financial systems and banking crises, an assessment

R ev ista M ex ican a d e E co n o m ¶³a y F in an za s, V ol. 5 , N o . 1 (2 00 6), p p . 1 3-2 7 13

F IN A N C IA L S Y S T E M S A N D B A N K IN GC R IS E S : A N A S S E S S M E N T

¤A n to n io R u iz -P o rra sA cco u n tin g a n d F in a n ce D ep a rtm en t

T ecn o l¶o g ico d e M o n terrey, C a m p u s C iu d a d d e M ¶ex ico

(R eceived 5 J a n u a ry 2 0 0 6 , a ccep ted 1 4 M a rch 2 0 0 6 )

A b str a c t

T ra d itio n a lly a n o ld co n cern a m o n g eco n o m ists h a s referred to th e e® ects th a t sp eci c ¯ n a n cia l

sy stem s m ay h av e o n eco n o m ic p erfo rm a n ce. H ere w e in v estig a te th e \ sty lised fa cts" a m o n g

¯ n a n cia l sy stem s a n d b a n k in g crises b y u sin g in d iv id u a l a n d p rin cip a l-co m p o n en ts in d ica to rs

a n d sets o f O L S reg ressio n s. T h e stu d y relies o n a set o f b a n k in g fra g ility, ¯ n a n cia l stru ctu re

a n d d ev elo p m en t in d ica to rs fo r a sa m p le o f 4 7 eco n o m ies b etw een 1 9 9 0 a n d 1 9 9 7 . T h e sty lised

fa cts su g g est th a t ¯ n a n cia l d ev elo p m en t is a sso cia ted to ¯ n a n cia l sy stem s lea d ed b y sto ck

a n d secu rities m a rk ets. F u rth erm o re th e ev id en ce su g g ests th a t su ch a sso cia tio n is m a g n i ed

d u rin g ep iso d es o f b o rd erlin e o r sy stem ic b a n k in g crises. T h u s w h a t o u r ¯ n d in g s m ig h t

su g g est is th a t b a n k in g crises m ay en co u ra g e ¯ n a n cia l d ev elo p m en t a n d th e tra n sfo rm a tio n

o f ¯ n a n cia l sy stem s in to m a rk et-b a sed o n es.

R e su m e n

T ra d icio n a lm en te u n a v ieja p reo cu p a ci¶o n en tre lo s eco n o m ista s se re¯ ere a lo s efecto s q u e

lo s sistem a s ¯ n a n ciero s p u d iera n ten er en el d esem p e~n o eco n ¶o m ico . A q u¶³ in v estig a m o s lo s

" h ech o s estiliza d o s" en tre lo s sistem a s ¯ n a n ciero s y la s crisis b a n ca ria s m ed ia n te el u so d e

in d ica d o res sim p les y d e co m p o n en tes-p rin cip a les, y m ed ia n te el u so d e reg resio n es d e M C O .

E l estu d io se fu n d a m en ta en u n co n ju n to d e in d ica d o res d e fra g ilid a d b a n ca ria , estru ctu ra

¯ n a n ciera y d esa rro llo ¯ n a n ciero p a ra u n a m u estra d e 4 7 eco n o m ¶³a s d u ra n te el p erio d o 1 9 9 0 -

1 9 9 7 . L o s h ech o s estiliza d o s su g ieren q u e el d esa rro llo ¯ n a n ciero se a so cia a a q u ello s sistem a s

¯ n a n ciero s en d o n d e p red o m in a n lo s m erca d o s d e va lo res y d e a ctiv o s. A d em ¶a s, la ev id en cia

su g iere q u e d ich a v in cu la ci¶o n es m a g n i ca d a d u ra n te ep iso d io s d e crisis b a n ca ria s sist¶em ica s y

n o sist¶em ica s. E n co n secu en cia , n u estro s h a lla zg o s su g ieren q u e la s crisis b a n ca ria s p u d iera n

fo m en ta r el d esa rro llo ¯ n a n ciero y la tra n sfo rm a ci¶o n d e lo s sistem a s ¯ n a n ciero s en sistem a s

b a sa d o s en lo s m erca d o s.

J E L c la s s i c a t io n : G 1 , F 4 , C 2

K ey w ord s: F in a n cia l sy stem s, b a n k in g crises, ¯ n a n cia l stru ctu re, ¯ n an cial d ev elo p m en t

I a m g ra tefu l to S p iro s B o u g ea s, R ich a rd D isn ey, A la n S tew a rt D u n ca n , P h ilip M o ly -n eu x , J o se A n to n io N u ~n ez-M o ra , a n d H u m b erto V a len cia -H errera fo r va lu a b le su g g estio n s o n

ea rlier d ra fts. H ow ev er, th e u su a l d iscla im er a p p lies.¤ A cco u n tin g a n d F in a n ce D ep a rtm en t. T ecn o l¶o g ico d e M o n terrey, C a m p u s C iu d a d d e

M ¶ex ico . C a lle d el P u en te 2 2 2 , C o l. E jid o s d e H u ip u lco , C . P . 1 4 3 8 0 , T la lp a n , M ¶ex ico , D .F .

T elep h o n e: + 5 2 (5 5 ) 5 4 8 3 -2 2 3 8 . E -m a il: ru iz.a n to n io @ itesm .m x

14 A . R u iz-P o rra s / F in an cia l S y stem s an d B a n k in g C rises: A n a ssessm en t

1 . In tro d u c tio n

Traditionally an old concern among economists has referred to the e®ects that ¯-1nancial systems may have on economic performance. This concern has encour-

aged the development of theories and empirical research to assess the relative2merits of di®erent ¯nancial systems for policy purposes. Here we investigate

the \stylised facts" among ¯nancial systems and banking crises by using in-ternational evidence. The study relies on a set of banking fragility, ¯nancialstructure and development indicators for a sample of 47 economies between1990 and 1997.

The investigation is motivated by the idea that banking crises may carry3repercussions of social nature in addition to the ones of private one. Financial

and non ¯nancial ¯rms and even the security of the payment system may bea®ected by banking crises [Freixas and Rochet (1997) , Goodhart et a l. (1998) ] .Particularly we focus on the relationship among ¯nancial systems and bankingcrises because such empirical studies are scarce in spite of the recognition thatthey may be useful to improve our understanding of the likelihood of banking

4fragility [Demirguc-Kunt and Detragiache (1998) ] .

The study of the stylised facts may be interesting for theoretical purposes.According to some economists, di®erent ¯nancial systems provide di®erent in-centives and opportunities to share risks and to encourage speci¯c performancegoals due to the existence of competition among ¯nancial markets and banks[Allen and Gale (2000) , (2004) ] . However, with exception of the studies ofLevine (2002) , and Lopez and Spiegel (2002) , we do not know about otherempirical assessments related to such claim. Thus the characterisation of thestylised facts may provide evidence to develop and support theoretical claims.

Methodologically, we characterise the stylised facts with assortments of5indicators. Categorical banking fragility indicators refer to episodes of systemic

1 S u ch co n cern s ca n b e tra ced b a ck to B a g eh o t (1 8 7 3 ) a n d F ish er (1 9 3 3 ). A cco rd in gto th e fo rm er, G erm a n y h a d ov erco m e th e U n ited K in g d o m a s a n in d u stria l p ow er in th en in eteen cen tu ry d u e to th e rela tiv e su p erio rity o f th e G erm a n b a n k -b a sed ¯ n a n cia l sy stemw ith resp ect to th e m a rk et-b a sed B ritish o n e. A cco rd in g to th e la tter, th e sev erity o f th e

A m erica n G rea t D ep ressio n w a s m a in ly a resu lt o f p o o rly p erfo rm in g ¯ n a n cia l m a rk ets.2 S ee G etler (1 9 8 8 ), S a n to m ero (1 9 8 9 ), L ev in e (2 0 0 2 ), B eck (2 0 0 3 ), A llen a n d G a le (2 0 0 0 )

a n d (2 0 0 4 ) fo r so m e su rv ey s a n d rev iew s o n th e th eo ries reg a rd in g ¯ n a n cia l stru ctu re a n deco n o m ic p erfo rm a n ce. C o m p a ra tiv e stu d ies o f th e ex istin g ¯ n a n cia l sy stem s a lo n g th e w o rlda re G o ld sm ith (1 9 6 9 ), F ra n k el a n d M o n tg o m ery (1 9 9 1 ), D em irg u c-K u n t a n d L ev in e (1 9 9 9 ),

a n d A llen a n d G a le (2 0 0 0 ).3 C a p rio a n d K lin g eb iel (1 9 9 6 a ) a n d (1 9 9 6 b ) sh ow th a t th e co sts th a t th e eco n o m ies p ay

d u e to b a n k in g in so lv en cy ep iso d es u su a lly a re a b ov e 1 5 % o f th eir G D P . M o reov er, th ese co stsd o n o t in clu d e th e o n es a sso cia ted to th e ex ch a n g e-ra te crises a n d eco n o m ic d ow n tu rn s th a t

u su a lly a cco m p a n y su ch ep iso d es.4 E x istin g stu d ies a re m a in ly d escrip tiv e a n d rela te to sp eci c ca ses o f ¯ n a n cia l fra g ility,

lik e th e A sia n C risis a n d th e U S ex p erien ce in th e eig h ties a n d n in eties [S ee A llen (2 0 0 1 ) a n dH o en ig (2 0 0 1 ), resp ectiv ely ]. In a n in tern a tio n a l co n tex t stu d ies th a t in clu d e so m e ¯ n a n cia lstru ctu re d eterm in a n ts a re th e o n es o f D em irg u c-K u n t a n d H u izin g a (2 0 0 0 ) o n b a n k in g p ro f-ita b ility a n d th e o n e o f B eck , D em irg u c-K u n t, a n d L ev in e (2 0 0 3 ) o n b a n k in g co n cen tra tio n

a n d crises.5 T h e a b sen ce o f a ccep ted em p irica l d e¯ n itio n s fo r ¯ n a n cia l stru ctu re a n d ¯ n a n cia l d e-


and borderline banking crises compiled by Caprio and Klingebiel (2002) . Dataextracted from the database of Beck, Demirguc-Kunt and Levine (2000) , areused to build ¯nancial structure and development assortments of indicatorsaccording to the guidelines of Levine (2002) . The assortments include measuresof activity, size and e±ciency of the intermediaries. Individual and principal-component indicators are used for the empirical assessments.

Our research aims to identify patterns or " stylised facts" that may shedlight on the normative properties of di®erent ¯nancial systems. We do this bycomparing relationships between indicators under di®erent banking conditions.The idea underlying the identi¯cation of such patterns is to suggest answersto some of the following questions: What are the main empirical relationshipsamong ¯nancial systems and banking crises? How does banking fragility a®ectthe relationships between ¯nancial structure and ¯nancial development? Whichtype of implications may be derived from these ¯ndings?

Our ¯ndings have implications for academic and policy purposes. Specif-ically the stylised facts suggest that ¯nancial development is associated to ¯-nancial systems leaded by stock and securities markets. Furthermore the evi-dence suggests that such association is magni¯ed during episodes of borderlineor systemic banking crises. These ¯ndings are consistent with the idea thatcrises have had a signi¯cant impact on the historical development of ¯nancialsystems. Thus what our ¯ndings might suggest is that banking crises may en-courage ¯nancial development and the transformation of ¯nancial systems intomarket-based ones.

The paper is divided in ¯ve sections. Section 2 describes the data used inthe econometric analyses. Section 3 discusses methodological issues to assessthe limits and scope of our ¯ndings. Section 4 characterises the stylised factsamong ¯nancial structure and development with banking fragility. Section 5summarises and discusses the main ¯ndings. Finally, the appendix focuses onthe principal-component methodology.

2 . F in a n c ia l a n d b a n k in g in d ic a to r s

Here we describe the ¯nancial and banking indicators used in the analysis.We believe this task particularly relevant because of the absence of empiricalde¯nitions for ¯nancial system features and banking fragility. Thus, beforeproceeding, we de¯ne certain de¯nitions for operative purposes. Speci¯cally, inthe following we will refer to ¯nancial development as the level of developmentof both intermediaries and markets, while by ¯nancial structure we will meanthe degree to which a ¯nancial system is based on intermediaries or markets.Banking fragility will mean a situation in which borderline or systemic bankingcrises are present in an economy.

We build the indicators by extracting ¯nancial and banking data from twodatabases. We use panel-data extracted from the cross-country database on¯nancial development and structure [Beck, Demirguc-Kunt and Levine (2000) ] ,to build the ¯nancial system indicators. Furthermore, we use data from the

v elo p m en t m a k es n ecessa ry to u se a sso rtm en ts o f in d ica to rs in th e a n a ly sis. S u ch a p p ro a chh a s b een u sed b y L ev in e (2 0 0 2 ) a n d B eck (2 0 0 3 ) to a n a ly se th e d eterm in a n ts o f lo n g -ru n

eco n o m ic p erfo rm a n ce.


database on episodes of borderline and systemic banking crises [Caprio andKlingebiel (2002) ] , to build qualitative indicators of banking fragility. The mainadvantage of using these databases is that they allow us to treat consistently

6the ¯nancial and banking system data across economies and across time.

The ¯nancial and banking data and their main features are summarised inthe table:

Table 1 . Financial and Banking Data.

D e¯ n itio n V a ria b le T im e sp a n C o u n tries O b serva tio n s

B a n kin g fra gility va ria bles

D u m m y va ria b le o n sy stem ic

ep iso d es o f b a n k in g fra g ility y 1 9 7 5 -1 9 9 9 9 3 1 1 3S

(crisis= 1 , n o n crisis= 0 )

D u m m y va ria b le o n b o rd erlin e

ep iso d es o f b a n k in g fra g ility y 1 9 7 5 -1 9 9 9 4 4 5 0B

(crisis= 1 , n o n crisis= 0 )

F in a n cia l stru ctu re a n d d evelo p m en t va ria bles

O v erh ea d co sts o f th e b a n k in g

sy stem rela tiv e to b a n k in g 1 9 9 0 -1 9 9 7 1 2 9 7 1 9B O H C

sy stem a ssets

P riva te cred it b y d ep o sit m o n ey

b a n k s to G D P 1 9 6 0 -1 9 9 7 1 6 0 3 9 0 1D B P C Y

(B a n k cred it ra tio )

P riva te cred it b y d ep o sit m o n ey

b a n k s a n d o th er ¯ n a n cia l 1 9 6 0 -1 9 9 7 1 6 1 3 9 2 3T I P C Y

in stitu tio n s to G D P

(P riva te cred it ra tio )

S to ck m a rk et ca p ita lisa tio n

to G D P 1 9 7 6 -1 9 9 7 9 3 1 1 7 1S M C Y

(M a rk et ca p ita lisa tio n ra tio )

S to ck m a rk et to ta l va lu e tra d ed

to G D P 1 9 7 5 -1 9 9 7 9 3 1 2 6 4S M V Y

(T o ta l va lu e tra d ed ra tio )

N o tes: T h e co m p lete ¯ n a n cia l d ev elo p m en t a n d stru ctu re d a ta b a se in clu d es sta tistics o n

th e size, a ctiv ity a n d e± cien cy o f va rio u s in term ed ia ries (co m m ercia l b a n k s, in su ra n ce

co m p a n ies, p en sio n fu n d s a n d n o n -d ep o sit m o n ey b a n k s) a n d m a rk ets (p rim a ry eq u ity

a n d p rim a ry a n d seco n d a ry b o n d m a rk ets). R eg a rd in g th e d a ta b a se o n b a n k in g crises,

it co m p rises o f th e tw o va ria b les in clu d ed h ere.

The sample was built according to data availability. It includes data for Ar-gentina, Australia, Bolivia, Brazil, Barbados, Canada, Switzerland, Chile, Co-lombia, Costa Rica, Cyprus, Germany, Ecuador, Egypt, Spain, Ghana, Greece,

6 T h e d a ta b a ses a n d d eta ils o n th eir co n stru ctio n a re ava ila b le a t th e w eb site o f th e W o rldB a n k . T h e a d d ress is th e fo llow in g : h ttp :/ / eco n .w o rld b a n k .o rg [T itles: \ A n ew d a ta b a seo n ¯ n a n cia l d ev elo p m en t a n d stru ctu re" a n d \ E p iso d es o f sy stem ic a n d b o rd erlin e ¯ n a n cia l

crises" ].


Guatemala, Honduras, Ireland, Jamaica, Jordan, Japan, Kenya, Korea, Mo-rocco, Mexico, Malaysia, Namibia, Nigeria, Netherlands, Norway, New Zealand,Peru, Philippines, Paraguay, El Salvador, Sweden, Thailand, Trinidad and To-bago, Tunisia, Turkey, Taiwan, United States, Venezuela, South Africa andZimbabwe. Hence, the sample comprises data for 47 economies and 115 bank-ing crises over the period 1990-97.

We de¯ne seven individual indicators. We follow Demirguc-Kunt andLevine (1999) and Levine (2002) to build two assortments of indicators to anal-yse the features of ¯nancial systems. The assortment of structural indicatorscontains individual measures of the activity, size and e±ciency of stock marketsrelative to that of banks. The assortment of development indicators containsmeasures of the activity, size and e±ciency of stock markets and banks. Thebanking fragility indicator is a qualitative variable for borderline and systemiccrises that follows the standard convention of the fragility literature [Demirguc-Kunt and Detragiache (1998) , and Hardy and Pazarbasioglu (1999) ] .

The six ¯nancial system indicators use di®erent measures to assess thestructure and the degree of ¯nancial development. The structural assortmentis integrated by the Structure-Activity, Structure-Size and Structure-E±ciencyindicators. In this assortment market-based ¯nancial systems are associatedto large values of the indicators while bank-based ones are associated to smallvalues. The ¯nancial development assortment is integrated by the Finance-Activity, Finance-Size and Finance-E±ciency indicators. In this assortment,¯nancial development is associated to large values of the indicators while un-

7derdevelopment is associated to small values.

Furthermore we build two aggregate indicators to summarise the informa-tion content of the assorted indicators. We follow the methodological approachof Levine (2002) to de¯ne and construct them by using principal-componentmultivariate methods. More speci¯cally, each aggregate indicator is de¯ned asthe ¯rst linear combination of the three individual indicators that integrate eachassortment. The way in which the aggregate indicators summarise the relevantinformation is based on proportions of the total variance that are accountedfrom the individual indicators. [See Appendix for further details]

We use the principal-components methodology to simplify the task to un-derstand the explanatory multivariate data in terms of a smaller number ofuncorrelated variables. Intuitively, given that a ¯rst principal-component ispositively correlated to the each of the individual indicators, it can be inter-preted as a measure of what is common to all the variables. Given the lack ofempirical de¯nitions for ¯nancial development and ¯nancial structure, we caninterpret the aggregate indicators as indexes of scale for the degree of develop-ment and of the relative prominence of markets in the ¯nancial system.

7 A cco rd in g to D em irg u c-K u n t a n d L ev in e (1 9 9 9 ), la rg e a n d sm a ll va lu es d ep en d o n th em ed ia n o f ea ch ty p e o f in d ica to r. H en ce, b y d e¯ n itio n , th is criterio n a ssu m es th a t h a lf o f th eo b serva tio n s b elo n g to a certa in ca teg o ry w h ile th e o th er h a lf to a n o th er. T h is criterio n , in

sp ite o f b ein g a rg u a b le, a llow s u s to av o id p o ten tia l ex trem e-va lu e p ro b lem s.


The set of ¯nancial and banking indicators is summarised in the followingtable:

Table 2. Financial and Banking Indicators.

N a m e D e¯ n itio n M ea su rem en t

B a n kin g fra gility in d ica to rs

B in a ry va ria b le fo r fra g ility E p iso d es o f sy stem ic a n d / o r

C risis B a n k in g crisis = 1 b o d erlin e b a n k in g crises

N o n b a n k in g crisis = 0

F in a n cia l stru ctu re in d ica to rs

A ctiv ity o f sto ck m a rk etsS M V Y

S tru ctu re A ctiv ity S T C A C T = ln rela tiv e to th a t o f b a n k sD B P C Y

S ize o f sto ck m a rk etsS M C Y

S tru ctu re S ize S T C S I Z = ln rela tiv e to th a t o f b a n k sD B P C Y

E ± cen cy o f sto ck m a rk ets

S tru ctu re E ± cien cy rela tiv e to th a t o f b a n k sS T C E F F = ln (S M V Y £ B O H C )

F irst p rin cip a l co m p o n en t o f S ca le In d ex o f ¯ n a n cia l

S tru ctu re A g g reg a te th e set o f in d iv id u a l ¯ n a n cia l stru ctu re

stru ctu re in d ica to rs

F in a n cia l d evelo p m en t in d ica to rs

A ctiv ity o f sto ck m a rk ets a n d

F in a n ce A ctiv ity in term ed ia riesF I N A C T = ln (S M V Y £ T I P C Y )

S ize o f sto ck m a rk ets a n d

F in a n ce S ize in term ed ia riesF I N S I Z = ln (S M C Y £ T I P C Y )

S M V YF in a n ce E ± cien cy F I N E F F = ln F in a n cia l secto r e± cien cyB O H C

F irst p rin cip a l co m p o n en t o f S ca le In d ex o f ¯ n a n cia l

F in a n ce A g g reg a te th e set o f in d iv id u a l ¯ n a n cia l d ev elo p m en t

d ev elo p m en t in d ica to rs

N o tes: L a rg e va lu es o f th e ¯ n a n cia l stru ctu re in d ica to rs a re a sso cia ted to m a rk et-b a sed

¯ n a n cia l sy stem s; sm a ll o n es to b a n k -b a sed o n es. L a rg e va lu es o f th e ¯ n a n cia l d ev elo p -

m en t in d ica to rs rela te to h ig h lev els o f ¯ n a n cia l d ev elo p m en t.

3 .M e th o d o lo g ic a l issu e s o n in d ic a to rs a n d e c o n o m e tr ic m e th o d o lo g y

Here we discuss some methodological issues. We regard this discussion as crucialbecause it allows to asses the limits and scope of our ¯ndings. Such discussionis necessary in spite that we use the most extensive data publicly available


and that the sample allows us to characterise most ¯nancial systems in theworld. Thus we will begin the discussion by examining the indicators usedin the empirical assessments. Latter, we will continue by explaining the OLSregression approach used to assess the stylised facts among ¯nancial systemsand banking crises.

We begin by examining the ¯nancial structure and development indicators.These indicators are useful to assess the stylised facts because the data usedto build them are consistent across economies and across time. However, theindicators have certain limitations. The ¯rst one relates to the fact that the

8information that indicators provide is relative to the sample. Thus it wouldnot be surprising if any classi¯cation for an economy changes when the samplechanges. Furthermore, a second one is that the interpretation of the aggregateprincipal-component indicators is somewhat subjective.

The referred subjectivity argument can be extended to include the bankingfragility indicator. The characterisation of banking crises periods is not as directas it seems [Caprio and Klingebiel (2002) ] . The time span of banking crises isnot easy to determine. Financial distress periods, where the banking systemhas negative worth, can occur over a period of time, before and after beingdetected. Also it is not always clear when a crisis is over. Thus, even at amere qualitative level, the characterisation of banking crises with a categoricalvariable requires certain judgement.

However, in spite of the above limitations, we use the best and most exten-sive ¯nancial and banking data publicly available. It allows us to quantitativelycharacterise the main features of most ¯nancial systems by using econometrictechniques like OLS and panel-data ones. Particularly the possibility to usedi®erent econometric techniques to study the data suggest us a concrete ¯rstapproach to study such stylised facts. Such assessment approach is based oncomparisons among indicator relationships under di®erent situations of bankingperformance. Here the chosen approach is based on OLS regression techniques.

Methodologically, the relationships among ¯nancial systems and bankingcrises are assessed with four regression sets (One for the aggregate indicatorsand the other three for the individual ones) . Each set is built by subsets of threesingle-variable regressions that describe the associations between a speci¯c pairof indicators under di®erent data samples. In each subset, the ¯rst regressionestimates an association using all the sampled data. The second and thirdregressions re-estimate the same association using two data sub-samples thatare di®erentiated according to the banking fragility indicator.

Each regression set analyses one speci¯c relationship among the indicators.The ¯rst set analyses the relationships among the ¯nancial development indi-cators with respect to the Structure-Activity one. The second set analyses therelationships with respect to the Structure-Size one. The third analyses the re-lationships with respect to the Structure-E±ciency one. It may be argued thatthe underlying assumption behind these regressions is that ¯nancial structure

8 F o r ex a m p le, ¯ n a n cia l stru ctu re in d ica to rs ca n in d ica te th a t certa in eco n o m ies m ay h av eb a n k -b a sed ¯ n a n cia l sy stem s b eca u se th eir sto ck m a rk ets a re v ery u n d erd ev elo p ed b y in tern a -tio n a l sta n d a rd s. C o n v ersely, ¯ n a n cia l sy stem s o f eco n o m ies w ith sm a ll a n d u n d erd ev elo p ed

b a n k in g sy stem s m ay b e a ssu m ed a s m a rk et-b a sed o n es [D em irg u c-K u n t a n d L ev in e (1 9 9 9 )].


causes ¯nancial development. However, this is not the case. The assessment ofstylised facts does not imply any causality.

4 . E c o n o m e tric a sse ssm e n t o f sty lise d fa c ts

Here we report the econometric results associated to the four regression sets usedto assess the stylised facts among ¯nancial systems and banking crises. Firstwe report the results associated to the three sets used to investigate the rela-tionships among individual indicators. Later we report the results associated tothe fourth set of aggregate indicators. The regression subsets between pairs ofindicators are estimated with three data samples according to the econometricprocedure described above. In all the regressions we have included a constant

9term to eliminate constant e®ects.

The ¯rst regression set analyses the relationship between ¯nancial devel-opment and the relative activity of stock markets with respect to that of banks.We summarise the results of the regression set of individual indicators in thefollowing table:

Table 3. Financial Systems and Banking Crises(Financial Development and Structure-Activity Indicators) .

2 2 2¯ R ¯ R ¯ R

(t) (t) (t)

R eg resso r A ll O b serva tio n s S ta b le B a n k in g S y stem s F ra g ile B a n k in g S y stem s

In d ica to r (1 ) (2 ) (3 )

R egressed In d ica to r: F in a n ce-A ctivity¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤

S tru ctu re 0.51 0.69 0.47 0.64 0.54 0.72A ctiv ity (2 7 .1 2 6 ) (1 9 .9 6 1 ) (1 6 .2 6 6 )

R egressed In d ica to r: F in a n ce-S ize¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤

S tru ctu re 0.55 0.35 0.44 0.27 0.64 0.43A ctiv ity (1 2 .8 5 3 ) (8 .8 9 9 ) (8 .5 4 8 )

R egressed In d ica to r: F in a n ce-E ± cien cy¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤

S tru ctu re 0.59 0.73 0.52 0.67 0.65 0.77A ctiv ity (2 7 .2 3 5 ) (1 9 .4 3 3 ) (1 7 .0 7 4 )

N o tes: T h e reg ressio n s u se O L S to estim a te eq u a tio n s o f th e fo rm : y = ® + ¯ x , w h erey a n d x a re th e reg ressed a n d reg resso r in d ica to rs, resp ectiv ely. T h e reg ressio n s u se

d i® eren t o b serva tio n s fo r co m p a riso n p u rp o ses. S p eci ca lly, th e ¯ rst co lu m n refers to

reg ressio n s th a t in clu d e a ll th e o b serva tio n s. T h e seco n d co lu m n refers to reg ressio n s

th a t in clu d e o b serva tio n s fo r w h ich th e b a n k in g fra g ility va ria b le is eq u a l to zero . T h e

th ird co lu m n refers to th e o n es fo r w h ich th e b a n k in g fra g ility va ria b le is eq u a l to o n e.

E a ch co lu m n co n ta in s th e estim a te o f ¯ , th e t-sta tistic o f th is estim a te (in p a ren th eses)2

a n d th e R va lu e o f th e reg ressio n . O n e, tw o a n d th ree a sterisk s in d ica te sig n i ca n ce

lev els o f 1 0 , 5 a n d 1 p ercen t resp ectiv ely. T h e estim a ted co e± cien ts fo r co n sta n ts a re

n o t rep o rted .

9 T h e co e± cien ts a n d t-sta tistics a sso cia ted to th ese co n sta n t va lu es a re n o t rep o rted inth e reg ressio n resu lts in d ica ted b elow . W e d o th is fo r sim p licity p u rp o ses a n d to fo cu s o n th e

rela tio n sh ip s a m o n g th e in d ica to rs.


Table 3 shows that ¯ n a n cia l d ev elo p m en t is a sso cia ted to a rela tiv e in crea se inth e a ctiv ity o f sto ck m a rk ets w ith resp ect to th a t o f b a n k s. All the associa-tions are positive and statistically signi¯cant (1 percent signi¯cance level) . Theconsistency and robustness of these associations hold independently of bank-ing stability considerations. Interestingly, the comparisons among data samplessuggest that su ch a sso cia tio n s a re m a g n i ed d u rin g ep iso d es o f b o rd erlin e o rsy stem ic b a n k in g crises. The regression coe±cients, ¯ , and coe±cients of de-

2termination, R , are higher for samples involving fragile banking systems.

The second regression set analyses the relationship between ¯nancial de-velopment and the relative size of stock markets with respect to that of banks.We summarise the results of the regression set of individual indicators in thefollowing table:

Table 4. Financial Systems and Banking Crises(Financial Development and Structure-Size Indicators) .

2 2 2¯ R ¯ R ¯ R

(t) (t) (t)


In d ica to r (1 ) (2 ) (3 )


S tru ctu re 0.10 0.12 0.07 0.06 0.14 0.21S ize (6 .5 9 1 ) (3 .7 4 1 ) (5 .0 6 8 )


S tru ctu re 0.18 0.16 0.15 0.12 0.21 0.20S ize (7 .6 2 8 ) (5 .3 5 5 ) (5 .0 4 2 )


S tru ctu re 0.14 0.16 0.11 0.10 0.18 0.24S ize (7 .1 6 5 ) (4 .6 4 2 ) (5 .1 3 4 )









n o t rep o rted .

Table 4 shows that ¯ n a n cia l d ev elo p m en t is a sso cia ted to a rela tiv e in crea sein th e size o f sto ck m a rk ets w ith resp ect to th a t o f b a n k s. Again the associa-tions are positive and statistically signi¯cant. The consistency and robustnessof these associations hold independently of banking stability considerations.Again the comparisons among data samples suggest that such associations are


2magni¯ed during banking crisis periods. The coe±cients ¯ and R are notablyhigher for samples involving periods of banking crises.

The third regression set analyses the relationship between ¯nancial de-velopment and the relative e±ciency of stock markets with respect to that ofbanks. We summarise the results of the regression set of individual indicatorsin the following table:

Table 5. Financial Systems and Banking Crises(Financial Development and Structure-E±ciency Indicators) .

2 2 2¯ R ¯ R ¯ R

(t) (t) (t)


In d ica to r (1 ) (2 ) (3 )


S tru ctu re 0.61 0.77 0.57 0.73 0.64 0.81E ± cien cy (3 1 .0 2 4 ) (2 2 .5 1 4 ) (1 9 .4 7 1 )













n o t rep o rted .

Table 5 shows that ¯ n a n cia l d ev elo p m en t is a sso cia ted to a rela tiv e in crea se inth e e± cien cy o f sto ck m a rk ets w ith resp ect to th a t o f b a n k s. Once again theassociations are positive and statistically signi¯cant. Once more the compar-isons among data samples suggest that such associations are magni¯ed duringbanking crisis periods.

The fourth regression set analyses the relationship between the ¯nancialdevelopment and ¯nancial structure aggregate indexes. We summarise the re-sults of the regression set of indicators in the following table:


Table 6. Relationships Between Financial and Banking AggregateIndicators (Regression Analysis) .

2 2 2¯ R ¯ R ¯ R

(t) (t) (t)


In d ica to r (1 ) (2 ) (3 )

R egressed In d ica to r: F in a n ce-A ggrega te¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤

S tru ctu re 0.69 0.56 0.59 0.51 0.81 0.60A g g reg a te (1 8 .3 4 5 ) (1 3 .5 2 3 ) (1 1 .2 5 9 )









n o t rep o rted .

Table 6 con¯rms that ¯ n a n cia l d ev elo p m en t is a sso cia ted to m a rk et-b a sed ¯ -n a n cia l sy stem s. Not surprisingly, the associations are positive and statisticallysigni¯cant and their consistency and robustness hold independently of banking

2performance. Once again, the coe±cients ¯ and R are higher when bankingdistress is present. Thus this regression set provides an overview of the stylisedfacts among ¯nancial systems and banking crises.

We summarise our ¯ndings by indicating that the evidence suggests thatd ev elo p ed ¯ n a n cia l sy stem s a re lea d ed b y sto ck a n d o th er secu rities m a rk ets.Moreover it also suggests that su ch a sso cia tio n is m a g n i ed d u rin g ep iso d es o fb o rd erlin e o r sy stem ic b a n k in g crises. These ¯ndings are consistent with theidea that crises have had a signi¯cant impact on the historical developmentof ¯nancial systems [Allen and Gale (2000) ] . Thus what our ¯ndings mightsuggest is that b a n k in g crises m a y en co u ra g e ¯ n a n cia l d ev elo p m en t a n d th etra n sfo rm a tio n o f ¯ n a n cia l sy stem s in to m a rk et-b a sed o n es.

5 . C o n c lu sio n s a n d d isc u ssio n

This paper has shown the results of an investigation regarding the clari¯cationof the stylised facts among ¯nancial systems and banking crises. The motivationof such study relies on theoretical and practical concerns. The former relatesto how the ¯nancial contracting process and the functioning of intermediariesand markets may depend on the ¯nancial structure prevailing in an economy.While the latter relates to the consideration that banking crises may carryrepercussions of private and social nature. Overall, this investigation aims toprovide some elements regarding the discussions around the e®ects that ¯nancialsystems may have on economic performance.


Research results may be summarised by indicating that the evidence sug-gests that d ev elo p ed ¯ n a n cia l sy stem s a re lea d ed b y sto ck a n d secu rities m a r-k ets. This result was obtained after estimating regressions between pairs ofindividual and principal-components indicators. In all cases the conclusionwas supported by consistent and statistically signi¯cant regression coe±cients.Furthermore other results suggested that su ch rela tio n sh ip is m a g n i ed d u rin g

2ep iso d es o f b a n k in g fra g ility . In all the regressions the coe±cients ¯ and Rwere higher when banking distress was present.

These ¯ndings are consistent with the idea that crises have had a signi¯-cant impact on the historical development of ¯nancial systems [Allen and Gale(2000) ] . Speci¯cally, our ¯ndings suggest that b a n k in g crises m ig h t en co u ra g e¯ n a n cia l d ev elo p m en t a n d th e tra n sfo rm a tio n o f ¯ n a n cia l sy stem s in to m a rk et-b a sed o n es. We consider this conclusion interesting under the basis that thequestion \What drives the evolution of the ¯nancial system?" is one of themain research issues in the literature of comparative ¯nancial systems [Allenand Gale (2004: 701) ] .

Academically, what follows up from this study is that further research canbe carried along the lines of the literature of comparative ¯nancial systems[Allen and Gale (2000) and (2004) ] . The relevance of such research is justi¯edunder theoretical and practical purposes. Well designed ¯nancial and bankingsystems are essential to guarantee the smooth allocation of resources within andacross economies. Further issues regarding globalization, ¯nancial fragility, riskmanagement and regulation practices may be analysed under the guidelines of

1 0this literature. We hope to encourage further research in such directions.

A p p e n d ix

The p rin cip a l co m p o n en ts a n a ly sis (PCA) is a method for re-expressing mul-tivariate data. It allows to reorient the data so that the ¯rst few dimensionsaccount for much as information as possible. The central idea is based on theconcept of the proportion of the to ta l va ria n ce (the sum of the variance of thep original variables) that is accounted for by each of the new variables. PCAtransforms the set of correlated variables (x ;:::;x ) to a set of uncorrelated1 p

variables (y ;:::;y ) called principal components in such a way that y explains1 p 1

the maximum possible of the total variance, y the maximum possible of the2

remaining variance, and so on.

The aim of PCA is to interpret the underlying structure of the data interms of the most important principal components. Usually, the ¯rst principalcomponent may be interpreted as a measure of what is common to the set ofcorrelated variables (x ;:::;x ) . Such interpretation relies on the fact that the1 p

¯rst principal component is the best one-dimensional summary of the data.Particularly, for the aims of the analysis developed here, the ¯rst principalcomponent may be interpreted as a sca le in d ex that summarises the informationcontained on a particular set of variables.

Mathematically, the PCA problem is to determine the coe±cients a forij

the following linear system:

1 0 S ee R u iz-P o rra s, V ¶a sq u ez-Q u ev ed o a n d N u ~n ez-M o ra (2 0 0 6 ) fo r a n ex a m p le o f su ch a n a l-

y sis in th e co n tex t o f th e M ex ica n ex p erien ce.

R ev ista M ex ica n a d e E con o m ¶³a y F in an za s, V ol. 5, N o . 1 (2 00 6), p p . 1 3-2 7 2 5

y = a x + a x + :::+ a x1 1 1 1 2 1 2 p 1 p

y = a x + a x + :::+ a x2 1 2 1 2 2 2 p 2 p

...

y = a x + a x + :::+ a xp 1 p 1 2 p 2 p p p

where (x ;:::;x ) is the former set of correlated variables and (y ;:::;y ) is1 p 1 p

the set of principal component variables.

Each principal component is de¯ned by the variables with which it is mosthighly correlated. The ¯rst principal component, denoted by y , is given by the1

linear combination of the original variables X = (x ;:::;x ) with the largest1 p

possible variance (where the variance is interpretable as the information con-tained in the data) . The second principal component denoted by y , is given2

by the linear combination of X that accounts for the most information (highestvariance) not already captured by y ; that is y is chosen to be uncorrelated1 2

with y . All the subsequent principal components are chosen to be uncorrelated1

with all previous principal components.

Because principal components analysis seeks to maximise variance, it canbe highly sensitive to scale di®erences across variables. Particularly, becausethe data analysed here involve arbitrary units of measurement, our approach toconstruct indexes based on principal component may be meaningless withoutintroducing further conditions. We standardise the data to avoid this problem.Such data are denoted X . This standardisation is achieved by introducing an

S

orthogonality condition for the coe±cients. Algebraically, such condition canbe written as:

pX2a = 1 ; (j = 1;2;:::;p )ij

i= 1

pXa a = 0 ; (j 6= k ; j = 1;2;:::;p ; k = 1;2;:::;p )ij ik

i= 1

Analytically, the solution to the principal components problem stated above isobtained by performing an eigenvalue decomposition of the correlation matrix(i.e. , the covariance matrix of the standardised data) . Thus, ¯nding the eigen-value vector ¸ = (¸ ;:::;¸ ) and eigenvectors of the correlation matrix solves1 p

the problem.

The variance of each principal component can be obtained by listing theeigenvalues from the largest to the smallest ¸ ¸ ¸ ¸ ::: ¸ ¸ ¸ 0. The1 2 p

variance of ¯rst principal-component will be the eigenvalue ¸ . The propor-1

tion of total variance explained by the ¯rst principal-component will be then¸ 1 . Bartholomew et. a l. (2002) , and Lattin, Carroll and Green (2003)¸ + ¸ + :::+ ¸ p1 2

provide detailed explanations on the principal-components methodology.

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