1. The Changing Geography of Banking What borders are (likely) made of? Massimiliano Affinito Matteo Piazza (Bank of Italy)

2. 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

3. 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

4.  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?

5.  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 ?

6. 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.

7. 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

8. 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.

9. Our exercise Y region,country = f ( X rc ; Z c ) Depending on the setting, we use either one of the following dependent variables: 1. Y rc : number of banks 2. Y rc : number of branches 3. Y rc : banks/branches ratio 4. Y rc : number of foreign banks

10. 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

11. Independent variables: regional and country variables Y rc = f ( X rc ; Z c )  X rc = {population rc, surface area rc, gdp rc per capita rc, firm size rc, dummy for linguistic minorities rc, agricultural workers/total employment rc, students/population rc, R&S rc, dummy for the capital region rc, etc.}  Z c = { index of supervisory practices c, share of assets held by government-owned banks in 1995 c, country fixed effects c }

12. Three different regressions number of banks Negative Binomial regression Y rc :  number of branches Log-linear regression Y rc : banks/branches ratio  Fractional logit regression Y rc : number of foreign banks  Zero Inflated Poisson

13. Barriers and banking structures Banking Struct. → Barriers ↓ Depen. Variab. → Proxy ↓ Number of banks Relevance of local banks Average Size of Banks Asymmetric Information (Relationship lending) Average size of firms --+ Asymmetric Information (linguistic and cultural differences) Linguistic Minorities ++- Ratio Banks/Branches

14. 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. +** country dummies s.

15. 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

16. 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/-***

17. 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