of institutions as commercial banks or as special creditinstitutions. The former specialized in the short-term business, i.e. shorter than 18months. The latter operated in the medium- and long-term business and specializedin one particular sector, such as agriculture, building, public works, industry, orMezzogiorno [Carletti et al., 2005].

This structure was radically altered in the course of the 1990's. The objectivesof e�ciency, performance and internationalization replaced the old goals of sup-porting the development of certain industries, sectors and economic regions. Twomain regulatory changes heavily in�uenced the reshaping of the banking system:the law reforming public banks and the implementation of the Second BankingDirective (89/646/EEC). The �rst accelerated the privatization and consolidationprocess of the banking industry. The second removed substantial barriers to entry

2The presence of the State in the banking system traces back to the creation of IRI ("Istitutoper la Ricostruzione Industriale") after the Great Depression. It was a publicly-owned holdingcompany controlling the three largest private banks Banca Commerciale Italiana, Credito Italiano,and Banca di Roma.

3Together with the banks (called "aziende di credito"), which operated in the short-term busi-ness, there were also about 90 special credit institutions (called "istituti di credito speciale")operating in the medium- and long-term business, some of them a�liated with banks.

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and allowed banks to perform a wider range of �nancial activities [De Vincenzo andMarullo Reedtz, 2005].

The privatization of Italian public banks took place mainly in three steps between1990 and 1999. The beginning of the process coincides with the passing of Law No.218 of 1990 (the Amato-Carli Law: �The restructuring and integration of the equityof public sector banks�). It permitted the transformation of public banks into joint-stock companies and therefore the free transfer of the relative ownership rights.From this change two separate entities emerged: a "foundation", representing theoriginal legal entity, and a "stock corporation" (the bank), entrusted with the task ofconducting banking business. The foundation, which conferred its banking divisionto the stock corporation, held the stock. A second important move was the directiveof Treasury Minister Dini in 1994. This regulation introduced tax incentives forfoundations shedding their bank shareholdings over the successive �ve years (1994-1999), stating also the timeframe and thresholds that foundations had to respectwith regards to their stakes in banks. The �nal step of the privatization processderived from the regulation put forward by Treasury Minister Ciampi in 1999. Onceagain, this act favored the divestment of conferred companies (the banks) through�scal incentives to the advantage of the conferring bodies (the foundations) [Ruoziand Anderloni, 1999].

Together with the reform of the ownership structure of public banks, a set ofother important reforms took place in the 1990's. Several provisions of the Italian�nancial laws were substantially changed compared to the previous decade. The newConsolidated Law on Banking of 1993 �nally replaced the legislation dating back to1936. Most restrictions introduced in the thirties were removed. The limit to regionalexpansion of saving banks, the portfolio requirement to hold government bonds andthe ceiling on credit to the private sector were already abolished at the end of the1980's. The mandatory specialization was gradually removed after 1990 and theuniversal bank model was �nally introduced with the new 1993 law. It allowed thebanks to raise funds in any form and to undertake any activities indicated in theSecond Banking Directive, such as factoring, leasing, medium- and long-term credit,and merchant banking. The limit to geographical diversi�cation for all special creditinstitutions was lifted. The notion of banking group was introduced in the legislation.

The Italian banking reforms of the previous decade substantially changed theentire nature of the banking system. As of 2005 the number of banks operating inItaly had dropped by 26.3% per cent to 784. At the same time, the liberalizationof branching saw the number of bank outlets jump: at the end of 2005 there were31,501 branches, an increase of around 78 per cent compared with the end of 1990.As a consequence, the availability of banking service improved substantially. Since1993 banking groups have also been created; as of 2005 their assets represent around90 percent of the total for the banking system. The share of total assets controlledby public banks has dramatically decreased, from 70 to around 9 percent.

Since the mid-1990's the average size of banks increased substantially, both atthe individual and the group level. Between 1994 and 2004 the average size of banksmore than doubled. The same happened for the largest banking groups (Table 7 inthe appendix). This trend is mainly the result of a process of mergers and acquisi-tions among banks which, measured in terms of the number of institutions involved,reached its peak in the course of the nineties. Between 1990 and 2004 a total of

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620 mergers were recorded, involving target banks accounting for 51 per cent of theassets of the entire banking system at the end of 1989. Put di�erently, in terms ofbanks' total assets more than half of the Italian banking system was a�ected by thewave of mergers and acquisitions during the considered period.

These developments were accompanied by a clear improvement in Italian banks'pro�tability indicators. At the beginning of the 1990's pro�tability was mainly drivenby traditional intermediation business, also in connection with an overall economicenvironment characterized by high in�ation and nominal interest rates. In the yearsbefore the launch of the monetary union (i.e. in the second half of the decade) Ital-ian in�ation and interest rates converged towards the level prevailing in the othermember countries. As a result, banks' revenues from standard intermediation activ-ity signi�cantly decreased. Banks' reaction, stimulated also by increasing internaland external competition, was to enlarge and diversify revenue sources.4 This wasachieved mainly by expanding the range of �nancial services provided to both cor-porate and retail customers. In the mean time, banks put particular e�ort into con-trolling expenses (especially sta� costs) and boosting productive e�ciency. Indeed,the substantial completion of the privatization process undertaken at the beginningof the decade had signi�cantly increased the discipline exerted by the market onbank managers and directors.

These trends can be easily detected by considering the evolution of some of themain bank pro�tability indicators in the period 1990-2004 (Table 7 in the appendix).As a ratio to total assets, net-interest income declined from 2.5 to 2.2 percent whilenon-interest income increased from 0.9 to 1.4 percent. This means that the share oftotal revenues originating from non-traditional (intermediation) business rose from27 to 39 percent. Operating expenses decreased from 2.4 to 2.1 percent of total assetsand the share of sta� costs diminished from 47 to 38 percent. The cost-income ratio,often used as an indicator of accounting cost e�ciency, signi�cantly improved from70 to 58 percent. The return on equity (ROE) increased from 1.1 to 10.7 percent,after having reached a peak of more than 13 percent in 2000. All these trends are,to some extent, ampli�ed when one looks at the performance of the largest bankinggroups, the ones that were privatized during the �rst half of the nineties.

2.2 Germany

Banking regulation in Germany dates back to 1931 when licensing requirements andbank supervision were �rst introduced on a general scale and a supervisory authoritywas set up. Already then the fundamental structure of the German banking system,a mix of private, public, and cooperative institutions, existed. In contrast to Italy,the German banking system did not experience fundamental deregulation measuresduring the 1990's. Despite the consolidation process no substantial changes regardingsupervisory and competition regulation took place [?].

The banking industry in Germany is composed of a variety of public and privatecredit institutions. In principle, the activities of credit institutions are not limited

4The fact that despite ongoing consolidation the average number of banks doing business in eachlocal market ("province") rose from 28 in 1989 to 35 in 2004 bears witness to increased internalcompetition.

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and this universal banking system is structured along three pillars: commercial,savings and cooperative banks. Commercial banks (Geschäftsbanken) comprise allprivate universal banks. They are the most inhomogeneous of all the groups andinclude �ve big German universal banks (Commerzbank, Deutsche Bank, Dresd-ner Bank, Hypovereinsbank, Postbank), regional banks, other credit institutionsand branches of foreign banks. Besides the legal form of a public limited company(Aktiengesellschaft), they are characterized by their national and international ori-entation. The savings bank pillar comprises regional savings banks (Sparkassen) andtheir central institutions (Landesbanken). All savings banks, apart from a few freesavings banks, are public-law institutions 5. An important feature of savings banksis the regional principle, which generally limits their business activities to a spe-ci�c economic region. Central savings banks are the community bodies of regionalsavings banks. Unlike the latter, central savings are not subject to any business re-strictions and the regional principle applies only to a limited extent. The third pillarof cooperative banks includes industrial and agricultural credit cooperatives (Volks-banken and Rai�eisenbanken) and their central institutions (GenossenschaftlicheZentralbanken). The cooperative banks are the most numerous of all groups. Theirdistinctive feature is each member's legal right to own an equal share of total assets.As universal banks they o�er all the standard �nancial services but play no role ininternational lending. These institutions are characterized by their small operatingsize.

In 1990 there were 341 commercial banks with total assets of 1,409 billions of euro(Table 7 in the appendix). Of these banks, 60 were subsidiaries or branches of foreignbanks. In addition, there were 771 savings banks organized under public law bymunicipal, regional, or state authorities, and 11 central institutions (Landesbanken).Germany's banking system also included 3,392 industrial and agricultural creditcooperatives, and allied institutions and their four central institutions. All of theseinstitutions were universal banks. Besides these universal banking institutions, therewere also 70 specialized credit institutions such as mortgage banks, ship mortgagebanks, building and loan associations and investment companies.

In 2005 the structure of the banking system with respect to the three pillartaxonomy did not change. However, the number of institution and their asset sharesdi�ered dramatically. The banking system witnessed an unprecedented bank mergerwave during the 1990's. The number of banks operating in Germany had droppedfrom 4,589 in 1990 to 2,147 units in 2005. As a result the average size of banksincreased by almost 60% during the same period. The asset share of commercialand saving banks experienced a decrease of about 5% while central savings andbranches of foreign banks increased their asset share by about 7%.

Idiosyncratic factors rather than regulatory changes in�uenced the evolution ofthe German banking sector in the 1990's [Carletti et al., 2005]. German reuni�ca-tion, the burst of a housing price bubble in East Germany after the constructionboom in the early 1990's, the introduction of money market funds in 1994 and thestock market crash around the turn of the century all represented shocks depress-ing the performance of German banks. Some interesting trends are exhibited bythe evolution of key performance indicators in the period 1994-2004 (Table 9 in

5Their public mandate is anchored in the savings bank law of the relevant federal state govern-ments

6

the appendix). In contrast to Italy, the pro�tability of the banking system dete-riorated: ROE and net-interest income (as a percentage of total asset) decreasedthroughout the 1990's and ROE even turned negative in 2003. The low pro�tabilityof German banks may result from intense competition [Hempell, 2004]. However,[Brunner et al., 2004] note that regional concentration ratios are higher comparedto federal averages. Since local savings and cooperatives operate on regionally con-�ned markets only, such regional concentration measures seem more appropriate toassess local competition. Hence, competition alone does not seem to explain poorperformance of German banks.

Low federal market concentration is most likely due to the fact, that almostthe half of the German bank market is dominated by banking groups that do notallow take-overs [Research, 2004]. Consolidation across pillars is more di�cult thanconsolidation within pillars. Regional and central savings in each state are governedby state-speci�c law and cannot be taken over by an institution of another pillarunless their legal status is changed, a process that requires majority support inthe state parliaments. The same holds for mergers between public sector banks ofdi�erent states [Brunner et al., 2004]. A further step in the consolidation processwould necessitate to turn savings banks into joint stock corporations governed byprivate law (privatization), such as the Postbank.

With this exception, however, no single public bank has been privatized in Ger-many since 1990. The number of publicly owned banks declines steadily solely dueto intra-pillar mergers rather than privatization and hence the approach followed byGerman banking markets is fundamentally di�erent from the Italian experience inthe 1990's. In this paper we are therefore interested in the bank productivity e�ectsof these two di�erent approaches, privatization versus maintaining the status quo,respectively.

3 Bank productivity studies

Most bank productivity studies use parametric techniques to estimate e�cient fron-tiers. Since many scholars consider it reasonable that even publicly owned banks aimto minimize cost when providing �nancial services, the estimation of cost frontiersis the most frequently encountered approach in the literature.

The study by Bhattacharyya et al. [1997] is particularly interesting. They ana-lyze total factor productivity growth for an unbalanced panel of privatized publicbanks in India between 1970 and 1992. They employ a translog cost system andassume that banks minimize cost when producing an output vector by demandinginput quantities at given prices. They specify an optimal cost function that allowsfor bank-speci�c �xed e�ects and heteroscedasticity across banks. In addition, theyuse Sheppard's Lemma [Shephard, 1970] to derive optimal input demand equations,which are also subject to bank-speci�c �xed e�ects. They use Iterative SeeminglyUnrelated Regression to estimate this system of a cost and factor demand equations.Following the value-added approach they specify �ve kinds of loans and deposits asoutputs: �xed deposits, savings deposits, current deposits, investments and loans.All outputs are measured in real monetary terms. In addition, they also include thenumber of branches as a convenience output, too. They consider labor and physical

7

capital as inputs of the production process and obtain input prices by dividing wagesover employees and annual capital expenditures are related to the stock of employedphysical capital. Finally, they include paid-in equity capital as a control variable.Qualitatively, their results are to a large degree in line with �ndings reported innon-parametric studies. In the initial aftermath of deregulating banking markets,productivity collapsed. However, it gradually exhibits improvements of 2 % on av-erage and peaks in the course of ongoing liberalization an impressive 7 % at the endof the observation period. Despite the advantage to enrich the analysis with an ac-count of random noise and a meaningful behavioral objective function, an importantmethodological caveat of this approach is mentioned only on the side. One of thekey assumptions of this estimation technique is error independence across share andcost equation. In fact, the presence of both allocative and technical ine�ciency inthe cost function, however, renders it likely that estimating a system yields inconsis-tent results since the technical component of ine�ciency should only be containedin the input demand functions. This phenomenon is known as the Greene problemin the e�ciency literature [Greene, 1993] and therefore most studies refrain from theestimation of systems but rather resort to estimating a single equation cost frontier.

Along these lines, Berger and Mester [1997] analyze productivity changes ofUS banks. For a large sample covering virtually all commercial banks in the US,they estimate cost, standard pro�t and alternative pro�t frontiers between 1984 and1995.6 They separate the total change in industry cost and pro�ts, respectively, intoproductivity growth and changing environmental conditions. The former equals theproportional change in cost (or pro�ts) given business conditions and is further sepa-rated into shifts of the frontier on the one hand (technical change) versus movementstowards the frontier on the other hand (e�ciency changes). In line with the interme-diation approach, they specify interest rates on purchased funds and core depositsplus labor cost as input factor prices. Regarding outputs, they specify consumerloans, business loans, real estate loans, and securities. In addition, they control forwhat Bhattacharyya et al. [1997] refer to as �xed netputs: o�-balance-sheet items,physical capital, and �nancial equity capital. In short, these factors are assumedintegral parts of bank production technology but impossible to alter in the shortrun. A major di�erence to earlier parametric and virtually all non-parametric stud-ies is the inclusion of explicit covariates to control for typical bank business traitssuch as the quality of loans or the risk associated with the asset portfolio. In total,Berger and Mester [1997] specify 14 controls in addition to production parametersand choose the translog functional form to obtain a reduced cost regression equation.The latter is estimated using the Distribution Free Approach (DFA) [Berger, 1993],which basically assumes that core e�ciency is time-invariant.7 Their results indicatefor the three respective components of cost changes, �rst, a modest decline in coste�ciency, second, cost improvements due to more favorable business conditions and,third, negative technological change both before and after 1990. On balance, costchanges have been very low due to these countervailing e�ects.

6The alternative pro�t approach allows for market power of banks in output markets by specify-ing banks to choose output prices given output quantities rather than vice versa as in the standardapproach assuming perfect competition [Humphrey and Pulley, 1997].

7Reported time intervals with constant core e�ciency are each six years long. However, itremains debatable whether there is something like a constant core ine�ciency and any choice ofan according time interval remains ultimately arbitrary.

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Stiroh [2000] examines scale economies, X-e�ciency and productivity change ofcosts and pro�ts for 661 bank holding companies (BHC) in the US between 1991 and1997. He particularly assess the sensitivity of results with respect to the inclusion ofnon-standard banking products and services, such as OBS and fee income.8 He �ndsthat especially productivity growth are to be held accountable for banks' success. Inturn, he also �nds that scale economies have been exploited before 1994 and opera-tional e�ciency persisted among this fairly homogenous sample of BHCs. The coremodel estimated speci�es a translog cost function with three input prices, namelythe price of purchased funds, the interest rate on core deposits and the price of labor.A reduced output vector contains business loans, private loans and securities. Stiroh[2000] subsequently expands the output vector for net non-interest income and OBS.In addition, he also speci�es some �xed netputs as controls; physical capital, equitycapital and, in some variation of the core model, also OBS. To estimate productivitychange, he uses three alternative methods. The �rst employs the whole sample andestimates a cost function, amended with time trends that interact with respectiveinput prices, as a pooled cross section with OLS. Hence, the assumption is that noine�ciency prevails and that all banks operate on the frontier. Productivity growth,then, is obtained as the sum of the partial derivatives with respect to time. His sec-ond approach follows Lang and Welzel [1996] and uses panel estimation techniquesthat allow for bank-speci�c e�ects. He hypothesizes that these bank-speci�c e�ectscapture any ine�ciency, which may di�er across banks, but leaves slope coe�cientsand thus time derivatives equal across banks. The panel is estimated as both a �xedand a random e�ects speci�cation and productivity growth, in turn, is determinedas previously via time derivatives. The third approach provides estimation of singleyear frontiers to decompose cost changes into higher cost due to less favorable eco-nomic conditions versus decreasing productivity. Since we are primarily interestedin this paper in measuring productivity, we won't discuss in greater details Stiroh'sapproach to measure scale economies and operational e�ciency. Note at this stageonly, that neither of his approaches incorporates explicitly the presence of ine�-ciency when obtaining parameter estimates required to derive productivity change.Thus, his results may very well con�ne productivity changes with changes in one ofthe latter two components.

In a more recent paper by Berger et al. [2003] extend their 1997 study to an-alyze productivity and e�ciency changes between 1984 and 1997. In contrast toStiroh [2000], they �nd that cost productivity worsened considerably during theearly 1990's. This is presumably due to the fact that their methodology to obtainproductivity changes distinguishes between changes in e�ciency and shifts of thefrontier.

In sum, to decompose cost changes most studies rely on the estimation of multipleannual frontiers to calculate (and separate) changes due to shifting parameters ofthe cost function and changing variables, either production variables or ine�ciency.In our view, this issue is problematic. Coelli et al. [1998], for example, carefully pointout that e�ciency estimated with SFA are relative measures. Comparing e�ciencymeasures derived from di�erent benchmarks is thus not permissable. Consider, forexample, a bank that is 80 % e�cient in t = 1 and exhibits a year later e�ciencyof 85 %. In any of these decomposition methods this would imply an improvement

8Note, that the importance of OBS was further analyzed in greater detail by Clark and Siems[2002].

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of e�ciency. However, it is very well possible that the majority of banks in t = 1exhibited substantially lower e�ciency, say 70 %, and markedly higher e�ciencyin t = 2, say 90 %. Compared to its' peers, our hypothetical bank did not reallyimprove its e�ciency between year t = 1 and year t = 2.

To avoid a comparison of banks relative to di�erent frontiers over time we there-fore suggest below a methodology to estimate one single frontier from panel data thatincorporates technical change according to a general productivity index suggestedby Baltagi and Gri�n [1988].

4 Methodology

4.1 Total factor productivity

Total factor productivity (TFP) change is one of the most widely employed measuresof overall productivity. The conventional Divisia index of TFP change is de�ned as:

·TFP =

·y −

·F , (1)

where·F =

∑i

wixi

C

·xi. (2)

Here y is observed output, F is an aggregate measure of observed input usage,wi is the price of the i-th input, xi is the observed use of the i-th input, and C isthe observed cost.9

Bauer [1990] departs from the Divisia index approximation of TFP change andbases his measure on the estimation of a cost frontier. Besides, he incorporatestechnical ine�ciency and non constant return to scale in the analysis. Let the multi-product cost frontier be represented by:

C∗ = C(y, w, t), (3)

where C∗ is the e�cient cost given (y, w, t). Following Farrell [1957], an inputbased overall measure of cost e�ciency may be de�ned as

CE =C(y, w, t)

C. (4)

Taking the natural logarithm of each side of equation (4), totally di�erentiatingwith respect to time, and making a few minor substitution yields

·CE = εcy(y, w, t)

·y +

∑i

∂C(y, w, t)

∂wi

wi

C(y, w, t)

·wi +

·C(y, w, t) −

·C, (5)

9We denote rates of changes with a dot over the variable, whereas changes are denoted by ∆.For example, in levels ·

y = (1/y)(dy/dt), which in logs is equivalent to ·y = d ln y/dt, as opposed to

∆y = yt − yt−1.

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where εcy(y, w, t) = [∂ ln C(y,w,t)∂ ln y

]. Kumbhakar and Lovell [2000] use the·

TFP de�-nition in equation (1) and rewrite equation (5) to obtain the following decompositionof pure TFP growth:10

·TFP = [1 − ∂ ln Ck(y, w, t)

∂ ln y]·y − ∂ ln Ck(y, w, t)

∂t− ∂u

∂t. (6)

The �rst expression on the right-hand side of equation (6) represents the com-ponent of TFP change stemming from changes in return to scale. The second termis the TFP change's component due to technological change. The last expressiondepicts the e�ect of technical e�ciency.

The component due to economies of scale for a bank k,·

SEkt, depends on twoe�ects, which together can in�uence rate in the change of TFP .

·SEy

kt = [1 − ∂ ln Ck(y, w, t)

∂ ln y]·y. (7)

The �rst e�ects are total scale elasticities as captured by the term inside thebrackets in equation (7). The second is due to changes in output quantities, denotedby ·

y = 1y

dydt. For example, if a bank exhibits constant returns to scale, changes in

the level of outputs do not in�uence the rate of TFP change. In turn, if for exampleinterbank loans exhibit increasing returns to scale

(∂ ln Ck(y,w,t)

∂ ln y1

)> 1, an increase in

these loans(

1y1

dy1

dt

)> 0 entails a decreasing contribution to TFP.

To model technological change most researchers rely on estimating separate fron-tiers per year and then disentangle cost changes due to changed parameters fromthose due to changing variables. This is problematic for the comparability problemof relative e�ciency measures discussed above. Instead, we follow an approach sug-gested by Baltagi and Gri�n [1988]. They argue that amending the cost frontierwith time trends both direct and interacted with production factors allows to derivetechnical change as the sum of partial time derivatives.11 If technological progressprevails, then

·TCkt < 0, and identical input quantities are, thanks to increased pro-

ductivity, converted into larger volumes of output at lower cost. We expect that cet.par. a downward shift in the cost frontier decreases costs and increases productivity.Note that technological change need not be neutral since we let t interact with inputprices w as well as output quantities y and further control variables z. Thus, we candistinguish three di�erent components of technical change: pure, non-neutral andscale augmenting technical change. We calculate the rate of total technical changeas:

10We assume the input mix is allocative e�cient. Therefore, the additional component of TFPgrowth that captures the impact of deviation of actual input cost shares from e�cient input costshares and the one due to allocative ine�ciency are not included in the decomposition of TFPgrowth we consider here.

11An application to European banking is a study by Altunbas et al. [1999] who disentangle pure,non-neutral, and scale-augmenting technical change.

11

·TCkt =

∂ ln Ck(y, w, t)

∂t= η0 +η1t+

3∑i=1

κi ln wikt +3∑

m=1

τm ln ymkt +2∑

r=1

θr ln zrkt. (8)

Where, η0+η1t identi�es pure technical change,3∑

m=1

τm ln ymkt+2∑

r=1

θr ln zrkt scale

augmenting technical change, and3∑

i=1

κi ln wikt non-neutral technical change. Pure

technical change re�ects reduction in total costs attainable holding constant thee�cient scale of production and the shares of each of the input in total costs. Scaleaugmenting technical change accounts for changes in the sensitivity of total coststo variation in the e�cient scale of production. If τm < 0 the scale of productionthat minimizes average costs is increasing over time. Finally non neutral technicalchange re�ects the sensitivity of total costs to variation in input prices. If κi < 0the share of the costs of input i in total costs is decreasing over time.

Alternatively, many researchers model technological change by estimating sepa-rate frontiers per year and then disentangle output changes due to changed parame-ters from those due to changing variables. This is in our view especially problematicwith regard to the estimated ine�ciency term ukt, as discussed next.

The �nal component of equation (6) captures the contribution to productivitychange of change in the cost of technical ine�ciency.12

·TEit =

∂uit

∂t. (9)

Until very recently econometric models of productivity change ignored the contri-bution of e�ciency. However if ine�ciency exists, its change provides an independentcontribution to productivity. In fact if e�ciency improves

·TEkt > 0, costs decrease

and productivity grows.

4.2 Stochastic frontier analysis

Components of total factor productivity are obtained from the parameters of anoptimum cost frontier that accounts for technological change as well as ine�ciency.To ensure the comparability of these relative measures across Italy and Germany wetherefore estimate an optimal cost frontier for both banking markets together. The-oretically, we assume that banks minimize cost C subject to a technology constraintT (•) when choosing input quantities x at given prices w in order to produce anoutput vector y. Even in the presence of public banks with potentially other objec-tives than pure pro�t maximization, we argue that cost minimization is a necessarycondition for any bank to remain in the market.

We use the translog functional form to write the resulting general form of astochastic cost frontier lnCkt = f(ykt, wkt, zkt, t) + vkt + ukt in logs as

12If e�ciency is time invariant, then this component drops out

12

ln Ckt = αk +3∑

i=1

αi ln wikt +3∑

m=1

βm ln ymkt +2∑

r=1

δr ln zktr (10)

+1

2

3∑i=1

3∑j=1

αij ln wikt ln wjkt +3∑

i=1

3∑m=1

γim ln wikt ln ymkt

+1

2

3∑m=1

3∑n=1

βmn ln ymkt ln ynkt +1

2

2∑r=1

πr(ln zktr)2

+2∑

r=1

3∑i=1

ωi ln wikt ln zktr +2∑

r=1

3∑m=1

ζm ln ymk ln zktr + η0t +1

2η1(t)

2

+3∑

i=1

κi ln wiktt +3∑

m=1

τm ln ymktt +2∑

r=1

θr ln zktrt + εkt.

In any year t, a bank k can deviate from optimal cost due to random noise, vkt,or ine�cient use of in- and outputs, ukt. To distinguish these two e�ects, we specifya composed total error, εkt. For a cost frontier ine�ciency leads to above frontiercosts. Therefore, the total error is εkt = ukt + vkt. The random error term vkt isassumed i.i.d. with vkt ∼ N(0, σ2

v) and independent of the explanatory variables.The ine�ciency term is i.i.d. with ukt ∼ N |(0, σ2

u)| and independent of the vkt. It isdrawn from a non-negative distribution truncated at zero. The use of duality impliesthe necessity to impose the following homogeneity restrictions:

∑i

αi = 1,∑i,j

αij = 0,∑im

γim = 0,∑

i

ωi = 0,∑

i

κi = 0. (11)

We therefore normalize total costs and input prices by the price of funds ω3. Totalfactor productivity components are estimated on the basis of estimated parametersof equation (10). Note, that we wrote the optimum cost frontier as a panel modelwhere banks' in- and outputs as well as their cost can vary across both the cross-section and time. Moreover, note that the ine�ciency component ukt is also allowedto vary across time. In contrast to most TFP (and e�ciency) studies we thus use anappropriate panel estimator to obtain the parameters as an input for the TFP cal-culation. More speci�cally, we employ a recently developed panel frontier estimatorsuggested by Greene [2005] that o�ers two decisive advantages in light of our study.First, non-random di�erences of banks' costs that are not due to ine�ciency arecaptured by the bank-speci�c �xed e�ect, αk. For example, systematic di�erencesdue to banking group membership, regional scope of activities or bank-speci�c char-acteristics not captured by the included variables. Note, that the αk's are allowedto be correlated with ykt, wkt and zkt [Greene, 2005]. Second, those banking studiesthat make full use of the panel nature of data usually choose to parameterize timevariation of the e�ciency component as a linear trend, see for example Lang andWelzel [1996]. A frequently employed estimator is based on the model by Batteseand Coelli [1988], where TEit = ui · γ · exp {−γ(t − T )}. Here, the parameter γis identical for all banks, and TE is either constantly increasing or constantly de-creasing. This is clearly a restrictive assumption, which is of particular economic

13

importance to our study. Since we are especially interested in the relative develop-ment of e�ciency (and other TFP components) due to the privatization of banks,we prefer not to impose too much structure on the development of the former overtime. Put di�erently, the Battese and Coelli [1988] model does not allow for an initialslump of ine�ciency in the immediate aftermath of a privatization, for example dueto necessary restructuring and subsequent adaptation to unrestricted competition.Instead, e�ciency is assumed a priori to follow a linear trend. In contrast to that,our estimator leaves the development of e�ciency over time entirely unrestricted.Greene [2005] points out that this method allows bank-speci�c e�ciency not only todi�er in it's development over time but also across cross-sectional units. Hence, weare able to allow some banks in a given year to improve their e�ciency while othersmay experience at the same time a deterioration.

After estimating equation (10) we obtain bank-speci�c e�ciency measures assuggested by Jondrow et al. [1982]. We use the conditional distribution of u given ε.A point estimator of technical e�ciency is given by E(ukt|εkt), i.e. the mean of ukt

given εkt. Cost e�ciency per bank and year is calculated as [exp(−ukt)] and equalsone for a fully e�cient bank. Likewise, CE of 0.9 implies that a bank could haveproduced an identical output vector with 90 percent of actually incurred cost.

5 Data and variables

We use balance sheet as well as pro�t and loss account data on an annual basis forall universal banks operating in Germany and Italy between 1994 and 2004. Thedata are obtained from the Deutsche Bundesbank and the Banca d'Italia. We usebalance sheets and pro�t and loss accounts data to de�ne the inputs and outputsof banking production process. Bank history data (such as the date of transforma-tion of the saving bank in stock company and foundation, the stake involved in thetransformation, and the date of the disposition of the banking share by the foun-dation) are used to analyze the impact of privatization on productivity change andcost e�ciency, and data on bank mergers to determine the e�ect of consolidation.

To estimate cost frontiers, we follow the intermediation approach of Sealey andLindley [1977] and de�ne three input and output categories. Input quantities are�xed assets x1, such as branches and administrative buildings; labor x2, measuredas full-time equivalents (FTE) and borrowed funds x3, measured as the volume ofdeposits and bonds. Input prices wi are derived per bank as depreciation relativeto �xed assets, personnel expenses relative to FTE and interest expenses relative tototal borrowed funds, respectively. As outputs we de�ne the volume of interbank andcustomer loans, y1 and y2, on the one hand and investment in stocks and bonds, y3,on the other. In addition, we add a time trend t alongside with interaction terms oftime and input prices as well as output quantities, respectively [Baltagi and Gri�n,1988]. We also include equity capital z1 and non performing loans z2 as part of thetransformation technology [Mester, 1993]. Descriptive statistics of all variables forthe period 1994-2004 are included in the appendix (Table 4).13

13All data were converted into 1995 prices using own country GDP de�ators.

14

6 Results

A thorough understanding of changes in productivity measures is important toeconomists and policy makers, because productivity growth is a major source ofeconomic growth [Bauer, 1990]. According to the Divisia Index, productivity growthoccurs when an index of outputs grows at a higher rate than an index of inputs. Incase of a cost frontier, productivity grows when Ct+1

yt+1 < Ct

yt .

Productivity growth results using the Divisia Index are reported in Table 1(GRC, �rst column; with a minus sign indicating growth). The results show that onaverage productivity grows in both countries during the period of analysis (1995-2004). Although both countries show almost the same trend, Italy improves pro-ductivity in the banking sector at a greater average rate. 14 Overall, productivitygrowth of Italian banks is indeed 0.074, which is signi�cantly higher than Germanbanks' productivity growth (0.027).

A question immediately arises: What are the sources of measured productivitychange? Econometric techniques permit to estimate the magnitude of total factorproductivity (TFP) change, and to decompose estimated productivity change into itsvarious sources [Kumbhakar and Lovell, 2000]. It can be seen again from Table 1 thatTFP growth results obtained from the parametric technique (TFP, second column)and the non parametric Divisia Index are consistent: mean TFP growth of Italianbanks is higher compared to German banks (0.045 in Italy and 0.033 in Germany).In Germany there are almost no di�erences between banking groups: private banksface a productivity growth of 0.032, saving banks of 0.031 and cooperatives of 0.034.On the contrary, in Italy there are di�erences between banking groups: the bankinggroups involved in the privatization process show indeed a higher level of TFPgrowth. Public banks (now private commercial) have a growth on average of 0.069,saving banks (now private commercial) of 0.057. Cooperatives, mutual and privatebanks have on average a TFP growth of 0.041.

The estimated rate of TFP growth depends on three components: the contribu-tion associated with returns to scale, technical change and change in cost e�ciency,which are depicted in the according columns in Table 1. Recall that output growthin the presence of diseconomies of scale reduces productivity growth, as does out-put contraction in the presence of scale economies. The results of the TFP changedecomposition show that the scale e�ect (Scale) was a source of decline in TFPboth for Italy and for Germany: banks face diseconomies of scale (the rate of changedue to scale e�ects is -0.013 in Italy and -0.009 in Germany). As a consequenceproductivity decreases. The e�ciency e�ect (E�ciency) appears non-relevant. It isclose to zero for both countries. Much of the increase in TFP is indeed the result oftechnological progress (Technical), that ameliorates at a rate of 0.057 in Italy and of0.040 in Germany. A negative sign of the technical change component indicates thatthe external factors (including time) enhanced TFP growth (cost diminution). Thuswe observe a downward shift in the cost frontier, ceteris paribus, for both countriesfrom 1995 to 2004. Following Altunbas et al. [1999], we decompose technical change

14Banks in Italy show negative productivity change in 1995 and end up in 2004 with a positiverate of productivity, slightly better than the German banks. The costs of Italian banks in factdiminish at a higher rate in comparison to the German banks , especially in the years 1996-1999(the average rate of change is 0.132 in Italy and 0.037 in Germany.)

15

Table 1: Average Productivity Growth and TFP Decomposition (1995-2004)

Country/Bank Type GRC TFP Scale Technology E�ciency NGermany Total -0.027 0.033 -0.009 -0.040 0.002 23797Saving banks -0.028 0.031 -0.015 -0.044 0.002 5276Private banks -0.015 0.032 -0.006 -0.036 0.002 649Cooperatives -0.027 0.034 -0.007 -0.039 0.002 17872Italy Total -0.074 0.045 -0.013 -0.057 0.0003 5241Public banks1) -0.086 0.069 -0.017 -0.081 0.005 64Saving banks1) -0.074 0.057 -0.002 -0.061 -0.003 620Private banks -0.052 0.034 -0.040 -0.071 0.003 430Cooperatives -0.063 0.039 -0.015 -0.063 -0.008 673Mutual banks -0.078 0.049 -0.010 -0.057 0.002 3454Notes: GRC: observed cost growth; TFP: Estimated total factor productivity changes;1) Privatized during the sample period. All numbers in percent.

into pure, scale augmenting and non-neutral components. Table 9 in the appendixreports estimates of the parameters of equation (10) obtained using the stochastic�xed e�ect panel frontier model. The results indicate that banks bene�t from costreductions due to pure technical change at an increasing rate during the period ofanalysis (η0 and η1 are both negative and strongly signi�cant). The e�cient scalefor production of interbank and customer loans decreased, but increased for securi-ties and bonds ( τ1 and τ2 are positive and signi�cant, while τ3 is negative and notsigni�cant). For non-neutral technical change, κ1 and κ2 are positive, indicating anincrease in the share of �xed asset and labor costs in total costs. Our results are inline with those of Altunbas et al. [1999]. They �nd increasing labor cost shares forEuropean banks during the period 1989-1996, and attribute the incapacity of banksto economize in this area to European labor market rigidities.

Standard microeconomic theory tells us that the presence of binding constrainsin the allocation of resources (due, for example, to state intervention) tends toincrease the cost of producing any given level of product or services. This is becausethe presence of constraints leads to ine�cient (non-optimal) allocation of inputs,and therefore results in increased costs. Conversely, when binding constraints areremoved (for example, in connection to a privatization of previously publicly-owned�rms), the industry is expected to generate some costs savings (given the level ofoutputs), which may be re�ected in higher productivity growth.

In order to evaluate the potential presence of binding constraints, we checked forthe e�ect of privatization and consolidation on TFP growth with a panel estimatoraccounting for time �xed e�ects. As a proxy for the privatization in Italy, we createdthree dummy variables: the �rst takes the value of one if a bank has been completelyprivatized in the previous four years ("privatization"); the second takes the value ofone if a bank has been partly privatized and its ownership still belongs for at least50 percent to a banking foundation ("foundation"); the third dummy is equal toone if a bank is public and owned by the state ("state"). As for the consolidation,we took account of the process with two dummies: the �rst has a value of one if amerger or an acquisition takes place in the same year of the observation ("M&A");the second takes the value of one if the merger or acquisition has taken place in theprevious four years ("M&A- P4Y").

The results of the analysis are reported in Table 2 below. We �rst test the impactof consolidation on TFP change and run a regression with "M&A" and "M&A-P4Y"as independent variables (model1). We than include the proxies for the privatization

16

process in the model and investigate the e�ects of changing in the ownership of banks(model2 include all bank, while model3 and model4 estimates Italian and Germanbanks separately). The results show that in Italy the privatization process has had apositive e�ect on TFP growth ("privatization" and "foundation" are both positiveand signi�cant). As for the consolidation, merger and acquisition dummies seem notto have had any signi�cant e�ect on TFP change of Italian banks and a slightlynegative e�ect on German banks ("M&A" and "M&A-P4Y" are non-signi�cant forItaly and negative for Germany).

Table 2: Regression Analysis

Consolidation PrivatizationTFP Change full sample full sample Italy Germany

M&A -0.024 ** 0.003 0.003 -0.030**(-2.32) (-0.36) (-0.36) (-2.5)

M&A-P4Y 0.007 0.005 0.005 0.006(-0.65) (-0.57) (-0.57) (-0.44)

country -0.017**(-2.52)

privatization 0.034*** 0.034*** 0(-3.51) (-3.51) (.)

state 0.025 0.025(-0.51) (-0.51)

foundation 0.029*** 0.029***(-3.15) ( -3.15)

saving banks Italy -0.019** -0.019**(-2.13) (-2.13)

saving banks Germany -0.007(-0.92)

Observations 17608 3126 3126 14482Number of banks 3450 630 630 2820R-squared 0 0.03 0.03 0Absolute value of z-statistics in parentheses * signi�cant at 10%; ** signi�-cant at 5%;*** signi�cant at 1%.

The results on the impact of the consolidation process on TFP change (both inItaly and in Germany) are not surprising. Many studies in the literature on banking�nd that on average mergers and acquisitions (M&As) do not necessarily lead tothe creation of additional value for shareholders [De Vincenzo et al., 2006]. Thisresult is partly related to the fact that the presence of economies of scale in thebanking industry is a highly debated topic in the literature. Moreover, the resultsof our regression on the impact of M&As seem to be coherent with the previousdecomposition of TFP: it has indeed shown that economies of scale have had adepressive impact on TFP change both in Italy and in Germany (and also that coste�ciency does not seem to have signi�cantly contributed to TFP growth).

As for the privatization results in the Italian banking system, they seem to bebroadly coherent with the empirical evidence on the evolution of that banking sys-tem since the beginning of the 1990's. The privatization that took place mainly inthe �rst half of that decade might have removed some potential distortion in theprocess of allocation of resources in the banking industry. This, in turn, appears tohave improved the investment capabilities of banks' (new) private owners and man-agers, especially with regard to the signi�cant amount of investments in technologyand innovation that took place in Italy since the mid-1990's. The result, as shownby our analysis, has been a signi�cant improvement in Italian banks' total factorproductivity, which seems to have (at least) converged to the level now prevailing

17

in the main European banking industries.

7 Conclusions

The comparative analysis of the evolution of two important European banking in-dustries, Italy and Germany, over the last decade may shed some light on the likelyconsequences of partly di�erent development paths. While both banking systemshave experienced a deep process of consolidation, currently still ongoing, Italy andGermany signi�cantly di�er with respect to the presence of publicly-owned banksin the industry.

At the beginning of the 1990's, important reforms in the banking legislationstimulated the privatization of the main banks in Italy. The process has proceededstep by step and has bene�ted from the intervention of institutional investors likethe banking foundations. Since 1990 the market share of publicly-owned banks hassteadily decreased from around 70 percent to 10 percent in 2005. Banking founda-tions still own equity of the main Italian banking groups. But, also as a consequenceof the consolidation process, they are now clearly minority shareholders and, moreimportantly, their behavior mimics increasingly that of other institutional investors(like pension funds). In Germany a signi�cant part of the banking industry (the sav-ings banks pillar) is still in the hands of local governments. The debate on potentialcosts and bene�ts of privatizing this important sector continues to be controversial.This debate has now come back to the fore in connection with a new wave of mergersand acquisitions that is shaping the European banking industry.

The analysis we have carried on in this study intends to contribute to the currentdebate by providing some empirical evidence based on the comparison of the Italianand German banking systems.

Our results on total factor productivity (TFP) changes show that both industrieshave experienced TFP growth during the last decade. But Italy's growth has beensigni�cantly higher than Germany's. In both countries most of the TFP growth isdue to improvements in the banking technology, potentially re�ecting strong ICTprogress recently. Cost e�ciency seems to have added little value to productivityOur results also show that, in line with many banking empirical studies, banks inboth countries face diseconomies of scale.

The results of a multivariate regression of TFP changes show that in Italy theprivatization process has had a positive in�uence on the dynamics of productivity.TFP changes have been larger for banks that have been totally or even partiallyprivatized. As a matter of fact, today's main Italian banking groups and large formersavings banks belong to the �rst type of banks (totally privatized), while a numberof small local savings banks still belong to the second type (partially privatized). Forthe latter type of banks, banking foundations still own a signi�cant shareholding,which is nonetheless decreasing as a result of the ongoing consolidation process.

Overall, we believe our empirical results can be useful for academicians, prac-titioners, the �nancially community, and, above all, for policy makers, to whomeventually rest the responsibility to take legislative steps in these issues.

18

8 Appendix

Table 3: Productivity Growth and TFP Decomposition (1995-2004)

Year TFPC SCALEC TCC EFC NItaly 1995 0.046 -0.026 -0.085 -0.013 466

1996 0.051 -0.031 -0.087 -0.005 4901997 0.091 -0.026 -0.082 0.035 5061998 0.047 -0.012 -0.066 -0.007 5221999 0.019 0.006 -0.039 -0.026 5522000 0.031 -0.022 -0.051 0.001 5592001 0.040 -0.021 -0.056 0.005 5582002 0.051 -0.003 -0.048 0.006 5442003 0.038 0.005 -0.034 -0.001 5252004 0.040 0.006 -0.027 0.007 519

Germany 1995 0.039 -0.013 -0.044 0.007 28351996 0.033 -0.007 -0.040 0.000 29001997 0.035 -0.008 -0.039 0.004 28501998 0.031 -0.015 -0.039 0.007 27301999 0.025 -0.006 -0.036 -0.006 25302000 0.032 0.006 -0.040 -0.014 23202001 0.038 -0.018 -0.043 0.013 21352002 0.033 -0.009 -0.039 0.002 19732003 0.021 -0.012 -0.034 0.000 18242004 0.033 -0.002 -0.032 0.003 1700

GRC=Growth Change, TFPC= Total Factor Productivity GrowthSCALEC= Scale Change, TCC=Technical Change, EFC= E�ciency Change

Table 4: Bank production data for Italian and German banks (1994-2004)

Country Variable Mean SD Min Max NItaly Interbank loans y1 212.432 1,117.340 0.004 22,396.290 6362

Customer loans y2 974.752 4,437.375 0.601 94,681.380 6362Securities y3 250.871 835.180 0.008 13,160.680 6362Price of �xed assets w1 5.890 2.455 1.993 19.671 6362Price of labor w2 48.476 5.818 30.573 80.694 6362Price of funds w3 3.697 1.936 1.010 15.689 6362Equity z1 136.722 561.552 0.558 11,677.200 6362Non-performing loan share z2 8.907 6.752 0.008 38.181 6362Total Cost C 110.015 486.868 0.569 9,280.111 6362

Germany Interbank loans y1 127.124 1,869.644 0.001 103,324.500 27736Customer loans y2 478.666 3,818.360 1.129 204,335.800 27736Securities y3 185.887 1,667.635 0.002 99,729.890 27736Price of �xed assets w1 14.532 8.033 5.135 74.130 27736Price of labor w2 48.530 7.352 28.386 92.741 27736Price of funds w3 3.515 0.651 1.868 5.475 27736Equity z1 35.798 290.488 0.245 14,052.140 27736Non-performing loan share z2 5.788 4.483 0.000 31.614 27736Total Cost C 46.129 396.123 0.356 21,705.730 27736

Notes: All variables except input prices and non-per. loans share in millions Euros. Price of funds,�xed assets and non-per. loans share in percent. Price of labor in thousands Euros.

19

Table5:

Structureof

theItalianbankingsystem

between1990

and2005

1990

2005

No.of

banks

No.of

branchesAs

sets

million

EUR

1

Assets

share

in%

No.of

banks

No.of

branchesAs

sets

million

EUR

1

Assets

share

in%

Public-sector

banks

62,4

49134,6

6420.1

Priva

te/C

omm.b

anks

243

24,04

51,8

79,94

579.3

Bankso

fnation

alinterest

31,4

5986,46

612.9

Savingsb

anks

844,6

95162,4

2724.2

Priva

teCo

mmercia

lbanks

106

3,981

137,3

6220.5

Cooperative

banks

108

3,290

95,00

414.2

363,7

45228,5

329.6

Mutualb

anks

715

1,792

29,09

64.3

Mutualb

anks

2439

3,603

126,3

695.3

Groupcentralinstitution

s5

515,87

52.4

��

Branches

offoreign

banks

3750

10.47

51.6

66108

137,0

635.8

Total

1,064

17,72

1671,4

09100

784

31,50

12,3

71,90

9100

Source:B

anca

d'Ita

lia(1)d

ataon

asolo-in

dividu

albasis

refer

redto

thebankingun

itsoperatingin

Italy

(2)"

banche

dicredito

cooperativo"

Public-sector

banks(

"Istitu

tidi

diritto

pubb

lico"),Ba

nkso

fnationalinterest(

"Banchedi

interessenazio

nale"

),Savingsb

anks

("Ca

ssedi

risparm

io"and"M

ontidi

credito

"),

Priva

tecommercia

lbanks

("Ba

nche

dicredito

ordinario

"),C

ooperativ

ebanks

("Ba

nche

popolari"

)Mutualb

anks

("Ca

sser

uralie

artig

iane"),G

roup

centralinstitutions

("Ist

ituti

centralidi

categoria

")

Table6:

Structureof

theGerman

bankingsystem

between1990

and2005

1990

2005

No.of

banks

No.of

branches

3As

sets

million

EUR

Assets

share

in%

No.of

banks

No.of

branches

Assets

million

EUR

Assets

share

in%

Commercia

lbanks

1281

6,919

1,332,68

825.4

163

14,19

41,8

29,87

226.5

Savingsb

anks

771

20,22

01,0

80,85

520.6

463

14,41

31,0

13,95

514.68

Regio

nalgiro

institutio

ns11

410

761,7

6914.5

Land

banks

12592

1,365,04

519.77

Creditcooperative

s3,3

9221,19

7591,8

8911.3

1,294

14,01

5591,8

868.5

7Re

gionalinstitution

sofcreditc

ooperativ

es4

37216,6

874.1

211

223,7

023.2

4Sp

ecialize

dbanks2

70300

1,183,66

322.6

662,8

371,7

75,36

525.71

Branches

offoreign

banks

6096

76,29

11.4

89207

103,3

441.4

9To

tal

4,589

49,17

95,2

43,84

2100

2,089

46,26

96,9

03,16

9100

Source:B

undesbank

1.In

1990

inclu

de:b

igbanks,regional

andotherc

ommercia

lbanks.In2004

inclu

de:b

igbanks,regional

andotherc

ommercia

lbanks.2

.In1990

inclu

de:m

ortgagebanks,special

purposebanks,post

o�ce

andpost

saving

banks.In

2004

inclu

de:m

ortgagebanks,bu

ildingandloan

associa

tions,specia

lpurpose

banks.3.Da

tafro

m1991

(not

availablefor

1990).

20

Table7:

Italianbankingsystem

-Ind

icators

Averagetotala

ssets

Net-interestincom

eNo

n-interest

income

Operatin

gcosts

Pro�tb

efore

tax

Net-interestincom

e/Operatin

gcosts/

Sta�

costs/

(1)

(2)

(3)

(3)

(3)

(3)

Totalincom

eTo

talincom

eOperatin

gcosts

ROE

Year

Banking

system

Largest

groups

Banking

system

Largest

groups

Banking

system

Largest

groups

Banking

system

Largest

groups

Banking

system

Largest

groups

Banking

system

Largest

groups

Banking

system

Largest

groups

Banking

system

Largest

groups

Banking

system

Largest

groups

1994

100,0

100,0

2,48

1,87

0,91

0,80

2,36

1,98

0,30

0,14

26,9

30,0

69,5

74,0

47,4

53,7

1,1

0,9

1995

107,3

111,3

2,77

2,20

0,88

0,79

2,44

2,18

0,43

0,26

24,1

26,4

66,9

72,9

44,9

51,5

2,0

2,7

1996

119,2

115,3

2,63

2,10

1,06

0,92

2,43

2,14

0,60

0,34

28,6

30,5

65,8

70,9

45,2

51,3

5,1

4,4

1997

130,9

118,5

2,41

1,94

1,12

0,99

2,35

2,07

0,42

-0,11

31,7

33,9

66,7

70,6

44,6

49,9

1,9

-7,5

1998

137,8

140,5

2,33

2,12

1,51

1,51

2,30

2,14

0,93

0,83

39,2

41,5

60,0

58,9

39,1

39,8

9,2

11,1

1999

157,4

156,6

2,12

1,97

1,58

1,61

2,23

2,06

0,91

1,00

42,7

45,0

60,3

57,6

39,0

39,2

11,0

16,3

2000

180,7

201,0

2,21

1,99

1,74

1,72

2,27

2,11

1,16

1,09

44,1

46,4

57,5

57,1

36,4

37,1

13,3

17,5

2001

193,8

201,3

2,18

1,91

1,47

1,47

2,16

1,95

0,79

0,74

40,3

43,4

59,0

57,7

37,4

37,8

9,1

12,5

2002

199,0

202,7

2,25

2,04

1,30

1,28

2,15

1,99

0,67

0,53

36,7

38,5

60,7

60,2

38,8

39,5

6,5

5,8

2003

206,7

199,2

2,19

1,96

1,41

1,49

2,12

1,98

0,70

0,77

39,1

43,3

59,0

57,3

38,2

38,0

6,7

9,2

2004

210,7

210,7

2,15

2,02

1,37

1,50

2,05

2,06

0,97

0,97

38,9

42,6

58,2

58,5

38,3

39,1

10,7

12,5

Source:B

anca

d'Ita

lia(1)T

otal

bankingsystem

'sassets

dividedby

thetotaln

umbero

fbanking

groups/banks

(2)ind

exnumbers

1994=100

(3)ratiost

ototala

sset

Table8:

German

bankingsystem

-Ind

icators

Year

Average

Net-interest

Non-interest

Operatin

gPro�t

Net-interestincom

e/

Operatin

gCo

sts/

Sta�

costs/

ROE

totala

ssets(

1)

(2)

income

(3)

income

(3)

costs(

3)

before

tax

(3)

Totalincom

eTo

talincom

eOperatin

gcosts

1994

100,0

1,91

0,43

1,43

0,50

18,5

60,9

57,7

6,8

1995

105,9

1,72

0,43

1,38

0,50

19,8

64,1

57,9

6,6

1996

112,6

1,63

0,41

1,30

0,46

20,0

64,0

56,5

6,0

1997

121,7

1,46

0,42

1,19

0,45

22,2

63,3

55,9

6,7

1998

124,6

1,34

0,46

1,15

0,62

25,6

63,7

55,5

9,4

1999

130,9

1,23

0,49

1,14

0,37

28,5

66,2

54,2

5,9

2000

142,3

1,13

0,58

1,17

0,34

34,0

67,9

53,4

6,1

2001

171,6

1,10

0,51

1,15

0,24

31,6

71,0

52,3

4,4

2002

161,7

1,20

0,48

1,14

0,20

28,4

67,7

52,4

2,2

2003

155,8

1,15

0,54

1,13

0,10

31,7

66,9

53,0

-1,7

2004

158,0

1,15

0,47

1,07

0,20

29,0

66,0

53,5

1,8

Source:D

eutscheBu

ndesbank

(1)T

otal

bankingsystem

'sassets

dividedby

thetotaln

umbero

fbanking

groups/banks

(2)in

dexnumbers

1994=100

(3)ratiost

ototala

sset

21

Table 9: Estimated stochastic cost frontier parametersVariable Coe�cient Standard Error b/St.Er. P[|Z|>z] Mean

α1 0.3241 0.0148 21.845 0.000 1.1855α2 0.2668 0.0207 12.87 0.000 2.6868β1 0.2835 0.0085 33.153 0.000 2.9977β2 0.5769 0.0121 47.374 0.000 4.8346β3 0.4261 0.0082 51.888 0.000 3.8033δ1 -0.3131 0.0153 -20.403 0.000 2.5056δ2 0.1060 0.0072 14.622 0.000 1.5559α11 0.0112 0.0032 3.425 0.0006 0.8698α12 -0.1132 0.0056 -20.145 0.000 3.2427α22 0.1468 0.0127 11.491 0.000 3.6713β11 0.0368 0.0005 69.164 0.000 5.6925β12 -0.0445 0.0015 -27.892 0.000 16.3425β13 -0.0201 0.0005 -40.042 0.000 13.1775β22 0.0696 0.0032 21.575 0.000 12.7993β23 -0.0480 0.0012 -39.846 0.000 20.3894β33 0.0595 0.0005 99.271 0.000 8.4524π1 -0.0522 0.0047 -11.079 0.000 4.1691π2 0.0077 0.0005 14.021 0.000 1.6820γ11 0.0200 0.0014 14.092 0.000 3.5520γ12 -0.0311 0.0028 -10.777 0.000 8.0555γ21 -0.0420 0.0026 -15.872 0.000 5.6636γ22 -0.0677 0.0046 -14.581 0.000 12.9835γ31 0.0360 0.0014 25.483 0.000 4.4258γ32 -0.0757 0.0032 -23.263 0.000 10.1942ζ11 0.0215 0.0016 12.712 0.000 9.2923ζ12 0.0070 0.0007 9.171 0.000 4.6492ζ21 0.0244 0.0032 7.531 0.000 14.1194ζ22 -0.0193 0.0009 -20.216 0.000 7.4437ζ31 0.0042 0.0012 3.332 0.0009 11.4820ζ32 0.0171 0.0007 24.093 0.000 5.8592ω11 -0.0285 0.0033 -8.6 0.000 2.8166ω12 0.0065 0.0011 5.875 0.000 1.7636ω21 0.1990 0.0060 32.994 0.000 6.7700ω22 -0.0391 0.0028 -13.649 0.000 4.1963η0 -0.1045 0.0038 -27.128 0.000 6.5291η1 -0.0008 0.0001 -5.487 0.000 52.1079τ1 0.0019 0.0003 5.521 0.000 20.2975τ2 0.0202 0.0007 27.583 0.000 32.6016τ3 -0.0004 0.0004 -1.097 0.2728 25.5366κ1 0.0149 0.0006 22.447 0.000 8.1037κ2 0.0132 0.0017 7.538 0.000 18.3687θ1 -0.0229 0.0008 -26.994 0.000 17.5324θ2 0.0036 0.0003 9.955 0.000 10.3300Observations 34076

22

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25