Discussion of:
Credit Availability. Identifying
Balance-Sheet Channels with Loan
Applications
Nicola Cetorelli
Federal Reserve Bank of New York
Fourth BI-CEPR Conference on Money, Banking and Finance
Rome, October 2-3, 2009
The views expressed in this paper do not necessarily reflect the position of the Federal Reserve Bank of
New York or the Federal Reserve System.
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Framing this paper in the literature

There is a vast literature exploring the
connections between the financial sector
and the real economy
Do shocks to financial institutions transmit
to the real sector?
Firms’ balance sheet
Cash
Funding !!
3
!! Assets
Banks balance sheet
Capital
Liquid
assets
Loans
!!
Funding
!!
1
Capital
!!
2
4
e.g., Khwaja and Mian, AER 2008
e.g. Kashyap and Stein, AER 2000
Paravisini, JF 2008
3
Main contributions of this paper
Go at the heart of the demand/supply
identification problem
 Unit of observation is an actual loan
application

Potential loan demand
Firms not
asking for a
loan
Firms in
need of a
loan
Main contributions of this paper
Empirical strategy based on determinants
of likelihood application is approved
 Identification coming from heterogeneous
impact across banks of different
characteristics and firms of different
characteristics

Do shocks to financial institutions transmit
to the real sector?
Firms’ balance sheet
Cash
Funding !!
3
!! Assets
4
Banks balance sheet
Capital
Liquid
assets
!!
Loans
Funding
!!
1
Capital
!!
2
7
Findings
Banks are affected by “shocks” to their
funding sources
 Shocks are transmitted to banks’ lending

Comments
Potential demand
Not in dataset
Apply but banks
do not request info.
In dataset
Apply and banks
request info.
Not in dataset
In need of funds
but do not apply.
Not in dataset
Potential loan demand
Not in dataset
In dataset
The composition between
observable and nonobservable demand may
change with macro
conditions.
Not sure which way it
goes.
Not in dataset
Potential loan demand
Not in dataset
In dataset
Not in dataset
In bad times, better firms may
choose not to apply if at the
margin bank funding becomes
more expensive.
Left in dataset is a
disproportionately “worse”
pool.
May lead to overestimate of
effects.
Potential loan demand
Not in dataset
In dataset
Not in dataset
In bad times, better firms may
choose to apply to obtain
quality certification from bank
loans.
Leads to dataset of
disproportionately “better”
pool.
May imply under-estimate of
effects.
Potential loan demand
Not in dataset
In dataset
Not in dataset
Apply but banks do not
request info. In this
unobservable group we
have the current bank
clients.
In bad times banks may
choose to shift resources
more toward old clients.
Effect found still
legitimate, but it requires
qualifications.
Should be able to delve deeper


May be able to get info on the unobservable
components of demand (at least some).
Spanish Credit Register has info on all credit
exposures of existing bank clients vis-à-vis all
banks. You could observe to what extent they
are getting (new) credit while others apply and
get denied.
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Should be able to delve deeper

Likewise, may be able to say something about
firms not applying (harder task). At least in
aggregate terms? (Not sure what is the data
source for the characteristics of the firms).
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Should be able to delve deeper

Encouragement to push further. With the
identity of the firms should be able to address
part “3” and “4” of the big picture.
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Do shocks to financial institutions transmit
to the real sector?
Firms’ balance sheet
Cash
Funding !!
3
!! Assets
4
Banks balance sheet
Capital
Liquid
assets
!!
Loans
Funding
!!
1
Capital
!!
2
17
Should be able to delve deeper



Encouragement to push further. With the
identity of the firms should be able to address
part “3” and “4” of the big picture.
Use firm data to measure overall change in
funding positions.
Use firm data to hopefully gauge ultimate impact
on their asset side (impact on capital investment,
growth, etc.).
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