Strategic and Queue effects on Entry in Spanish Banking

Strategic and Queue Effects on Entry inSpanish Banking

Lucio Fuentelsaz and Jaime GomezUniversidad de Zaragoza

Zaragoza Spainlfuentepostaunizares jgvillaspostaunizares

This paper analyzes the factors that inuence entry and geographic diversi-cation decisions a topic of special strategic interest in a context of growingglobalization The empirical model we propose is tested in a frameworkmdashthe Spanish savings-bank marketmdashwhere recent deregulation has eliminatedthe legal barriers to entry Our results show two important conclusions forthe evolution of the effects of branching deregulation in Europe and the USFirst it seems that entry in new geographical markets has been impeded bythe strategic interactions between entrants and incumbents Second savingsbanks exhibit a preference for closer locations at the time of expanding whichmay have undermined the effects of deregulation and its potential benetsfor consumers

1 Introduction

A stream of research in the literature of strategic management andof industrial organization has been concerned with the determinantsof diversication and entry decisions Both perspectives have recog-nized that entry decisions (diversication) are not random whencethe interest in studying the importance of the explanatory factors thatdetermine them Characteristics of the potential entrant and the targetmarket have attracted the main attention from researchers In this con-nection recent papers have tended to emphasize the idea that poten-tial entrants are not alike and the inuence of incumbentsrsquo behavioron the number of rms entering the market (Barros 1995 Coterill and

This research was supported by the DGICYT project PB95-0808 (Lucio Fuentelsaz) andthe CICYT project 96-0712 (Jaime Gomez) We gratefully acknowledge helpful com-ments from Pedro P Barros Rebeca de Juan Andy Lockett Miquel Manjon FranciscoPerez Yolanda Polo Angela Rauff Vicente Salas Inmaculada Villanua Michael Water-son two anonymous referees and the editor and coeditor of this journal Howevererrors remain our sole responsibility The research of one of us (JG) was partly doneduring visits to the University of Nottingham Business School and the Department ofEconomics of the University of Warwick I am grateful for their hospitality

copy 2001 Massachusetts Institute of TechnologyJournal of Economics amp Management Strategy Volume 10 Number 4 Winter 2001 529ndash563

530 Journal of Economics amp Management Strategy

Haller 1992 Ingham and Thompsom 1995 Mata 1993 Merino andRodrotildeguez 1997)

Following this emphasis the objective of this paper is to offerempirical evidence on the role of the factors that determine the pat-terns of geographical diversication in the Spanish noncooperativesavings-bank market This is an especially interesting sector in thatcompetition in the Spanish banking system has been tightly regu-lated until recently The existence of ceilings on interest rates on loansand deposits and the clear delimitation of the activities carried outby each nancial intermediary with the commercial banks special-ized in wholesale activities and the noncooperative savings banks inretail banking are two examples of this situation As a consequencecommercial banks and noncooperative savings banks have had littleleeway in the design of their competitive strategies

Deregulation affected commercial banks as much as noncoop-erative savings banks However the different legal and foundationalnature of the latter and the additional restrictions they had to supporthave produced more intense and relevant changes for them Until1989 the geographical scope of these entities was restricted to a local-ity province or region1 The elimination of this limitation and the sub-sequent expansion of various banks constitutes an ideal framework inwhich to perform our analysis

In this paper we analyze the behavior of Spanish noncoopera-tive savings banks after interregional banking was allowed in order topresent some reections on the factors that have affected their expan-sion decisions Our research attempts to study the reasons that haveprovided some banks with an incentive to choose a strategy of growthunderpinned by the widening of their geographical scope of operation(offensive expansion) in contrast with the ones that have preferredto consolidate their position in their traditional markets (defensiveexpansion)

Our contribution is threefold First unlike previous papers thathave concentrated on market (industrial organization) or rm (strate-gic management) variables we attempt to integrate both kinds in ajoint framework Second given that some of the constructs used tojustify diversication decisions in the strategic management literatureare unobservable the econometric procedure attempts to overcomethe consistency problems that stem from traditional estimation pro-cedures To achieve this a conditional logit model is estimated anda Hausman test is used to provide consistent and efcient estima-tors of the entry model We follow the approach initially proposed by

1 Whereas commercial banks have had full freedom of establishment since 1974savings banks lacked that ability until 1989 (RD 15821988 December 29th Law)

Entry in Spanish Banking 531

Lemelin (1982) and recently adapted by Merino and Rodrotildeguez (1997)to the consistent analysis of diversication decisions Finally whereasthe few existing papers that analyze diversication decisions in such acontext deal with product diversication this research places empha-sis on the geographical dimension of entry In accordance with pre-vious studies the results show that both strategic and queue effectsare signicant in explaining noncooperative savings banksrsquo entry pat-terns

The rest of the paper is structured as follows First a brief over-view of the expansion process followed by the Spanish noncoopera-tive savings banks is presented Section 3 summarizes the perspectivesfrom which diversication and entry decisions have traditionally beenanalyzed in the strategic-management and industrial-organization lit-eratures Section 4 presents the model and hypothesis that will betested in the analysis Sections 5 and 6 dene the variables and econo-metric procedures that will be used in the empirical analysis per-formed in Section 7 Finally we summarize the main conclusions ofthe paper and discuss some implications of our ndings for otherrecently deregulated banking systems as in the US and in other Euro-pean countries

2 The Expansion of the Spanish Savings Banks

The Spanish banking sector consists of three types of intermediariescommercial banks (CBs) noncooperative savings banks (NSBs) andcooperative savings banks (CSBs) The geographical area of activity ofthe rst is national in scope (although some CBs have chosen to limittheir activities to smaller areas) and the expansion of their activitieshas not been subject to any type of limitation for many years How-ever the possibilities of geographical expansion for savings-bankswere limited to a regional level (in the case of some NSBs) or a locallevel (in the case of other NSBs and all CSBs) until 19892

Table I reects the evolution of the number of branches depositsand loans for CBs and NSBs It can be noted how the greater impor-tance enjoyed by CBs during the 1980s has been progressively reducedwith the passage of time Thus the volume of deposits currentlybeing captured by NSBs exceeds that of CBs whereas in 1980 their

2 In Spain by contrast with other European countries the CSBs have been rele-gated to a secondary role (throughout the last two decades they have maintained astable market share with a branch network representing around 9ndash10 of the totalcapturing approximately 5 of deposits and granting 4 of loans) For this reason wewill concentrate our analysis on the two basic pillars upon which the sector rests theCBs and NSBs


TA

BL

EI

Nu

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De

po

sit

s

an

dL

oan

sfo

rC

om

me

rc

ial

Ban

ks

an

dN

on

co

op

er

ativ

eS

av

ing

sB

an

ks

Rel

ativ

eM

arke

tSh

are

Nu

mbe

rof

Dep

osit

sL

oans

Bra

nche

s(B

illio

nsof

pes

etas

)(B

illio

ns

ofp

eset

as)

Bra

nch

esD

epos

its

Loa

ns

Year

CB

NSB

CB

NSB

CB

NSB

CB

NSB

CB

NSB

CB

NSB

1980

132

318

588

722

93

714

726

22

033

6139

6634

7822

1985

166

0610

797

111

837

804

106

874

058

6139

5941

7228

1990

169

1713

720

150

2113

669

180

779

945

5545

5248

6535

1996

176

7416

094

223

0425

312

310

3521

218

5248

4852

5941

Sour

ce

Ban

kof

Spai

n


deposits were barely half those of CBs Although the later still main-tain a dominant position in the loan market the differences are con-siderably less than they were The same applies to the numbers ofbranches While CBs went from3 16606 to 17674 branches between1985 and 1996 the number of branches of NSBs increased from 10797to 16094 in the same period despite the wave of mergers that tookplace at the beginning of the 1990s which led many of the mergedentities to restructure their networks eliminating some of the redun-dant branches4

These changes in the structure of the sector may be explainedin terms of the response of NSBs to changes in regulation and par-ticularly the elimination of the restrictions that impeded them fromoperating throughout the national territory As we have already men-tioned prior to liberalization activities of savings banks were geo-graphically conned in scope with the largest operating within theirregions and the smallest in one or two provinces5 Furthermore thenumber of NSBs that operated in each province was very small (nor-mally one or two) which gave them signicant market power There-fore branching restrictions had important consequences in terms ofrivalry within the sector as they acted as a protection against a par-ticularly important aspect of competition attributable to the threat ofentry into the market by new competitors6

After the total lifting of entry regulation the Spanish savings-bank network substantially increased in size This network growthpresents some peculiarities that should be highlighted Table II showsa clear change in the pattern of the expansion process During therst years NSBs opened most of their new branches in their origi-nal markets (defensive expansion) but from 1989 there was a change intendency and the majority of them were opened in provinces differ-ent from the ones that constituted their traditional scope of operation(offensive expansion)7 The last column of Table II conrms the situa-

3 We must take into account that an important part of this increment comesas a consequence of the integration of public banks into the ranks of CBs (theywere previously considered separately) This means 680 new branches If wediscount that increase the real growth is reduced to something less than 400branches

4 As a consequence of the process of mergers and acquisitions that took place afterderegulation the number of NSBs was reduced from 77 to 50

5 Spain in divided into 17 autonomous regions Each region is divided intoprovinces of which there are a total of 50

6 It could be argued that potential competition did in fact exist given that CBs hadfreedom of location throughout the national territory Nevertheless the segmentationof activities between CBs and savings banks meant that rivalry between the two wasuntil recent years limited

7 As will be argued later we consider that a NSB is operating in a province whenit has at least 1 of its bank branches in that province Taking this into account the


TABLE IIEvolution of the Number of Branches of

Noncooperative Savings Banks

Total Original NewNumber of Market Markets (3)(1)Branches (1) Increase (2) Increase (3) Increase ()

1986 11291 11037 254 221987 11707 416 11320 283 387 133 331988 12295 588 11797 477 498 111 411989 13135 840 12157 360 978 480 741990 13664 529 12402 245 1262 284 921991 13839 175 12298 104 1541 279 1111992 14107 268 12310 12 1797 256 1271993 14245 138 12294 16 1951 154 1371994 14578 333 12348 54 2230 279 1531995 14989 411 12509 161 2480 250 1651996 15851 862 12728 219 3123 643 197

Source Authorrsquos calculations from CECA (Spanish Federation of Savings Banks)

tion pointed out while in 1986 only 2 of the branches of NSBs werelocated outside the provinces in which they had traditionally beenoperating this percentage was almost 20 ten years later

TABLE IIINumber of Provincial Markets by

Savings Bank

No of NSBsNumber ofprovinces 1986 1996

1 48 142 11 83 5 64 8 95 2 36 9 3 310 15 0 5gt 15 0 2Total 77 50Mean 20 55

Source Authorrsquos calculations from CECA

original market of each bank consists of the provinces in which it was operatingin 1986 We will speak in terms of defensive expansion when the opening of newbranches takes place in the original market of the bank On the other hand offensiveexpansion will be in provinces in which the bank was not initially operating


TABLE IVNumber of NSBs by Province

No of provincesNumber ofNSBs 1986 1996

1 12 02 18 13 10 14 6 135 1 126 2 117 0 78 1 29 0 110 0 2

Total 50 50

Mean 28 55


The information in Table II is complemented with that presentedin Tables III and IV where the numbers of branches that were affectedby the offensive expansion are taken into account Table III evidencesthe markedly provincial character of the NSBs at the beginning of theperiod of analysis Almost two-thirds of them had that geographi-cal scope of operation Of the remainder the majority operated in anumber of provinces ranging from two to four (usually belonging tothe same autonomous region) and only ve were present in ve ormore markets

The expansion of NSBs has for the most part been moderateAlmost all the NSBs that decided to access new markets limited them-selves to completing their presence within the autonomous region inwhich they had their headquarters or to expanding to a neighbor-ing province8 As a result the average NSB extended its scope ofoperation from 20 provinces in 1986 to 55 in 1996

As a consequence the intensity of competition in the provin-cial markets has been substantially altered If rivalry were evaluatedthrough any of the usual indexes of concentration a decrease of theseindicators and consequently an intensication of competition would

8 This evidence is consistent with expansion patterns observed for the US bankingsystem during the period 1988ndash1993 McLaughlin (1995) shows that following liber-alization of state branching and interstate banking laws US bank holding companiesldquoexhibited a preference for intraregional rather than interregional expansionrdquo On theother hand ldquonearly 75 percent of all rst time entries represented moves into a neigh-boring staterdquo (McLaughlin 1995)


be observed We simply have to look at the data in Table IV in whichthe number of NSBs operating in each provincial market at the endof 1986 and 1996 is summarized In 1986 NSBs were operating asmonopolies in 12 provinces The market was a duopoly in eighteenprovinces In another ten provinces three savings banks were com-peting and only in four provinces were there ve to eight

In ten years the situation has changed substantially and there isno province in which just one NSB is operating There are two or threeNSBs in only two provinces in most there are four to seven From1986 to 1996 the average number of NSBs per province increasedfrom 28 to 55 This fact together with the ability of savings banksto undertake the same activities as CBs has led to an increase incompetition in the banking sector

Therefore two important implications emerge from the new com-petitive framework On the one hand placing the activities carried outby both CBs and savings banks on the same footing has implied thatthe former have become important competitors for the latter On theother hand the elimination of restrictions has meant that some ofthe savings banks have reconsidered their strategies and have rapidlyexpanded beyond their traditional markets This has affected not onlythe behavior of the expansionist savings banks but also the attitudeof the other banks which now face greater competition from savingsbanks that previously were not direct rivals9

3 Geographic Diversi cation and Entry intoNew Geographical Markets

The process of entry into new markets has been analyzed in theliterature from at least two perspectives Strategic management has beenmainly occupied with the causes and consequences derived from busi-ness diversication in a context in which the rm selects the size thatmaximizes prots On the other hand the industrial-organization per-spective has studied entry and exit processes considering that theintensity of entry will depend as much on barriers to entry as onfuture prot expectations Next we synthesize the main concerns ofeach one of these approaches

From a business diversication point of view the selection of theproduct-market set in which it is to be operating is one of the main

9 One the most commonly employed responses of savings banks to the new compet-itive situation was merger Thus 19 mergers of this nature took place in Spain between1989 and 1993 affecting a total of 40 NSBs the number of NSBs thereby falling from77 to 50 between 1989 and 1996


challenges associated with the process of formulation and implemen-tation of a rmrsquos corporate strategy Naively the optimum level ofdiversication will be the one that maximizes the rmrsquos value in thelong run However in some circumstances there can be other reasonsapart from the strictly economic ones that condition the decisionstaken by managers The literature on business diversication offersseveral justications (Montgomery 1994 Markides 1995) Accordingto them rms also decide to add new businesses to their existing onesbecause of market-power arguments agency theory or the existenceof market failures

According to the market-power view diversication may be exp-lained by a rmrsquos attempts to increase its power through the creationof a multimarket rm Market-power arguments emphasize that oneof the consequences of diversication is an increase in the size ofrms combined on some occasions with a reduction in their num-ber In an oligopoly context this would reduce rivalry and facilitatemutual forbearance strategies [tacit collusion between rms simul-taneously operating in the same marketsmdashKarnani and Wernerfelt(1985) Gimeno and Woo (1996)]

From an agency-theory point of view diversication may be con-sidered as a mechanism to take advantage of resources generated insome activities in order to achieve a bigger rm (Jensen 1986)10 Inthis context a related approach that has received extensive attentionin the economics and banking literature has been the expense prefer-ence theory11 According to this theory managers in these rms havea positive preference for staff expenditures managerial emolumentsand discretionary prots Therefore an expense-preference rm willshow a relatively larger staff or will pay higher managerial wagesthan a prot-maximizing rm12

Diversication may also arise as a consequence of either nancialor product-market failures Sometimes large diversied corporationscreate internal capital markets to avoid the costs incurred by usingnancial markets As Grant (1995 p 382) notes ldquoone of the objectives

10 Sometimes excess diversication is undertaken by managers not for the satisfac-tion of personal objectives but because the managersrsquo expectations are more optimisticthan those of shareholders with regard to the capacity of the rm to transfer assetsamong industries This is known as hubris hypothesis (Roll 1986)

11 See Edwards (1977) and Hannan (1979) for banking in general See Verbruggeand Jahera (1981) Akella and Greenbaum (1988) Krinsky and Thomas (1995) andAkella and Greenbaum (1995) for evidence of expense-preference behavior in the sav-ings and loan industry

12 An especially interesting hypothesis is the one that points to the possibility ofbranching as a way of maximizing managersrsquo utility ldquoExpense preference managersmay use branching as a strategy to increase utility-enhancing expenditures on staffrdquo(Edwards 1977 p 156)


of portfolio analyses is to achieve a balance between cash-generatingand cash-using business so that the corporation is largely independentof external capital marketsrdquo Alternatively the resource-based view ofthe rm (Penrose 1959 Wernerfelt 1984 Barney 1986) has receivedspecial attention during recent years From this view rms diversifybecause they have excess resources in their basic activities but theseresources are not easily traded because of the difculties in assigningthem a market price (Peteraf 1993 Barney 1991) or because of thesynergies that can be obtained by transferring assets such as brandname reputation or technological or organizational capabilities fromone business to another The only way of taking efcient advantageof such resources comes through increasing the rm size allowingsimultaneous use by its various activities According to this secondeffect diversication is a consequence of the use of internal organiza-tion as a solution to the problem of product-market failure (Inghamand Thompson 1994)

Regardless of the reasons underlying diversication strategiesthe majority of the empirical studies that stem from this perspectiveshare two characteristics First the level of diversication is used inall of them as an explanatory variable (together with other regres-sors which summarize the structure of each market) The purpose ofthese models is to identify the determinants of business performanceand diversication In these cases diversication is usually measuredthrough a continuous indicator (Herndahl or entropy-type indexes)or through discrete classications along the lines proposed by Rumelt(1974 1982)13

The second common element in these studies is that almost allof them are fundamentally centered on questions related to productdiversication [some attempts to explain international diversicationcan also be found Ghoshal (1987) Hitt et al (1997)] If we understandbusiness strategy as the selection of the product-market set in whichthe rm wishes to operate these studies have focused much more onthe product variable paying less attention to the geographical dimen-sion of strategy

One complementary way of approaching the geographical exp-ansion process is the one provided by industrial organization in theanalysis of rmsrsquo entry and exit processes These studies consider thatthe existence of economic prots in some industrial sectors will pro-vide new rms with an incentive to enter the market In this context

13 The main problem with these measures is that they lose too much informationMontgomery and Wernerfelt (1988) try to overcome this problem by choosing a con-centric index represented by a set of dummies that account for the distribution of salesbetween more or less related businesses


entry is frequently seen as an error-correction mechanism that drivesmarket performance towards competitive outcomes (Geroski 1991)Intensity of entry is conditioned as much by future prot expecta-tions within the sector as by the presence of barriers that make entryof new competitors difcult14

The analysis of rm entry from this point of view presents somepeculiarities which should not be ignored Perhaps the most impor-tant is the difference in interests from the previous perspective beingmore focused on the entry process itself and not only on the conse-quences of entry for prots15

Following Geroski (1991) four types of models of entry are avail-able for this purpose

1 Models of incidence of entry are mainly interested in the marketconditions that facilitate entry and the way the new entrants mayovercome possible entry barriers or adverse reaction of incumbents

2 Models of market penetration center on the explanation of the marketshare acquired by entrants In this case a common model pio-neered by Orr (1974) conditions entry on the expected postentryprots and entry costs

3 Very often the previous models do not t very well when appliedto the data probably due to the existence of transitory shocks dif-ferently affecting incumbents and new entrants If this hypothesisholds prots will depend substantially on unobservable variablesand modeling entry would be a difcult and unsatisfactory exer-cise Thus autoregressive models of entry should be used

4 Finally selection models postulate that new entrants have only lim-ited room in the market For these models the relevant interactionsare between potential entrants which must ght to obtain newprotable opportunities

In these studies entry of new competitors in a market is pos-tulated to be an increasing function of entrant capabilities and futureprot opportunities in that market and decreasing in the barriers thatlimit entry of new rms16 Prot opportunities are not directly observ-

14 It is important to insist on the difculty of reaching equilibrium in this contextdue to the existence of a continuous process of rm turnover in the market The poten-tial to earn economic rents acts as an incentive to entry for new competitors which inturn promotes exit by some less efcient incumbents Therefore we should speak of netentry (Geroski 1995) However this problem is not relevant in the Spanish savings-bankmarket as exit is not found Therefore we omit its treatment in this paper

15 Logically entry is conditioned among other factors on future prot expectations16 A good collection of studies of these characteristics can be seen in Geroski and

Schwalbach (1991) or in the monographic 1995 issue of the International Journal of Indus-trial Organization


able and must be approximated from variables that reect competi-tion intensity within the market The relative competence of entrantsis usually measured through previous protability Entry barriers areinferred through some measure such as the minimum efcient size (asa proxy of scale economies) or concentration

Some of the models included under this perspective go a stepforward by considering the strategic aspects surrounding entry Thesemodels consider the entry decision not only as the consequence of asimple evaluation of the costs and prots to be obtained from a mar-ket but as the result of entrantsrsquo and incumbentsrsquo strategic behaviorIn the case of banking research efforts have made use of a Hotel-ling (1929) model An interesting example is found in Cabral andMajure (1993 1994)

Finally it is important to highlight that in the context of NSBsthe concept of entrant is more restricted than in the general case Onthe one hand banksrsquo expansion is always of a diversifying type thatis it is always carried out by banks that were already competing in thesector On the other there is no exit (except for merged savings bankswhose branch network is totally incorporated to the resulting bank)and therefore we cannot speak of entry and exit as linked processesbut only of entry

4 Model and Hypotheses

The approach followed in this research is similar to the one initiallyproposed by Lemelin (1982) and later adapted by Merino and Rodrotildeguez(1997)17 although with a different perspective in order to capture thepeculiarities of thinking in terms of new geographical markets insteadof new products It is important to highlight that our objective is notto nd the factors affecting the expansion process globally but onlythe (offensive) expansion coming from rms initially outside the mar-ket18 Our study attempts to identify common rm characteristics andmarket conditions that explain why some banks have chosen to widentheir traditional scope of operation through the opening of outlets innew markets19

17 Other studies that have used variants of this model are Montgomery and Hari-haran (1991) Barros (1995) and Ingham and Thompson (1995)

18 The reason for this approach is that the banks implementing a defensive strategyare different from the ones implementing an offensive strategy (only three NSBs belongto both groups) This would suggest that the factors determining expansion in originaland target markets are different and justify independent analyses of the two types ofstrategies Detailed information on the strategies implemented by all the NSBs includedin the sample is available from the authors on request

19 The process of mergers and acquisitions taking place after deregulation couldhave been used as a mechanism of expansion alternative to the opening of outlets


Therefore our interest focuses on estimating the probability thata rm having a given scope of operation will decide to extend itsactivities into a new market As we have seen a rm will decideon entry into the new market as long as it anticipates its survival inthe long run On the other hand we will assume that a rm decidesto enter a new market independently of the decisions adopted withrespect to other markets the survival of the new outlets being theonly guiding criterion20

Let Ykip be a dichotomous dependent variable dened as

Ykip

8gtlt

gt

1 if rm k initially operating inprovince i enters province p

0 otherwise

Lemelin (1982) states that the probability that a rm k initially oper-ating in the provincial market i decides to widen its activity towardsprovince p is given by

Pr[Ykip 1] f (Wk Xi Mp Zip)

where f is a function to be specied and

Wk is a vector that captures rm-k-specic characteristics Many ofthese characteristics are following the resource-based view of therm unobservable and allow the rm to earn sustainable rentsover timeX i is a vector that synthesizes structural characteristics of the marketin which the rm is initially operatingMp is a vector of structural characteristics of the target market Mar-ket attractiveness is captured through the variables included in thisvector mainly in terms of growth and prot opportunities (Lemelin1982)

in new markets This adds a complication to the entry analysis given the dif-culty of identifying the bank actually entering Our approach has been to focus on theentry that takes place through the opening of new branches in new markets and tocontrol for the involvement of savings banks in mergers and acquisitions as explainedin Section 6

20 Following Barros (1995) in modeling the decision to enter a market it should berecognized that when a bank designs its branch network it takes into account both itsown and competitorsrsquo network choices Nevertheless the difculty of estimating sucha complex model suggests its replacement by a partial equilibrium model in which itis accepted that banks take independent decisions in relation to their presence in eachof the different geographical markets


Zip is a vector of variables capturing the interrelation between mar-ket i and market p The underlying hypothesis is that the greater thesimilarity between origin and destination provinces the higher theprobability that the entity decides to diversify into that province

This model is especially well suited for our purposes given thatit provides us with a way of integrating the two perspectives analyzedin Section 3 On the one hand as argued in the industrial organiza-tion literature the focus is on entry processes by themselves and onthe diversication incentives and constraints provided by origin andobjective markets In this sense incentives and constraints may bedeemed to be summarized by the structural characteristics of marketsand the relationships between them On the other hand the modelalso gives the possibility of incorporating some of the relevant vari-ables suggested by the strategic management literature and frequentlyneglected by industrial economists As has been argued rm-specicvariables are also important for explaining entry decisions From someof the previously analyzed perspectives (eg agency theory or theresource based view) diversication decisions are fundamentally jus-tied in terms of the operation of such rm internal factors Althoughat least some of these variables are potentially unobservable (Godfreyand Hill 1995 Merino and Rodrotildeguez 1997) and therefore it is dif-cult to include them in the estimations the econometric procedurethat is used will allow us to test for their presence and to control theireffect The hypotheses to be tested in this research are related to thefour groups of variables previously identied21

41 Firm Characteristics

The rst set of hypotheses refer to rm characteristics (Wk ) that mea-sure internal resources and might enable a bank to earn economicrents in new markets Resource constraints can be a determining aspectof entry22 The literature usually considers that the size of the entityinuences diversication decisions (Ingham and Thompson 1994Merino and Rodrotildeguez 1997) We also consider that more protable

21 Throughout the analysis it will be assumed that savings banks behave as ifthey were prot maximizers In this regard Hill and Waterson (1983) provide a frame-work in which under a set of sufcient conditions positive prot expectations are themotivating force that induces entry in both the entrepreneurial and the labor-managedindustries In their model entry leads to a stable and unique equilibrium in which zeroprots are obtained

22 This problem is especially relevant among savings banks given their limitedability to access equity capital markets and restrictions that prevent them from usingother sources of funding


companies are more likely to enter a market More competent rmsmay use internally generated funds in order to nance their expan-sion On the other hand they have fewer difculties in surmountingentry barriers than worse-performing rms (Jayaratne and Strahan1998 Coterill and Haller 1992)23 Therefore this reasoning suggeststhe rst set of hypotheses

Hypothesis 1a Firm size is expected to have a positive effect on theprobability of entry

Hypothesis 1b Protability is expected to have a positive effect onthe probability of entry

When we consider multiproduct rms it is important to takeinto account that some resources may be used simultaneously to elab-orate two or more products or to participate in two or more marketsIn these cases economies of scope may be obtained if joint produc-tion allows the sharing of costs within the rm because of the pres-ence of market imperfections (nonseparable cost asset specicitiesetc) Thus rms may decide to use this excess of capacity internallyinstead of selling it in the market given its availability (freely or ata reduced additional costs) to other market units (Gimeno and Woo1999)

In the case of banking perhaps a more appropriate concept isthat of network economies Sharing of cost in banking rms may resultfrom the simultaneous use of centralized resources (central buildingsbrand names etc) across branch networks But it may also arise froma more efcient use of the resources available within the branch net-work For example some banks with an excess of deposits have incen-tives to open new branches in order to lend those resources in ageographically different market at higher rates that those offered inthe money market In these circumstances growth may be under-stood as a means of internalizing some activities in order achieve theeconomies of scope derived from the possession of slack resourcesgiving rise to a multimarket rm24 Thus

23 Jayaratne and Strahan (1998) in their study of the US banking system ndsupport for the hypothesis that banking deregulation fostered a process of selection(they call it the selection hypothesis) Better-performing banks (in terms of protabilityand loan losses) grew faster and expanded at the expense of high-cost low-prot banksCoterill and Haller (1992) use return on equity as a proxy for the relative competenceof entrants They nd that more capable rms are more likely to enter a new market

24 The possession of slack resources has been seen in the management literatureas a facilitator of strategic behavior Viewing slack ldquoas a resource in excess of what isrequired for the normal efcient operation of an organizationrdquo Bourgeois (1981 p 35)points out that ldquoas slack is generated the organization can literally afford to exper-iment with new strategies by for example introducing new products entering newmarkets rdquo


Hypothesis 1c The possibility of making use of slack resources isexpected to favor network economies having a positive effect on theprobability of entry

Finally we consider that the original scope of operation of a rmmay condition its expansion decisions Those rms operating in sev-eral markets (and that historically have chosen to cover a larger terri-tory) will probably tend to expand more due to the higher expectedprots from operation in any additional market This is due to thefact that the rms covering a smaller number of markets need to holdmore equity capital than the ones operating in several markets giventhat they are not as diversied (Economides et al 1996)25

Hypothesis 1d A larger original scope of operation is expected tohave a positive effect on the probability of entry

42 Original-Market Characteristics

The second group of hypothesis in the model is related to the struc-tural characteristics of the markets in which the rm is initially oper-ating (X i) At least at the beginning of the diversication activitiesthese markets constituted the main source of revenues and prots ofa bank and therefore its survival fundamentally depended on themGiven the commitment of the rm to its core markets the competitiveconditions may be an important determinant of expansion decisionsIn this context when rivalry in the home market is high we shouldexpect that the rm would try to secure its survival by looking for newmarkets As Schoenecker and Cooper (1998) point out the threat thata new product presents to the central activities of a rm will inuencethe moment at which this rm will decide to enter a new market Ithas been empirically proved (Mitchell 1989 Cooper and Smith 1992)that rms with threatened core markets entered new markets earlierthan rms whose core markets were safe The reason is that whenthe new product is an alternative to the existing one the rm pos-sesses incentives to offer it to its existing customers in order to retainthem

Thus rivalry in the core market may be an indicator of the extentto which the central activities of the rm are threatened and the neces-sity to look for new locations in which to develop these activitiesTherefore our second hypothesis is the following

25 As Economides et al (1996) point out in a context of monopolistic competitiona large bank (dened as one that holds branches in more than one market) will haveceteris paribus higher expected prots than a small bank (in their denition one withoperations in just one market) This is due to the fact that small banks need to holdmore equity capital than large banks (per branch) because they are not as diversied


Hypothesis 2 A greater rivalry in the core geographical markets ofthe rm is expected to have a positive effect on the probability ofentry into new markets

43 Target-Market Characteristics

The hypotheses on target-market characteristics (Mp) can be dividedinto supply- and demand-side hypotheses On the supply side weconsider market concentration Following Coterill and Haller (1992)the predicted effect of concentration on entry may differ in differ-ent approaches Higher concentration could be an indicator of weakrivalry in the industry and therefore higher prot expectations forentrants However more concentrated markets are more prone to theerection of strategic or conduct-based entry barriers which could bea disincentive to potential entrants Finally according to contestable-markets theory and Chicago-school reasoning concentration shouldnot have any effect on entry

Hypothesis 3a The initial concentration in the target market may havea positive a negative or no effect on the probability of entry

Entry may be also conditioned by potential rivalry in the targetmarket (Shermand and Willet 1967) On the one hand under perfectinformation (all potential entrants possess information about the exis-tence of an opportunity in the market) each potential entrant sees thatthe probability of another rm entering the market increases with thenumber of potential entrants having a negative effect on entry Simi-larly when the number of potential entrants is large it is more neces-sary for the incumbents to punish the entrants in order to disciplinethem and avoid further entries Thus incumbents are more prone toact in accordance to the Sylos (1962) postulate26 This would meanthat per-branch expected prots of a new entrant (and therefore theprobability of entry) would be reduced

However in a context of imperfect information this negativeeffect would be counteracted by the increase in the number of poten-tial entrants having information about the entry opportunity As Sher-mand and Willet (1967 p 403 footnote 9) note ldquoif an increase in Nwould increase the probability of a potential entrantrsquos having informa-tion about the opportunity it would increase the likelihood of entryrdquoAs a consequence the net effect would be unclear Therefore

26 As Modigliani (1958 p 217) points out the postulate is Sylosrsquos assumption ldquothatpotential entrants behave as though they expected existing rms to adopt the policymost unfavorable to them namely the policy of maintaining output while reducing theprice (or accepting reductions) to the extent required to enforce such an output policyrdquo


Hypothesis 3b A greater potential rivalry in the target market mayhave a positive a negative or no effect on the probability ofentry

On the demand side concentration of population and intensityof demand are variables frequently used in the literature studying thenumber of rms in a market More concentrated population enablesrms to serve a larger proportion of the demand and higher intensityof demand will lead to higher expected prots in the market (Coterilland Haller 1992 Barros 1995) With respect to market growth Coterilland Haller (1992) consider that it has a double effect on entry conductWhereas a higher rate of market growth can lead to higher expectedprots and easier entry the attractiveness of entry will be reducedby the larger queue of potential entrants Nevertheless the availableempirical evidence has generally implied that the rst effect (increasedattractiveness) outweighs the second (higher potential competition)

Hypothesis 3c A higher concentration of population is expected tohave a positive effect on the probability of entry

Hypothesis 3d A higher demand is expected to have a positive effecton the probability of entry

Hypothesis 3e A higher market growth rate is expected to have apositive effect on the probability of entry

44 Relation between Original- and Target-MarketCharacteristics

Finally among this last set of hypotheses (Zip) we consider that arm will also direct itself towards those markets adjacent to its orig-inal scope of operation Proximity is a measure of potential entrantcapability (Coterill and Haller 1992) Entrants with nearer headquar-ters are more able to better control activities may have better knowl-edge of the markets closer to them and may be known to customersIn a slightly different context however Montgomery and Wernerfelt(1988 p 625) stress that ldquoif a rm diversies it will transfer excessfactors to the closest markets it can enter If excess capacity remainsit will enter markets even farther aeld until marginal rents becomesubnormalrdquo27

Hypothesis 4 Proximity is expected to have a positive effect on theprobability of entry

27 Montgomery and Wernerfelt dene the closest market as the one in which therm will obtain the highest rents If those highest rents are obtained in those mar-kets where the rm has better control and knowledge our idea of proximity may beunderstood as similar to the one used by them


TABLE VHypotheses and Expected Effects

Expected Effect onVariable Probability of Entry Hypothesis

Firm Characteristics

Size (k) size 1aProtability (k ) pro 1bMoney market position (k) money 1cScope of operation (k) scop1 1e

scop2

Original-Market Characteristics

Rivalry in the core market (i) coreh 2

Target-Market Characteristics

Target market concentration (p) objeh 0 3aPotential rivalry (p) numsb 0 3bDensity of population (p) densi 3cIntensity of demand (p) depha 3dRelative market growth (p) mgrow 3e

Relation Between Original and Target Market

Proximity (ip) proxi 4

Table V summarizes the hypotheses considered in this researchas well as the expected effect on the probability of entry

5 Dependent Variable and Econometric Methods

Our interest in studying the entry determinants of the Spanish savingsbanks leads us to consider a dichotomous dependent variable It takesa value of 1 for the provincial market in which a savings bank hasstarted its operations during the considered period and 0 otherwiseIt is necessary to note that some banks open one or two branches inthe main cities of the country The objective of these new branches ismore to establish a presence and build a reputation than to engage inretail banking This fact is also taken into account in constructing thedependent variable In this research a savings bank is considered tooperate in a province when it has at least 1 of the bank branchesin that province Therefore an entry is recorded when the number ofbranches of an entity in a market exceeds the 1 threshold This limitmight be considered arbitrary however it may be seen as a guarantee


that the bank takes its retail activities in a market seriously (Geroski1991)28

Given the character of our dependent variable two economet-ric strategies are used in this paper First a binomial logit modelis estimated29 In the absence of unobservable rm-specic effectsit is demonstrated that the maximization of the corresponding like-lihood function provides us with consistent and efcient estimates ofthe parameters However as we have seen some of the theories thatlie at the heart of the explanation of diversication decisions includethe operation of factors difcult to measure given the availabilityof data The resource-based view of the rm provides us with anexample Its emphasis on internal specic resources subject to hightransaction costs is frequently difcult to test in practical terms Sta-tistical models that analyze cross-sectional data tend to ignore indi-vidual differences and treat the aggregate of the individual effect andthe omitted-variable effect as a pure chance event The result is thatthe heterogeneity among individuals who are homogeneous in theirobserved characteristics is frequently not considered in the analysis(Hsiao 1986) An important benet of pooling time series and cross-section data is precisely the possibility of treating the unobservedindividual specic effects which may be correlated with other vari-ables included in the model (Hausman and Taylor 1981) When suchunobservable rm-specic effects are in operation the logit modelgives unsatisfactory results with regard to consistency (Hsiao 1986)and therefore an alternative approximation to the problem is required

Therefore a conditional logit model will be applied to the datato control for the possibility of the presence of unobservable xedeffects30 The model determines the probability of any entry pattern

28 When we modify this 1 limit and consider that a savings bank is really com-peting in the market when it has 2 or 5 of the branches the results remain similar

29 According to it the probability that a savings bank follows a given diversicationpattern is expressed in the following way

prob(yk )p

Pp 1

exp[(acutek b xkp)ykp ]

1 exp[acutek b xkp]

where yk is a vector of ones and zeros that represents the diversication pattern of sav-ings bank k acute and b are parameters and ykp xkp are the variables previously identiedin the model Note that the use of the subindex i is not necessary given that originalmarket characteristics may be subsumed in k

30 Chamberlain (1980) shows how to estimate a logit model in a context inwhich xed effects are present and panel data are available The approximation stemsfrom considering the probability function conditioned on a sufcient statistic thatin this case is given by S p ykp The resulting function is in the form of a conditional logit


among the p provincial markets towards which each rm may diver-sify conditioned on the number of entries effectively undertaken byeach rm The transformation realized is analogous to that for thelinear case and eliminates the unobservable xed effects obtainingconsistent and efcient estimates The contribution to the likelihoodfunction of the rm with a total number of entries that equals thepotential number of diversication options is 1 and therefore the esti-mation procedure does not depend on the rms The same happenswith the rms with no entries These rms may be eliminated fromthe sample without any variation in the value of the estimates

This econometric strategy has recently been used in the strategicmanagement literature (Merino and Rodrotildeguez 1997) to study diver-sication decisions Our approach differs in that we consider mar-kets instead of the time or product dimension in order to obtain therepeated observations necessary to apply the method

The main inconvenience of this approach is that together withthe elimination of the xed effects the estimation procedure also elim-inates the coefcients of all the explanatory variables that do not showwithin-group variability However it gives the possibility of testingfor potential misspecication in the logit model through a Hausman(1978) test The test allows checking for the presence the unobservedrm-specic xed effects mentioned above Under the null hypothesisof no xed effects the logit estimators are consistent and asymptot-ically efcient but they are inefcient under the alternative Cham-berlainrsquos xed-effect estimates are consistent under both the null andthe alternative but inefcient under the null hypothesis Hausmanconstructs a statistic based on the difference of the two types of esti-mates31 Under the null hypothesis Hausmanrsquos statistic is a consistentestimate of zero and is asymptotically distributed as a chi-square withk degrees of freedom (where k is the dimension of the slope vector b )

model (McFadden 1974) and the considered probability is expressed in the fol-lowing way

prob yk

p

Sp 1

ykp

exp b

p

Sp 1

xkp ykp

Sbk Bk

exp b

p

Sp 1

xkpdkp

where Bk dk (dk1 dkp) dkp 1 or 0 and S p dkp S p ykp dk is a vectorof zeros and ones indicating diversication patterns with the same number of entriesas the actual one and Bk is the set of all possible vectors for rm k (Merino andRodrotildeguez 1997)

31 The Hausman test statistic is m q [AtildeV(q)] 1q where q is the difference betweenthe k common parameters obtained from the estimation of both the traditional and thexed-effects logit models and AtildeV(q) is a consistent estimate of var (q)


6 Sample and Variable Measurement

The data available to the analysis belong to the period between 1986and 1996 Although the total elimination of restrictions did not takeplace until 1989 a few years before that date NSBs were authorized tocompete within their own autonomous region and to establish up tothree branches in the main cities As a consequence some banks hadopened a small number of branches before that date This led us toextend the time of the analysis back to 1986 As we mentioned beforein order to avoid distortions we only consider that a bank is presentin a market when it has at least 1 of the branches in that market32

In 1986 the Spanish NSB market consisted of 77 banks whichafter the mergers that took place at the beginning of the 1990s werereduced to 50 We treated merged savings banks as having existed fromthe beginning Therefore the number and location of the branches of abank affected by a merger will be equal to the arithmetic sum of thebranches of the banks integrated in the new rm

Thus our sample consists of the observations corresponding tothe 50 NSBs that existed in Spain in 1996 each one of which couldoperate in 50 provincial markets This means a total of 2500 NSB-province pairs of which 127 correspond to the original markets of theNSBs We therefore have 2373 possibilities of entry into new marketsof which 150 (632) actually occurred

To test the predictions of the hypotheses related to rm charac-teristics six variables were created33 Firm size (size Hypothesis 1a)is measured as equity capital (sum of capital rotation fund reservessubordinated nancing and retained earnings)34 The effect of sav-ings banksrsquo protability (pro t Hypothesis 1b) on entry probabil-ity is measured as the ratio of net prots to total assets In bothcases (size and pro t) the values correspond to the average overthe period 1986ndash1988 in order to smooth abnormal uctuations Slackresources are measured as the proportion of total assets invested in themoney market (money Hypothesis 1c) The underlying hypothesis isthat those with an excess of deposits will have additional incentives

32 The number of branches eliminated by the application of this criterion is 120(11 of the total number of branches in 1986) mostly situated in Madrid (55) Barcelona(31) Valencia (12) and Zaragoza (7) We only eliminate 15 branches (014) not situatedin the four mentioned provinces

33 In the Appendix the variables used in the analyses are dened in more detail34 Although other measures could be used all of them are highly correlated As

an example the correlation between equity and assets during the analyzed period is097 The reasons to choose equity are several First CBs and savings banks must meetstrict capital requirements Second and more important given that savings banks aremutuals they cannot raise new equity capital and therefore must nance expansionwith internal funds or subordinate debt


to open new branches and benet from economies of scope (assum-ing the nonseparability of the credit and deposit markets) As in thecase of size and pro t the average during the years 1986ndash1988 wasconsidered

To test Hypothesis 1d three types of NSBs are identied one-province banks banks operating in several provinces but not through-out an autonomous region and banks competing in an entireautonomous region or on a larger scale Accordingly two dummyvariables were created scop1 and scop2 scop1 takes a value of 1when the scope of operation of the entity at the beginning of theperiod (1986) comprised several provinces but did not exhaust anautonomous region On the other hand scop2 equals 1 when the sav-ings bank was operating in at least all the provinces of an autonomousregion in 1986

As has been pointed out in Section 5 entry is also explainedthrough the consideration of the structural characteristics of originand target markets An average Herndahl (coreh Hypothesis 2)during the period 1986ndash1988 was constructed to measure concentra-tion in the market in which each bank was initially operating To cal-culate this a province-average Herndahl for the years 1986ndash1988 wasrst developed using the number of branches as a proxy for marketshare Then the core-market Herndahl was worked out by multi-plying each single Herndahl of the provinces in which the bank wasoperating in 1986 by the relative importance of the province for thatbank (the average number of branches during 1986ndash1988 was used tomeasure the importance of the province)

Similarly concentration in the target market (objeh Hypothesis3a) was measured through an average province Herndahlcalculated for the period 1986ndash1988 Both CBs and NSBs were takeninto account to construct this index With respect to Hypothesis 3bthe number of banks operating in the markets adjacent to every poten-tial target market was used as a proxy for potential rivalry (numsb)The 1 limit was again used in order to decide whether a savingsbank was operating in a province or not Population concentration(Hypothesis 3c) was included through the consideration of the densityof population (densi) in every potential target market (inhabitants persquare kilometer) On the other hand the total amount of deposits perinhabitant in the banking system (depha) proxies for the intensity ofdemand in the target market (Hypothesis 3d) Finally market growth(mgrow Hypothesis 3e) was measured by the relative increase in thetotal volume (CBs and NSBs) of deposits of the province during theperiod 1986ndash1988


TABLE VIDescriptive Statistics

Explanatory StandardVariable Mean Deviation Min Max

size (k) 13533 24588 0226 155884pro (k) 1189 0451 0031 2076money (k) 0088 0059 0025 0274scop1 (k) 0259 0438 0000 1000scop2 (k) 0249 0432 0000 1000coreh (i) 0129 0046 0065 0250objeh (p) 0147 0049 0065 0289numsb (p) 5323 3029 0000 13000densi (p) 0106 0137 0009 0619depha (p) 0539 0170 0252 0907mgrow (p) 0208 0112 0028 0667proxi (ip) 0111 0314 0000 1000merge 0214 0410 0000 1000

In relation to the variables capturing the interrelation betweenorigin and target market (Hypothesis 4) a proximity variable (proxi)was created The variable takes a value of 1 when the target marketinto which a bank may enter is adjacent to the province(s) formingthe original market in which the bank was operating in 1986 and 0otherwise

Finally given that approximately one-third of the Spanish NSBshave been involved in mergers it is reasonable to assume that theentities affected by this situation have chosen a growth formula dif-ferent from de novo entry This circumstance together with the neces-sity of concentrating rm resources on the solution of the problemsderived from merging leads us to control for these processes throughthe creation of a dummy variable Thus merge takes a value of 1when the NSB has been involved in at least one merger during theperiod considered According to this a negative relationship betweenmerger activity and de novo expansion is expected

A detailed measurement of the variables used in the analysis ispresented in the Appendix Tables VI and VII complete this informa-tion with the sample descriptive statistics and the correlation betweenthe variables

7 Analysis of Results

Table VIII reports logit estimation results for the proposed modelColumn (1) contains all the independent variables and is signicantat the 1 level The empirical model correctly predicts 9964 of the


TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0


TABLE VIIILogit Analysis of De Novo Entry by Spanish NSBs

Explanatory Logit Logit Fixed EffectsVariable (1) (2) (3)

Constant 5905 0389 mdash( 5744) ( 0710)

size (k) 0083 mdash mdash(11188)

pro (k) 0675 mdash mdash(1887)

money (k) 6216 mdash mdash(2120)

scop1 (k) 1376 mdash mdash(2747)


coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)

Log likelihood 219422 430690 137505Pseudo R2 0607 0230 0339Number of observations 2373 2373 1178Hausman test mdash mdash 7335 (1 vs 3)

17206 (2 vs 3)

Coefcient statistically signicant at 1 5 or 10 level respectively t-ratios in parenthesis

zeros and 6133 of the ones appearing in the data From the point ofview of savings-bank characteristics the entities more likely to diver-sify towards new markets are the ones with larger size and higherprotability that have not been involved in mergers On the otherhand a larger initial scope of operation and a higher position in themoney market are also positively related to the probability of entry asexpected However the degree of rivalry in the core market in which


the rm was initially operating does not provide it with incentives tolook for new markets

With respect to the target-market hypothesis the more concen-trated the market is the less the probability that a rm enters that mar-ket Therefore target-market concentration does have a negative effecton entry This result suggest that savings banks show a preferencetowards less concentrated markets and offers support for the hypoth-esis that in more concentrated markets incumbents erect strategic orconduct-based entry barriers A similar conclusion may be extractedfrom the analysis of the variable potential rivalry The negative signof the coefcient gives support for the hypothesis that every potentialentrant expects the probability of another rm entering the market toincrease with the number of potential entrants reducing the attrac-tiveness of the target market As mentioned before a larger num-ber of potential entrants would also mean that incumbents are moreinclined to discipline entrants and act in accordance with the Sylos(1962) postulate Taken jointly these two results highlight the impor-tance of strategic effects at entry As we have seen the probability ofentry decreases when entrants anticipate that target-market conditionsare such as to deter entry

A higher intensity of demand attracts new competitors into themarket as expected However density of population and relativemarket growth are not signicant at the normal levels35 Finally itis important to emphasize that potential-entrant proximity also hasa positive effect on the probability of entry Thus the model con-rms that the expansion is fundamentally directed towards adjacentprovinces in which branches are better controlled or the market isbetter known

If we set all the explanatory variable values at their means theprobability of observing at least one savings bank entering a typi-cal market during the period considered is 045 Firm and marketcharacteristics have a substantial effect on the probability of entryThe marginal effect of an increase by one standard deviation in thesize (protability) variable multiplies by 709 (135) the probability

35 Contrary to expectations this result is consistent with the evidence for the USbanking system (Berger et al 1999) As suggested by one of the reviewers if not allbanks are in all markets the markets under more competition may have lower interestrates in loans andor higher interest rates on deposits and thus the market size will belarger This may happen if each bank uses the same pricing policy in all its branchesAs the reviewer further points out this would suggest the necessity of using some sortof average-price control Unfortunately the unavailability of information about interestrates per provincial market prevents us from controlling for this effect This could alsobe a reason why the variable does not present a signicant sign


of one bank entering a typical market in comparison with the aver-age bank In the same way an increase by one standard deviation inthe potential-rivalry variable (concentration) has a negative 069 (053)effect on the average probability of a bankrsquos diversifying towards thatmarket On the other hand if the target market is adjacent to the orig-inal market of a typical savings bank then the probability of observ-ing its entry in the market is 2199 times higher than for the marketslocalized far from the entity

Column (2) shows the results of the estimation of a new logitmodel in which rm-specic variables have been omitted The modelis again globally signicant and the results of the estimation are sim-ilar to those of the complete model The main differences stem fromthe estimates accompanying potential- and current-rivalry variablesThis estimation will be used to illustrate the conclusions that maybe extracted from the econometric approach proposed by Merino andRodrotildeguez (1997) Although (unlike interindustry studies) our sampleis expected to be relatively homogeneous given the lack of data onother relevant variables at rm level (technology human resourcesetc) the presence of unobserved heterogeneity could be relevant Toexplore the importance of rm-specic heterogeneity in our modelwe estimated the xed-effects logit model proposed by Chamberlain(1980)

The results of the estimation of the conditional logit model incomparison with models (1) and (2) are shown in column (3)36 Savingsbanks with no or full diversication were dropped because their con-tribution to the conditioned likelihood function is 1 and its maximiza-tion does not depend on them This leaves us with 1178 observationsavailable to estimate the models As we see the values of the coef-cients in column (3) are not far from those of their counterparts

Similarly to other xed-panel-data models the logit xed-effectsmodel does not provide us with the estimation of the coefcientsof the within-group non-variant variables However it gives us thepossibility of testing for potential mis-specication in the logit model[columns (1) and (2)] through a Hausman (1978) test Under the nullhypothesis of nonxed effects the logit estimators are consistent andasymptotically efcient but they are inefcient under the alternativeChamberlainrsquos xed-effects estimates are consistent under both thenull and the alternative but inefcient under the null Hausman con-structs a statistic based on the difference of the two types of esti-mates Under the null hypothesis Hausmanrsquos statistic is a consistent

36 Note that in our case the computational burden imposed by this method isvery high For a typical savings bank which has 49 possibilities of diversication andchooses 10 the number of possible combinations is 49 (1039)


estimate of zero and is asymptotically distributed as a chi-square withk degrees of freedom (where k is the dimension of the slope vector b )

The results of the test for the comparison between models (1) and(3) and between models (2) and (3) are also presented in Table VIII Inthe rst case the null hypothesis of nonxed effects cannot be statisti-cally rejected and therefore the estimates presented in column (1) areshown to be consistent and asymptotically efcient However whenmodels (2) and (3) are compared the result of the Hausman test dif-fers The exclusion of rm-specic variables that are in operation inour empirical model leads to the inconsistency of the estimates Inthis case the xed-effects estimates are the ones to be used giventhat they are the consistent and efcient ones This example illus-trates the potential misspecication bias that may stem from neglect-ing the importance of some rm-specic effects that as suggestedby the strategic-management literature may be in operation in entrymodels

8 Conclusions

This paper has presented a consistent analysis of the determinantsof entry in the Spanish savings-bank market during the period 1986ndash1996 Unlike other studies which are interested in the product dimen-sion of diversication our focus is on entry into new geographicalmarkets As we have previously seen liberalization has producedimportant changes in the structure of the markets where the savingsbanks compete In this context the conduct of rms has been affectednot only by structural conditions of the original and target marketsand by the rivalry they support but also by their own resources andcapabilities

Our research adds both methodological and theoretical contribu-tions to the existing literature On the methodological side we intro-duce the estimation procedure suggested by Merino and Rodrotildeguez(1997) in order to obtain consistent estimators If we omit relevantvariables in the specication of the entry model (which is usuallyunavoidable in the traditional models of diversication) the presenceof nonobservable rm effects may bias our estimators The maximiza-tion of the conditional likelihood function has allowed us to infer thatour estimates are consistent even in the presence of unobserved rmeffects

From the theoretical point of view as argued by the industrial-organization literature the focus is on entry processes in themselvesand on the diversication incentives and constraints provided by ori-gin and target markets In this sense incentives and constraints may


be deemed to be summarized by the structural characteristics of mar-kets and the relationships between them On the other the modelalso gives the possibility of incorporating some of the relevant vari-ables suggested by the strategic-management literature and frequentlyneglected by industrial economists As has been argued rm-specicvariables are also important for explaining entry decisions

In accordance with previous research our results show that rm-specic characteristics (the capabilities of potential entrants) are signif-icantly related to diversication (entry) decisions Entry into provincialmarkets is signicantly and positively related to resource availabilityrm protability and the size of the initial scope of operation Thisresult highlights the importance of the characteristics of the queue ofpotential entrants in explaining entry patterns in the Spanish savings-bank market On the other hand the involvement in mergers is seenas an alternative to entry through branching expansion decreasingthe probability of de novo entry By contrast the degree of rivalry inthe core market does not provide rms with incentives to search fornew markets

In relation to target-market characteristics density of populationand relative market growth do not seem to have any inuence onthe conduct of banks Concentration and potential rivalry do havea negative effect on the probability of entering a market suggestingthe importance of the strategic interactions that take place betweenentrants and incumbents and within the group of entrants Finally ahigher intensity of demand serves to attract new competitors into themarket

Proximity also emerges as a very important determining factorin explaining entry patterns across savings banks Savings banks tendto direct themselves towards closer markets of which they have abetter knowledge in which they may be known and in which theymay better control their activities

Our results seem to be in accordance with previous analysisof data from the US and Europe where branching deregulation hasalso taken place They closely agree with the selection hypothesisevidenced for the US banking sector (Jayaratne and Strahan 1998)Expansion seems to be led by more competent rms and is carried outat the expense of the less competent ones If so efciency improve-ments should have stemmed from the elimination of entry restric-tions and the consequent expansion process This evidence also agreeswith the conclusions of the strategic model developed by Cabral andMajure (1993) in order to analyze branching in the Portuguese bank-ing system As in their model efciency arguments seem to be at theheart of entry decisions


Two important conclusions for the evolution of the effects ofbranching deregulation in Europe and the US emerge from the anal-ysis First it seems that entry in new geographical markets has beenimpeded by the strategic interactions between entrants and incum-bents Incumbents located in more concentrated markets have beenable to deter entry at least in the case of the less favored entrantsEntry deterrence could have been undertaken through expansion ofincumbents in their home markets Cabral and Majure (1993) showhow customer loyalty is a very important promoter of incumbentexpansion Given the structure of the market before branching deregu-lation took place it seems plausible that incumbents could have devel-oped this loyalty in their original markets What seems clear from theanalysis is that further consolidation (more concentration) in the mar-ket would discourage new diversication decisions limiting the effectof the elimination of legal entry barriers

The second implication is that as shown in the analysis sav-ings banks exhibit a preference for closer locations when expand-ing This effect is also found in the case of the US (McLaughlin1995) where banks show a preference for intraregional over inter-regional expansion This result is important as the search for closelocations may undermine the effects of deregulation and its poten-tial benets in terms of diversication of bankrsquos assets and liabilities(McLaughlin 1995) at least in the short run It also casts some doubton the effect of the measures on the integration of the banking sys-tems of the EU and the attainment of consumer benets Although theintroduction of new information technologies seems to have reducedthe importance of location on banking competition (Moore 1995) thisincremental pattern of expansion may have lessened the benets ofbranching deregulation namely the reduction of loan losses non-interest expenses and loan rates (Jayaratne and Strahan 1997) andincreases in deposit interest rates (Hannan and Prager 1998)

Appendix Description of VariablesHere the variables used in the paper and the way of calculating eachof them are detailed Monetary variables are expressed in constant 1986pesetas The data for the analysis come from the following sources Con-federacion Espanola de Cajas de Ahorros (CECA) Boletotilden Estadotildestico delBanco de Espana and Anuario El Paotildes

A1 Firm Characteristics

Size (size) Equity average value per entity for 1986ndash1988 deated bythe Spanish ination index (IPC) Equity consists of capital rotationfund reserves subordinated nancing and retained earnings


Protability (pro ) Savings-bank protability (net prot divided byassets average 1986ndash1988Scope of operation 1 (scop1) Dummy variable that equals 1 if in 1986the scope of operation of the bank comprised several provinces butnot an entire autonomous regionScope of operation 2 (scop2) Dummy variable that equals 1 if in 1986the savings bank is operating at least in all the provinces of anautonomous region or has a larger scopeMerger (merge) Dummy variable that equals 1 if a savings bank hasbeen involved in one or more mergers during the period 1986ndash1996and 0 in any other caseMoney-market position (money) Net balance (as a lender) of a savingsbank in the money market (1986ndash1988 average) normalized by theaverage assets of the bank

A2 Origin-Market Characteristics

Concentration (coreh) Average Herndahl during the period 1986ndash1988 weighted by the relative importance of each market for thebank To calculate it a province-average Herndahl for the years1986ndash1988 was rst developed using the number of branches asa proxy for market share Then the core-market Herndahl wasworked out by multiplying each single Herndahl of the provincesin which the bank was operating in 1986 by the relative impor-tance of the province for the entity under observation The averagenumber of branches during 1986ndash1988 was used to measure theimportance of the province for the entity

A3 Target-Market Characteristics

Concentration (objeh) Average Herndahl (1986ndash1988) in each mar-ket calculated from the number of branches It refers to both CBand NSB branchesPotential rivalry (numsb) Number of savings banks operating in themarkets adjacent to every potential target market A 1 limit wasused in order to decide whether a savings bank was operating in aprovince or notDensity (densi) Number of inhabitants per square kilometer in theprovinceIntensity of demand (depha) Total deposits per inhabitant (millionsof pesetas)Market growth (mgrow) Relative increase in the total volume ofdeposits in the province during the period 1986ndash1988 calculatedfor the total banking system (CBs and savings banks)


A4 Relationship between Firm Characteristics andTarget Industry Characteristics (Zkp)

Proximity (proxi) Dummy variable that equals 1 if the provincetowards which an entity could direct itself is adjacent to the pro-vince(s) in which the entity has its original market and 0 in anyother case

References

Akella SR and SI Greenbaum 1988 ldquoSavings and Loan Ownership Structure andExpense Preferencerdquo Journal of Banking and Finance 12 419ndash437

and 1995 ldquoSavings and Loan Ownership Structure and Expense PreferenceReplyrdquo Journal of Banking and Finance 19 171ndash172

Barney JB 1986 ldquoStrategic Factor Markets Expectations Luck and Business StrategyrdquoManagement Science 42 1231ndash1241

1991 ldquoFirm Resources and Sustained Competitive Advantagerdquo Journal of Man-agement 17 99ndash120

Barros PP 1995 ldquoPost-entry Expansion in Banking The Case of Portugalrdquo InternationalJournal of Industrial Organization 13 593ndash611

Berger A S Bonime LG Goldberg and LJ White 1999 ldquoThe Dynamics of Mar-ket Entry The Effects of Mergers and Acquisitions on De Novo Entry and SmallBusiness Lending in the Banking Industryrdquo Discussion Paper 1999ndash41 Finance andEconomics Discussion Series Federal Reserve Board

Bourgeois LJ 1981 ldquoOn the Measurement of Organizational Slackrdquo Academy of Man-agement Review 6 (1) 29ndash39

Cabral L and R Majure 1993 ldquoA Model of Branching with an Application to Por-tuguese Bankingrdquo WP 5-93 Banco de Portugal

and 1994 ldquoAn Empirical Analysis of Bank Branchingrdquo in The EmpiricalAnalysis of Industrial Organisation London CEPR

Chamberlain G 1980 ldquoAnalysis of Covariance with Qualitative Datardquo Review of Eco-nomic Studies 47 225ndash238

Cooper A and C Smith 1992 ldquoHow Established Firms Respond to Threatening Tech-nologiesrdquo Academy of Management Executives 6 55ndash70

Coterill RW and LE Haller 1992 ldquoBarrier and Queue Effects A Study of LeadingUS Supermarket Chain Entry Patternsrdquo The Journal of Industrial Economics XL (4)427ndash440

Economides N RG Hubbards and D Palia 1996 ldquoThe Political Economy of Branch-ing Restrictions and Deposit Insurance A Model of Monopolistic Competition amongSmall and Large Banksrdquo Journal of Law and Economics XXXIX (October) 667ndash704

Edwards FR 1977 ldquoManagerial Objectives in Regulated Industries Expense Prefer-ence Behaviour in Bankingrdquo Journal of Political Economy 85 (1) 147ndash162

Geroski PA 1991 Market Dynamics and Entry Oxford Blackwell 1995 ldquoWhat Do We Know about Entryrdquo International Journal of Industrial Organi-

zation 13 421ndash440and J Schwalbach eds 1991 Entry and Market Contestability Cambridge UK

BlackwellGhoshal S 1987 ldquoGlobal Strategy An Organizing Frameworkrdquo Strategic Management

Journal 8 425ndash440


Gimeno J and CY Woo 1996 ldquoHypercompetition in Multimarket Environment TheRole of Strategic Similarity and Multimarket Contact in Competitive De-escalationrdquoOrganization Science 7 322ndash341

and 1999 ldquoMultimarket Contact Economies of Scope and Firm Perfor-mancerdquo Academy of Management Journal 42 (3) 239ndash259

Godfrey PC and CW Hill 1995 ldquoThe problem of Unobservables in Strategic Manage-ment Researchrdquo Strategic Management Journal 16 519ndash533

Grant RM 1995 Contemporary Strategy Analysis 2nd ed Oxford BlackwellHannan TH 1979 ldquoExpense-Preference Behavior in Banking A Reexaminationrdquo Jour-

nal of Political Economy 87 (4) 891ndash895and RA Prager 1998 ldquoThe Relaxation of Entry Barriers in the Banking Industry

An Empirical Investigationrdquo Journal of Financial Services Research 14 (3) 171ndash188Hausman JA 1978 ldquoSpecication test in econometricsrdquo Econometrica 46 (6)

1251ndash1271and WE Taylor 1981 ldquoPanel Data and Unobservable Individual Effectsrdquo Econo-

metrica 49 (6)Hill M and M Waterson 1983 ldquoLabor-Managed Cournot Oligopoly and Industry Out-

putrdquo Journal of Comparative Economics 7 43ndash51Hitt MA RE Hoskisson and H Kim 1997 ldquoInternational Diversication Effects on

Innovation and Firm Performance in Product-Diversied Firmsrdquo Academy of Man-agement Journal 40 767ndash798

Hotelling H 1929 ldquoStability in Competitionrdquo Economic Journal 39 41ndash57Hsiao C 1986 Analysis of Panel Data Cambridge UK Cambridge University PressIngham H and S Thompson 1994 ldquoWholly Owned vs Collaborative Ventures for

Diversifying Financial Servicesrdquo Strategic Management Journal 15 325ndash334and 1995 ldquoDeregulation Firm Capabilities and Diversifying Entry Deci-

sions The Case of Financial Servicesrdquo The Review of Economic and Statistics 77177ndash183

Jayaratne J and EP Strahan 1997 ldquoThe Benets of Branching Deregulationrdquo FRBNYEconomic Policy Review (December) pp 13ndash29

and 1998 ldquoEntry Restrictions Industry Evolution and Dynamic EfciencyEvidence from Commercial Bankingrdquo The Journal of Law and Economics XLI (April)239ndash273

Jensen MC 1986 ldquoAgency Costs of Free Cash-Flow Corporate Finance and TakeoversrdquoAmerican Economic Review 76 323ndash329

Karnani A and B Wernerfelt 1985 ldquoMultiple Point Competitionrdquo Strategic ManagementJournal 6 87ndash96

Krinsky I and H Thomas 1995 ldquoSavings and Loan Ownership Structure and ExpensePreferencerdquo Journal of Banking and Finance 19 165ndash170

Lemelin A 1982 ldquoRelatedness in the Patterns of Interindustry Diversicationrdquo TheReview of Economic and Statistics 64 646ndash657

Markides CC 1995 Diversication Refocusing and Economic Performance CambridgeMA The MIT Press

Mata J 1993 ldquoEntry and Type of Entrant Evidence from Portugalrdquo International Journalof Industrial Organization 11 101ndash122

McFadden D 1974 ldquoConditional Logit Analysis of Qualitative Choice Behaviorrdquo inP Zarembka ed Frontiers in Econometrics New York Academic Press

McLaughlin S 1995 ldquoThe Impact of Interstate Banking and Branching Reform Evi-dence from the Statesrdquo Current Issues in Economics and Finance 1 (2) 1ndash5

Merino F and D Rodrotildeguez 1997 ldquoA Consistent Analysis of Diversication Decisionswith Nonobservable Firm Effectsrdquo Strategic Management Journal 18 733ndash743


Mitchell W (1989) ldquoWhether and When Probability and Timing of Incumbentsrsquo Entryinto Emerging Industrial Subeldsrdquo Administrative Science Quarterly 34 208ndash230

Modigliani F 1958 ldquoNew Developments on the Oligopoly Frontrdquo Journal of PoliticalEconomy 66 215ndash232

Montgomery CA 1994 ldquoCorporate Diversicationrdquo Journal of Economic Perspectives 8163ndash178

and S Hariharan 1991 ldquoDiversied Expansion by Large Established Firmsrdquo Jour-nal of Economic Behavior and Organization 15 71ndash89

and B Wernerfelt 1988 ldquoDiversication Ricardian Rents and Tobinrsquos qrdquo RANDJournal of Economics 19 623ndash632

Moore R 1995 ldquoDoes Geographic Liberalisation Really Hurt Small Banksrdquo FinancialIndustry Studies (December) 1ndash13

Orr D 1974 ldquoThe Determinants of Entry A Study of the Canadian ManufacturingIndustriesrdquo Review of Economics and Statistics 56 58ndash66

Peteraf MA 1993 ldquoThe Cornerstones of Competitive Advantage A Resource-BasedViewrdquo Strategic Management Journal 14 179ndash191

Penrose ET 1959 The Theory of the Growth of the Firm Oxford BlackwellRoll R 1986 ldquoThe Hubris Hypothesis of Corporate Takeoversrdquo Journal of Business 69

197ndash216Rumelt RP 1974 Strategy Structure and Economic Performance Cambridge MA Har-

vard University Press 1982 ldquoDiversication Strategy and Protabilityrdquo Strategic Management Journal 3

359ndash369Schoenecker TS and AC Cooper 1998 ldquoThe Role of Firm Resources and Organisa-

tional Attributes in Determining Entry Timing A Cross Industry Studyrdquo StrategicManagement Journal 19 1127ndash1143

Shermand R and TD Willet 1967 ldquoPotential Entrants Discourage Entryrdquo Journal ofPolitical Economy 75 400ndash403

Sylos P 1962 Oligopoly and Technical Progress Cambridge MA Harvard UniversityPress

Verbrugge JA and JR Jahera 1981 ldquoExpense Preference Behavior in the Savings andLoan Industryrdquo Journal of Money Credit and Banking 13 (4) 465ndash476

Wernerfelt R 1984 ldquoA Resource-Based View of the Firmrdquo Strategic Management Journal5 171ndash180


Haller 1992 Ingham and Thompsom 1995 Mata 1993 Merino andRodrotildeguez 1997)

Following this emphasis the objective of this paper is to offerempirical evidence on the role of the factors that determine the pat-terns of geographical diversication in the Spanish noncooperativesavings-bank market This is an especially interesting sector in thatcompetition in the Spanish banking system has been tightly regu-lated until recently The existence of ceilings on interest rates on loansand deposits and the clear delimitation of the activities carried outby each nancial intermediary with the commercial banks special-ized in wholesale activities and the noncooperative savings banks inretail banking are two examples of this situation As a consequencecommercial banks and noncooperative savings banks have had littleleeway in the design of their competitive strategies

Deregulation affected commercial banks as much as noncoop-erative savings banks However the different legal and foundationalnature of the latter and the additional restrictions they had to supporthave produced more intense and relevant changes for them Until1989 the geographical scope of these entities was restricted to a local-ity province or region1 The elimination of this limitation and the sub-sequent expansion of various banks constitutes an ideal framework inwhich to perform our analysis

In this paper we analyze the behavior of Spanish noncoopera-tive savings banks after interregional banking was allowed in order topresent some reections on the factors that have affected their expan-sion decisions Our research attempts to study the reasons that haveprovided some banks with an incentive to choose a strategy of growthunderpinned by the widening of their geographical scope of operation(offensive expansion) in contrast with the ones that have preferredto consolidate their position in their traditional markets (defensiveexpansion)

Our contribution is threefold First unlike previous papers thathave concentrated on market (industrial organization) or rm (strate-gic management) variables we attempt to integrate both kinds in ajoint framework Second given that some of the constructs used tojustify diversication decisions in the strategic management literatureare unobservable the econometric procedure attempts to overcomethe consistency problems that stem from traditional estimation pro-cedures To achieve this a conditional logit model is estimated anda Hausman test is used to provide consistent and efcient estima-tors of the entry model We follow the approach initially proposed by

1 Whereas commercial banks have had full freedom of establishment since 1974savings banks lacked that ability until 1989 (RD 15821988 December 29th Law)









TA

BL

EI

Nu

mb

er

so

fB

ra

nc

he

s

De

po

sit

s

an

dL

oan

sfo

rC

om

me

rc

ial

Ban

ks

an

dN

on

co

op

er

ativ

eS

av

ing

sB

an

ks

Rel

ativ

eM

arke

tSh

are

Nu

mbe

rof

Dep

osit

sL

oans

Bra

nche

s(B

illio

nsof

pes

etas

)(B

illio

ns

ofp

eset

as)

Bra

nch

esD

epos

its

Loa

ns

Year

CB

NSB

CB

NSB

CB

NSB

CB

NSB

CB

NSB

CB

NSB

1980

132

318

588

722

93

714

726

22

033

6139

6634

7822

1985

166

0610

797

111

837

804

106

874

058

6139

5941

7228

1990

169

1713

720

150

2113

669

180

779

945

5545

5248

6535

1996

176

7416

094

223

0425

312

310

3521

218

5248

4852

5941

Sour

ce

Ban

kof

Spai

n














1986 11291 11037 254 221987 11707 416 11320 283 387 133 331988 12295 588 11797 477 498 111 411989 13135 840 12157 360 978 480 741990 13664 529 12402 245 1262 284 921991 13839 175 12298 104 1541 279 1111992 14107 268 12310 12 1797 256 1271993 14245 138 12294 16 1951 154 1371994 14578 333 12348 54 2230 279 1531995 14989 411 12509 161 2480 250 1651996 15851 862 12728 219 3123 643 197




Savings Bank


1 48 142 11 83 5 64 8 95 2 36 9 3 310 15 0 5gt 15 0 2Total 77 50Mean 20 55






1 12 02 18 13 10 14 6 135 1 126 2 117 0 78 1 29 0 110 0 2

Total 50 50

Mean 28 55




















































Ykip

8gtlt

gt


0 otherwise





















































scop2
















prob(yk )p

Pp 1


1 exp[acutek b xkp]








prob yk

p

Sp 1

ykp

exp b

p

Sp 1

xkp ykp

Sbk Bk

exp b

p

Sp 1

xkpdkp



























TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0




Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References






































































TA

BL

EI

Nu

mb

er

so

fB

ra

nc

he

s

De

po

sit

s

an

dL

oan

sfo

rC

om

me

rc

ial

Ban

ks

an

dN

on

co

op

er

ativ

eS

av

ing

sB

an

ks

Rel

ativ

eM

arke

tSh

are

Nu

mbe

rof

Dep

osit

sL

oans

Bra

nche

s(B

illio

nsof

pes

etas

)(B

illio

ns

ofp

eset

as)

Bra

nch

esD

epos

its

Loa

ns

Year

CB

NSB

CB

NSB

CB

NSB

CB

NSB

CB

NSB

CB

NSB

1980

132

318

588

722

93

714

726

22

033

6139

6634

7822

1985

166

0610

797

111

837

804

106

874

058

6139

5941

7228

1990

169

1713

720

150

2113

669

180

779

945

5545

5248

6535

1996

176

7416

094

223

0425

312

310

3521

218

5248

4852

5941

Sour

ce

Ban

kof

Spai

n














1986 11291 11037 254 221987 11707 416 11320 283 387 133 331988 12295 588 11797 477 498 111 411989 13135 840 12157 360 978 480 741990 13664 529 12402 245 1262 284 921991 13839 175 12298 104 1541 279 1111992 14107 268 12310 12 1797 256 1271993 14245 138 12294 16 1951 154 1371994 14578 333 12348 54 2230 279 1531995 14989 411 12509 161 2480 250 1651996 15851 862 12728 219 3123 643 197




Savings Bank


1 48 142 11 83 5 64 8 95 2 36 9 3 310 15 0 5gt 15 0 2Total 77 50Mean 20 55






1 12 02 18 13 10 14 6 135 1 126 2 117 0 78 1 29 0 110 0 2

Total 50 50

Mean 28 55




















































Ykip

8gtlt

gt


0 otherwise





















































scop2
















prob(yk )p

Pp 1


1 exp[acutek b xkp]








prob yk

p

Sp 1

ykp

exp b

p

Sp 1

xkp ykp

Sbk Bk

exp b

p

Sp 1

xkpdkp



























TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0




Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References











































































1986 11291 11037 254 221987 11707 416 11320 283 387 133 331988 12295 588 11797 477 498 111 411989 13135 840 12157 360 978 480 741990 13664 529 12402 245 1262 284 921991 13839 175 12298 104 1541 279 1111992 14107 268 12310 12 1797 256 1271993 14245 138 12294 16 1951 154 1371994 14578 333 12348 54 2230 279 1531995 14989 411 12509 161 2480 250 1651996 15851 862 12728 219 3123 643 197




Savings Bank


1 48 142 11 83 5 64 8 95 2 36 9 3 310 15 0 5gt 15 0 2Total 77 50Mean 20 55






1 12 02 18 13 10 14 6 135 1 126 2 117 0 78 1 29 0 110 0 2

Total 50 50

Mean 28 55




















































Ykip

8gtlt

gt


0 otherwise





















































scop2
















prob(yk )p

Pp 1


1 exp[acutek b xkp]








prob yk

p

Sp 1

ykp

exp b

p

Sp 1

xkp ykp

Sbk Bk

exp b

p

Sp 1

xkpdkp



























TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0




Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References

































































1 12 02 18 13 10 14 6 135 1 126 2 117 0 78 1 29 0 110 0 2

Total 50 50

Mean 28 55




















































Ykip

8gtlt

gt


0 otherwise





















































scop2
















prob(yk )p

Pp 1


1 exp[acutek b xkp]








prob yk

p

Sp 1

ykp

exp b

p

Sp 1

xkp ykp

Sbk Bk

exp b

p

Sp 1

xkpdkp



























TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0




Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References












































































































Ykip

8gtlt

gt


0 otherwise





















































scop2
















prob(yk )p

Pp 1


1 exp[acutek b xkp]








prob yk

p

Sp 1

ykp

exp b

p

Sp 1

xkp ykp

Sbk Bk

exp b

p

Sp 1

xkpdkp



























TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0




Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References












































































































scop2
















prob(yk )p

Pp 1


1 exp[acutek b xkp]








prob yk

p

Sp 1

ykp

exp b

p

Sp 1

xkp ykp

Sbk Bk

exp b

p

Sp 1

xkpdkp



























TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0




Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References




































































prob(yk )p

Pp 1


1 exp[acutek b xkp]








prob yk

p

Sp 1

ykp

exp b

p

Sp 1

xkp ykp

Sbk Bk

exp b

p

Sp 1

xkpdkp



























TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0




Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References



































































prob yk

p

Sp 1

ykp

exp b

p

Sp 1

xkp ykp

Sbk Bk

exp b

p

Sp 1

xkpdkp



























TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0




Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References






















































































TA

BL

EV

II

Co

rr

el

atio

ns

Be

twe

en

th

eE

xpl

an

ato

ry

Va

ria

bl

es

size

pro

m

on

eysc

op1

sco

p2co

reh

obj

ehn

um

sbd

ensi

dep

ha

mgr

ow

pro

xi(k

)(k

)(k

)(k

)(k

)(i

)(p

)(p

)(p

)(p

)(p

)(i

p)m

erge

size

(k)

100

00

338

014

70

000

025

40

188

001

80

001

001

90

015

000

40

112

030

9pr

o

(k)

033

81

000

023

10

052

044

90

237

001

70

009

001

10

009

000

40

002

016

4m

on

ey(k

)0

147

023

11

000

024

80

054

039

60

011

000

40

012

000

40

004

000

30

032

sco

p1(k

)0

000

005

20

248

100

00

340

011

80

002

000

30

000

000

10

001

001

50

464

sco

p2(k

)0

254

044

90

054

034

01

000

000

10

007

000

10

003

001

30

006

004

20

124

core

h(i

)0

188

023

70

396

011

80

001

100

00

037

001

40

025

000

00

001

007

10

104

obj

eh(p

)0

018

001

70

011

000

20

007

003

71

000

046

00

444

020

50

054

008

90

003

nu

msb

(p)

000

10

009

000

40

003

000

10

014

046

01

000

033

10

398

004

80

202

000

0d

ensi

(p)

001

90

011

001

20

000

000

30

025

044

40

331

100

00

239

004

80

064

000

4d

eph

a(p

)0

015

000

90

004

000

10

013

000

00

205

039

80

239

100

00

380

007

90

004

mgr

ow

(p)

000

40

004

000

40

001

000

60

001

005

40

048

004

80

380

100

00

011

000

4pr

oxi

(ip)

011

20

002

000

30

015

004

20

071

008

90

202

006

40

079

001

11

000

007

5m

erge

030

90

164

003

20

464

012

40

104

000

30

000

000

40

004

000

40

075

100

0




Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References

































































Constant 5905 0389 mdash( 5744) ( 0710)






coreh (i) 4197 24214 mdash( 1048) ( 8350)

objeh (p) 10714 3663 12076( 2967) (1560) ( 3027)

numsb (p) 0120 0099 0086( 1965) ( 2425) ( 1342)

densi (p) 0794 0479 0346( 0682) ( 0546) ( 0269)

depha (p) 1925 0907 2218(1758) (1209) (1810)

mgrow (p) 0205 0043 0543(0150) ( 0046) (0376)

proxi (ip) 3286 2657 3610(10634) (12121) (10109)

merge 0856 1530 mdash( 2064) (7397)


17206 (2 vs 3)


















8 Conclusions

























References













































































8 Conclusions

























References



















































































References






























































Strategic and Queue effects on Entry in Spanish Banking

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Transcript of Strategic and Queue effects on Entry in Spanish Banking