The Changing Geography of Banking

What borders are (likely) made of?

Massimiliano AffinitoMatteo Piazza

(Bank of Italy)

The Changing Geography of Banking

Outline

• Several barriers to European banking integration

but not so many clues about which ones really

have a major impact

• Regional data on banking structures could provide

some tentative answers

• Data sources

• Results

Barriers to European banking integration

Asymmetric information• Languages and cultural norms

• Relevance of SMEs

Regulatory barriers

Corporate governance rules

Taxation regimes

Governments’ stakes in banking sector

… a partial list

If language, relationship lending, regulatory and legal rules are “barriers”, we should see their impact on banking structures.

E.g.: if local banks thrive because they reduce economic frictions (ECB-CFS, 2004), we should find a significant relationship between indicators of those frictions (say, information asymmetries) and the number of these local banks, ceteris paribus.

Why do we focus on banking structures to investigate borders and barriers?

some phenomena may be better grasped at a regional level:

• matching between small firms and small (local) banks

• linguistic differences we may include country fixed-effects regional characteristics have already been

resilient to national integration…

Why do we focus on regional banking structures to investigate borders and barriers across countries?

If the level of integration reached within countries represents an upper bound for the level of integration EU markets can reach (because regional traits that resisted national integration are likely to survive European integration as well) … … then our results may provide some hints on which barriers are more likely to keep European banking markets fragmented.

Data our sample: 147 regions (mostly, NUTS2 level) in 13

European Countries: Austria (9 regions), Belgium (11), Denmark (1),

Finland (5), France (22), Germany (16), Greece (13), Ireland (2), Italy (20), The Netherlands (12), Portugal (7), Spain (17), United Kingdom (12)

regional and national data are drawn from: - Eurostat for most data (regional database

REGIO) - OSCE for linguistic minorities - World Bank for supervisory indexes

we matched data from the List of MFIs with postal codes to determine the region of establishment for all the credit institutions in the 13 countries in our dataset

Variability in banking structures within European countries is not negligible

The number of banks is much more variable across European regions than across USA States.

The standard deviation in the number of

banks within European countries is, on average, greater than the standard deviation of national averages across countries.

Our exerciseYregion,country = f (Xrc ; Zc)

Depending on the setting, we use either one of the following dependent variables:

1. Yrc : number of banks

2. Yrc : number of branches

3. Yrc : banks/branches ratio

4. Yrc : number of foreign banks

The ratio banks/branches may be a proxy of two different variables

banks’ size(inverse proxy)

correlations (Italy): average deposits 0.95; average loans 0.8

degree of localism(direct proxy)

0 ≤ ratio ≤ 1

- many branches from outer regions - few branches from outer regions

- large banks in the region - small banks (mono-branch) in the region

Independent variables: regional and country variables

Yrc = f (Xrc ; Zc)

Xrc = {populationrc, surface arearc, gdprc per capitarc, firm sizerc, dummy for linguistic minoritiesrc, agricultural workers/total employmentrc, students/populationrc, R&Src, dummy for the capital regionrc, etc.}

Zc = {index of supervisory practicesc, share of assets held by government-owned banks in 1995c, country fixed effectsc}

Three different regressions

number of banks Negative Binomial

regression Yrc :

number of branches Log-linear regression

Yrc : banks/branches ratio Fractional logit regression

Yrc : number of foreign banks Zero Inflated Poisson

Barriers and banking structures

BankingStruct.

→

Barriers↓

Depen.Variab.

→

Proxy↓

Numberofbanks

Relevanceof localbanks

Average

Size ofBanks

AsymmetricInformation

(Relationship lending)

Average size of firms - - +

AsymmetricInformation

(linguistic and culturaldifferences)

LinguisticMinorities + + -

Ratio Banks/Branches

Summary results

  Banks Branches

Ratio banks/branches

Home country

Population +*** +*** +**

GDP per capita +*** +*** +***

Firm size -*** -**/n.s -**

Linguistic minorities +**/* +n.s +***

Supervision -**/n.s +***/* -***

Public banks n.s. n.s. +**

country dummies s. s. s.

An additional exercise: cross-border branching

the dependent variable is the number of foreign branches in each region, broken down according to the region of the establishment of the foreign banks (i.e. the Bavarian branch of a Catalan bank). This means almost 20,000 observations.. but also many zeros

we use a Zero Inflated Poisson model- first step: binary probability logit model (the probability of a zero outcome)- second step: Poisson distribution

(positive outcomes) three different sets of independent variables:

host, home, pair host-home

Summary results

 Foreign banks(cross-border)

Host region

Population +n.s/- ***

GDP per capita +*** /-n.s

Firm size n.s.

Supervision n.s.

Public banks -***

Capital n.s./-***

Country dummies s.

Home region GDP per capita +***/- ***

country dummies s.

Home-host pairs

Trade +**

Common language n.s.

Common border countries n.s/-*

Common border regions n.s/-***

Summing up.. Country dummies and regional characteristics are

strongly significant: history matters The number of banks, branches and foreign banks

depends positively on per capita gross product and population;

Information asymmetries also seem to play a role:The presence of linguistic minorities implies a

larger number of local banks (and reduces banks’ average size)

Smaller non-financial firms are associated with smaller (local) banks

role for supervision is more blurred but this may depend from our indexes