Ｃomment on “The Role ofCollateral and Personal
Guarantees in Lending
Relationships:..”Colin McKenzie
Keio UniversityFocus of the Paper
What are the roles of collateral and personal
guarantees in loans to SMEs? Do they avoid
adverse selection or moral hazard? FOUND:
Collateral more likely to pledged by risky lenders{ Do they substitute for screening and monitoringof financial institutions?
FOUND: Collateral and guarantees appear to be
associated with more monitoring{ What is the impact of long-term relationships oncollateral?
FOUND: Collateral/Guarantees are complementaryto relationship lending{Data Set
Matches the “Survey of the Financial Environment”(2002) with the Tokyo Shoko Research (TSR)
data base{ Sample restricted to firms with capital of up to300 million yen or up to 300 regular employees{ Sample covers large SMEs (median employees 36,median capital 2 million){ Sample that can be used for regression analysis isabout 5000 (not exactly clear in the paper){ Only loan applications that have been accepted
are covered (unavoidable sample selection){

Model for Collateral(1){ COLL*i=a+bRATEi+cGUARi+dMONITORINGi+eCONTROLi+ei{ COLL = 1 if COLL*i>0= 0 if COLL*i<0{ Model estimated by standard probit
model and techniques allowing for
endogeneity of RATE & GUAR
{ Endogoneity of RATE confirmed

Model for Collateral (2){ When endogeneity is allowed for,GUAR has a positive & significant
effect, and RATE has a negative
significant effect. A reduction in
document monitoring reduces theprobability of collateral.
(complementarity) [Table 13]

Model for Guarantee{ Essentially the same model as for

Collateral{ Collateral and RATE are found to beexogenous{ The presence of collateral, increases
in interest rates, and increases indocument monitoring increase thelikelihood of guarantees. [Table 14]

Model for Interest Rate
{ Ratei=a+bGUARi+cCOLLi+dMONITORINGi+eCONTROLi+ei{ Estimated only by OLS. No tests forendogeneity. Why not? Given results for
Table 13 might expect Collateral to beendogenous.
{ Existence of collateral and guarantees
both increase interest rate. Reductions inmonitoring lead to interest rate falls.
[Table 15].

Comment 1:
Representative Nature of of the Sample{ Paper needs to make more effort to show

how representative the sample is.
{ How are the initial 15,000 surveyed
companies chosen?
{ What are the characteristics of the 70009000 companies responding?
{ How do the sample characteristics relate
to other samples? Stated that firm size isfound to large.

Comment 2: Monitoring Variables(DOCFREC & CONTACTFREC)
Definition of variables needs more consideration{ DOCFREQ takes the values 1 (every 1-2
months),2 (quarterly), 3 (semiannual), 4 (annual).
Does a move from 2 to 3 and 3 to 4 really havethe same meaning?
{ In terms of the number of document checks peryear, these values corresponds to 6-12, 4, 2, 1
{ Alternatively use a dummy variable for eachoption{ Appropriate to treat this variable as exogenous?
{ Is this variable determined at the time of thecontract or later?
{

Comment 2: Monitoring Variables(DOCFREC & CONTACTFREC)
Similar argument applies to CONTACTFREC evenmore strongly{ CONTACTFREC the values 1 (every day),2(weekly), 3 (one every two weeks), 4 (monthly),5(bi-monthly), 6 (quarterly), 7(semi annual), 8(annual), 9 (no contact). Does a move from 2 to
3 and 3 to 4 really have the same meaning?
{ In terms of the number of document checks per
year, these values corresponds to 365(?), 52, 12,
6,4,2,1,0{ Alternatively use a dummy variable for eachoption{ Appropriate to treat this variable as exogenous?
{ Is this variable determined at the time of thecontract or later?
{

Comment 3: Treatment of Credit
Guarantees Inappropriate
Lack of discussion of credit guarantees until page
28. This is a critical point that needs to beintroduced much earlier.
{ Gives the reader the impression that there arestrong doubts about the meaning andinterpretation of the analysis to date mean.
{ Given that half the loans have credit guarantees
the analysis of interest rates, personal guarantees
and collateral must include some variable(s) totake account of the presence/absence of credit
guarantees, e.g. credit guarantee dummy variable{ Possibility of sample selection bias (analysis inTable 16 is conditioned on no credit guarantees){

Comment 4:
What about long-term loans?
{ What is the real nature of the data? Do wehave information on individual loancontracts?
{ The analysis appears to be restricted toshort-term loans(?) [given the use of theshort-term interest rate]. Is that really thecase?
{ For longer maturity loans, problems of
moral hazard likely to more important. As aresult, collateral may be more important inthese cases.
{ Would need to worry about macro
conditions at the time of the loan?

Comment 5:
Analysis of Credit Guarantees{ While not the current focus of the paper,

this data set provides an excellent dataset to examine the impact creditguarantees.
{ Limiting the sample to those cases withcollateral and personal guarantees, still
has a large sample, and provides acommon base to examine the impact ofcredit guarantees.
{ Given that banks bear no risk, we might
expect a ‘lazy bank’ outcome here!

Comment 6:
Alternative Choice of Samples
Collateral Guarantee Credit Sample
X
X
X
889
0X
X
A
X
0X
BX
X
0
65
00
X
1413
0X
0C
X
00D
0002819
A+C=627
B+D=752 C+D=497
Source: Table 1Possible to compare groups that differ in only one
characteristic?

Comment 7:
Alternative to Binary Choice{{

Rather than deal with COLL andGUAR separately, what about
using a multi-nomial logit model?
Choices:01
2
3

No collateral and no guarantee
Collateral and no guarantee
Guarantee and no collateral
Guarantee and collateral