Banks, Distances and Financing
Constraints to Firms
Pietro Alessandrini
Andrea F. Presbitero
Alberto Zazzaro
Labis - Dipartimento di Economia
Università Politecnica delle Marche
Presentation to be given at the conference
The Changing Geography of Banking
Università Politecnica delle Marche
Ancona, 22-23 September 2006
Motivations
Geography of the banking system has changed as
follows:
 Diffusion of banking structures and instruments has
increased the operational proximity (OP) between
banks and borrowers;
 Spatial concentration of decisional centers has
increased the functional distance (FD; Alessandrini,
Croci and Zazzaro 2005), i.e. the distance between the
locus of control of local lending offices and
borrowers.
 OP and competition should guarantee an adequate
response to the needs of local borrowers, regardless of
the locus of control
of local bank offices.
Ancona, 23 September 2006
2
P. Alessandrini, A. Presbitero and A. Zazzaro

Aims



We investigate whether the distance between the
locus of control of bank branches and the local
communities where they operate do affect
financing constraints to local firms.
We introduce a new indicator of FD.
We apply our analysis on Italian data over the
period 1995-2003 at the local market level
defined as province.
Ancona, 23 September 2006
P. Alessandrini, A. Presbitero and A. Zazzaro
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Operational Proximity: Theory and Evidence



OP impacts on information asymmetries, producing positive
and negative effects on firms’ financing constraints (‘soft’
information; relational lending; market power; winner’s curse).
At the bank level:
 OP is associated with higher interest rates and
more
relationship lending (Petersen and Rajan 2002; Degryse
and Ongena 2004, 2005; Elsas, 2005).
At the market level (Italy):
 OP increases credit availability, reduces the share of bad
loans, raises the probability of firms introducing
innovations (Bonaccorsi and Gobbi 2001; Benfratello et al.
2005).
Ancona, 23 September 2006
P. Alessandrini, A. Presbitero and A. Zazzaro
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Functional Distance: Theory and Evidence







The importance of FD is due to:
 Social and cultural differences across communities;
 Asymmetric distribution of information within organizations.
FD affect bank-firm relationships through a number of channels (Agency costs;
Influence activities; Career focus).
Agency problems and influence costs affect banking and credit allocation (Udell
1989; Berger and DeYoung 2002; Liberti 2005).
Out-of-market owned banks allocate less resources to small firms (Alessandrini
et al. 2005; Berger et al. 2005; Mian 2006).
Consolidations involving large banks lead to a reduction in small firm loans
(Berger et al. 1998; Sapienza 2002; Alessandrini et al. 2006).
In Italy the birth rate of firms is positively associated with the share of deposits
held by chartered banks in each province (Bonaccorsi and Dell’Ariccia 2004).
In US the correlation between local economic growth and the number of bank
offices differs with the locus of their ownership (Collender and Shaffer 2003).
Ancona, 23 September 2006
P. Alessandrini, A. Presbitero and A. Zazzaro
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Measuring Distances


Operational Proximity is
measured by branch density:
 Branchesk
OP j 
Functional Distances are built
weighting local bank branches
with the distance from their own
bank’s headquarter measured by:

Km distance.
kj
 10000
Population j
 Branchesk
kj
FD1 j 
j
j

 ln 1  KM jz
 Branchesk
kj



j

Absolute difference in social
 Branchesk  ln 1  SC j  SCz
k
capital, measured as
FD 2 j 
participation rate at referenda.
 Branchesk
j
j
kj
Ancona, 23 September 2006
P. Alessandrini, A. Presbitero and A. Zazzaro
j
6

Measuring Financing Constraints
Three indicators of financing constraints:




A dummy variable if the firm states it is
credit rationed;
The sensitivity of investment to cash flow;
Ratio between credit lines used and those
available.
Ancona, 23 September 2006
P. Alessandrini, A. Presbitero and A. Zazzaro
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The Dataset

Information on firms, bank office location, bank
type, composition of banking groups, credit
market, institutional characteristics and macro
variables.

Sources: MCC-Capitalia surveys, Bank of Italy,
ISTAT.

The surveys include balance sheet data (12,627
observations); 526 firms present in every survey
(rotating panel).
Ancona, 23 September 2006
P. Alessandrini, A. Presbitero and A. Zazzaro
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Credit Rationing

The basic model is a pooled probit on 7974 observations:

 
Pr RATij  f OP j , FD j , FIRMmi , PROVni


FIRM: return on investment (ROI), degree of
indebtedness (DEBT), propensity to innovate (R&D), firm
size (SIZE), bank-firm relationships (BANKS and
BANK_PR).

PROV: characteristics of the banking system (market
concentration and degree of localism), institutional
environment (the efficiency of courts).

Geographic and time dummies.
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P. Alessandrini, A. Presbitero and A. Zazzaro
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Probit results
Dependent Variable:
Pr(Rationing)
OP
FD1
FD1×SIZE
FD2
FD2×SIZE
Pseudo-R2
GEO dummy
(1)
(2)
Ancona, 23 September 2006
(4)
(5)
(6)
(7)
(8)
-0.026*** -0.010* -0.025***
-0.01
-0.021*** -0.011** -0.021*** -0.010*
0.016*** 0.011** 0.042*** 0.035***
-0.007*** -0.006***
0.039*** 0.028** 0.106*** 0.091***
-0.017*** -0.016***
0.068
0.071
0.067
0.07
0.069
0.071
0.067
0.069
No
Yes
No
Yes
No
Yes
No
Yes
Effect of FD (1996 - 2003) on Pr(Rationing)
Large firms (250 workers)
Small firms (25 workers)
Small southern firms
Pr(Rationing)
(3)
0.21
1.11
1.61
0.02
0.80
1.14
Whole sample
14.8%
South
Note: All coefficients are marginal effects
P. Alessandrini, A. Presbitero and A. Zazzaro
0.19
1.09
2.24
0.01
0.81
1.62
26.6%
10
Investment-Cash Flow

The basic model is a GMM estimation on a balanced
panel of 279 firms over the period 1996-2003.
 INVt

 K t 1

 INVt 1 
 CF 
 CF 
    b1 
  b 2  t   b 3  t   OPjt 
 ij
 K t  2  ij
 K t 1  ij
 K t 1  ij
 CF 
 b 4  t   FD jt  b 5 GROWTH ijt   i   t   i ,t
 K t 1  ij

The effect of distances on financing constraint is given
by b4.
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P. Alessandrini, A. Presbitero and A. Zazzaro
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One-step System-GMM results
Dependent
variable: (INV/K)
(INV/K) (-1)
(CF/K)
(CF/K)×FD1
(CF/K)×FD2
(CF/K)×OP
OBS
Marginal effect
Ancona, 23 September 2006
Pooled
Small
Large
Pooled
Small
Large
0.002*** -0.125** 0.002*** 0.002*** -0.122** 0.002***
-0.442
-4.582***
-0.22
0.354***
0.536**
0.385***
0.015
0.644***
-0.046
-0.232
-3.487***
0.027
0.933***
1.400**
0.818***
0.012
0.503**
-0.027
1953
357
1596
1953
357
1596
0.623
0.831
0.568
0.625
0.711
0.561
P. Alessandrini, A. Presbitero and A. Zazzaro
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Credit Lines Drawn

Dynamic GMM on aggregated data at provincial level
over the period 1997-2003.
log CRED jt    b1 log CRED jt 1  b 2OP jt  b 3 FD jt 
 n PROVnjt  i   t   it
n


We estimate separate models according to 5 loan size
classes.
We control for credit market concentration, degree of
localism and per capita value added.
Ancona, 23 September 2006
P. Alessandrini, A. Presbitero and A. Zazzaro
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One-step System-GMM results
Loan Size
Dependent
variable: CRED CRED_ CRED_ CRED_ CRED_ CRED_ CRED_ CRED_ CRED_
1
2
3
SME
1
2
3
SME
CRED (-1)
0.677*** 0.810*** 0.678*** 0.784*** 0.738*** 0.819*** 0.665*** 0.804***
OP
-0.006**
-0.003
FD1
0.008**
0.006**
-0.008** -0.004** -0.007**
0.005
Ancona, 23 September 2006
0.009
618
618
618
-0.009** -0.005**
0.005**
FD2
OBS
-0.003
618
618
P. Alessandrini, A. Presbitero and A. Zazzaro
-0.001
618
0.004
618
0.006
618
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Concluding Remarks



Empirical evidence shows that increased
functional distance makes financing constraints
more binding.
The negative impact of FD survives
independently of the density of bank offices
and the degree of competition.
The negative impact of FD is particularly
significant for small firms in southern regions.
Ancona, 23 September 2006
P. Alessandrini, A. Presbitero and A. Zazzaro
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