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Effect of Relationship Banking on Financing cost and
Performance of SMEs: Evidence from Panel Data of Korean
Small Firms
Moon-Kyum Kim and Gyoo-Ok Lee
Soongsil University, Seoul, Korea
1. Introduction
The fact asymmetric information problems are apt to be much more serious in SMEs than
in large companies makes the ways in which they raise debt capital differ significantly. In
other words, SME financing through commercial banks often involves a long-term
relationship that may mitigate these information problems. As Berger and Udell (2002)
mentioned, one of the features of small and medium enterprise (SME) financing is that
personal experience and subjective judgment of loan officer in financial intermediaries,
particularly commercial banks, can affect decisions and terms of loan for SMEs while large
companies typically obtain credit in the public debt markets. This close relationship between
banks and companies in financing is called ‘relationship banking’ and Boot (2000) mentioned
that the relationship banking is established when banks make investments to get clients’
information exclusively and provide various financial services through consistent and wide
range of interactions.
The relationship banking in Korea has evolved from the one which resembles ‘main-bank
system’ in Japan. Korea had adopted the main-bank system to control credit for large
business conglomerates in 1982. Those main-banks played roles of controlling client
companies financing plans and investments, performing financial analyses, and even
promoting the improvements of capital structures.
1
This study tried to investigate how the relationship banking affects financing costs of
SMEs and SME performance from perspectives of traditional corporate finance and SME
theories. Most of existing researches have tried to figure out which party in the relationship
banking, between companies and banks, has more superior position in terms of marginal
profits and costs. They used a data of public companies, which usually are not SMEs, to
analyze the relationship banking. We, however, believe that there must be a different shape of
the relationship banking for SMEs and tries to find impacts on the level of interest rates
charged by banks, the size of interest payments by SMEs, and the managerial performance of
SMEs from the perspective of the relationship banking. This study has several features
differentiating it from other researches. First, this study has used only non-public SMEs in
analysis, which expands our understanding of the relationship banking into the area of non-
listed SMEs. Second, we tested if there exists any difference in effects of the relationship
banking according to the capitalization size and ages of SMEs. Lastly, this study tried to see
not only how the relationship banking affects the level of financial cost, but also the
performance of SMEs, which would be the first attempt to relate the relationship banking to
company performance like profitability and growth.
2. Literature review
Boot (2000) and Bornheim & Herbeck (1998) said that relationship banking provides
clients with differential financial services based on mutual trust established over long-run
business relation which promotes private information flows. Also, the relationship banking
has features of monopolizing client firms’ information by banks and implicit long-run
contract between two parties, which produces positive effects such as security of contractual
2
flexibility between banks and client firms, easiness of collateral requirement, reduction of
transaction cost through accumulation of information. S. J. Kim (2007) mentioned that
through the main-bank system in Korea, firms can mitigate problems of information
asymmetry and expand credit availability by making close and special relationship with
banks and concentrating borrowing on main-bank. Weinstein & Yafeh (1998), Petersen &
Rajan (1994) and Harhoff & Korting (1998) said that strong relationship with banks
increased fund availability but other effects were not clearly found. Rajan (1992) and Sharpe
(1990) argued that banks possibly control firms when the business relationship between bank
and firm get stronger over time and banks could raise financing cost by utilizing information
that banks get through the relation, which, in turn, increases social cost. Thaker (1996) and
Detragiache et al. (2000) argued that firms with one bank relation have less capacity raising
capital in the time of liquidity crisis than firms with multi bank relation.
Likewise, researches of the relationship banking for SMEs showed mixed results. Berger &
Udell (1995) using a SME data in US reported SMEs with long and close relation with banks
have lower interest rates and smaller collateral requirements. Gines & Pedro (2007) found out
by utilizing Spanish SME data that firms with one bank relation have lower rise in interest
rates and financial advantages over firms with multi bank relation. Degryse & Van Cayseele
(2000) reported in the study of German SMEs that the period of relationship banking had a
positive relationship with borrowing interest rates while the range of financing sources had a
negative relationship with interest rates. This implied that when a firm had a multi bank
relationship, there were a completion among financial providers so that lending rates
decreased and bank’s monopoly power diminished.
3. Data and methodology
3
The data used in this study were obtained from Industrial Bank of Korea data base. Industrial
Bank of Korea is a commercial bank with a focus on SME financing, so that it maintains a
consistent and complete database for SMEs. The data analyzed in this study was three year
panel from 2006 through 2008 which encompass total 7,618 SMEs with more than debt of
about 0.9 million US dollars (1 billion Korean Won). Variance Inflation Factor (VIF) was
applied to check if the problem of multicollinearity existed with the data. It was found that
the value of VIF was less than 2 so that it would not harm running regression analysis. The
variables included in regression analyses are suggested in Table 1.
Table 1
Definition of Variables in Regression Analyses
Variables Name Definition
CDS Market Interest Rate – 3month KORIBOR
Dependent RAB Interest Payment / Total Borrowing
Variables INR Operating Income / Total Assets
GRO (Total Assetst - Total Assetst-1) / Total Assetst-1
LLT log(Borrowing Period)
Level of Client Contribution: log(1-best, 2-better,
CGI
3-normal, 4-bad, 5-worse)
TAR Tangible Asset / Total Assetst
(Borrowing from Bank t - Borrowing from Bank t-1) /
BLI
Independent Borrowing from Bank t-1
Variables NLL Credit Loan / Total Loan
log(7 levels according to the number of client’s
CRS
account in the bank)
OCF Operating Cash Flow / Total Assets
SIZE Log(Total Assets)
AGE log(Age of Firm)
DBANK If the number of main bank is one then1, others 0
Dummy
∑DYEAR Three year dummy
4
Classification of firms is made by capitalization size of 6.5 million US dollars (7 billion
Korean Won), which requires external auditing. If the size of capitalization is greater than 6.5
million US dollars, firms are categorized as medium firm while small firm with less
capitalization. Also each category is divided into two subsequent categories, so that total four
categories are involved in the analyses. Also, another classification was introduced in terms
of age: one to five years, six to ten years, eleven to twenty years and more than twenty years.
There are total four regression models are engaged. Equation (1) and (2) are for
investigating effects of the relationship banking on the level of interest rates and interest
payments. The variable of ∑DYEAR is included to control a possibility of spurious correlation.
(1)
(2)
Equation (3) and (4) are for investigating effects of the relationship banking on the firm’s
performance (profitability and growth). The variables of CDS and RAB which are dependent
variables in equations of (1) and (2) to control the effects of financial activities on
profitability and growth which are the results of operating activities.
4. Analysis
4.1 Statistics and correlations
The general level of interest rates is lower in the categories of Less than 2 billion KRW and 2
billion ~ 7 billion KRW comparing to the other two categories. The reason for this
5
phenomenon is that government’s SME promotion policies offer relatively cheap financing
measures to SMEs.
Table 2
Statistics according to capitalization
Total Less than 2 billion KRW 2 billion ~ 7 billion KRW
Variables
Avg. Median Std. Avg. Median Std. Avg. Median Std.
CDS 2.13225 2.14819 1.98233 2.02039 1.96988 1.86328 2.12834 2.17015 1.96191
RAB 0.04388 0.04299 0.02123 0.04614 0.04514 0.02346 0.04519 0.04425 0.02226
INR 0.06529 0.06449 0.06994 0.09371 0.08729 0.08154 0.06381 0.06398 0.05949
GRO 0.31035 0.13554 1.88291 0.37552 0.12590 0.97658 0.28689 0.12273 0.63858
LLT 0.86341 0.88845 0.28732 0.77876 0.84509 029292 0.83446 0.84509 0.27523
CGI 0.21950 0.22160 0.24096 0.27395 0.30103 0.22351 0.20174 0.00000 0.23530
TAR 0.43855 0.43815 0.22237 0.45995 0.47845 026043 0.45231 0.45811 0.21188
BLI 0.79189 0.09584 25.88987 1.49294 0.17628 27.50794 0.68631 0.08895 27.22176
NLL 0.30122 0.29000 0.36931 0.27865 0.25636 0.27535 0.26132 0.25681 0.31660
CRS 0.73126 0.75577 0.12089 0.68724 0.69897 0.13928 0.73407 0.77815 0.11387
OCF 0.03048 0.02773 1.48987 0.05489 0.04572 0.21869 0.02703 0.02443 0.13422
SIZE 6.71042 6.67207 0.42886 6.12845 6.17185 0.16029 6.59032 6.59252 0.15979
AGE 1.09101 1.07986 0.23266 1.02474 1.00000 0.21878 1.06045 1.04139 0.21795
7 billion ~ 20 billion KRW More than 20 billion
Variables
Avg. Median Std. Avg. Median Std.
CDS 2.15484 2.19992 2.01032 2.28408 2.20595 2.19658
RAB 0.0190 0.04100 0.01768 0.03795 0.03762 0.01695
INR 0.05296 0.05402 0.07604 0.05483 0.05265 0.07611
GRO 0.32467 0.16042 3.79504 0.30289 0.16883 1.01672
LLT 0.93517 0.95424 0.27491 1.00103 1.04139 0.28952
CGI 0.20379 0.00000 0.24709 0.30103 0.26418 0.26531
TAR 0.41926 0.41178 0.20031 0.37084 0.35268 0.18576
BLI 0.58393 0.07191 24.68974 0.70239 0.07911 16.59756
NLL 0.32957 0.33253 0.50431 0.49486 0.49695 0.35220
CRS 0.75213 0.77815 0.11250 0.74082 0.77815 0.12677
6
OCF 0.02363 0.02487 0.12844 0.02539 0.02818 0.12400
SIZE 7.04068 7.02165 0.12999 7.58052 7.51290 0.23769
AGE 1.14820 1.14612 0.23346 1.23981 1.27875 0.24162
However, the absolute burden of interest payments is bigger in the categories of Less than 2
billion KRW and 2 billion ~ 7 billion KRW comparing to the other two categories. This
finding indicates smaller firms depends on more debt financing than larger firms.
Table 3
Correlation Coefficients
Variables CDS RAB INR GRO LLT CGI TAR BLI NLL CRS OCF SIZE AGE
CDS 1.000
RAB 0.223** 1.000
INR -0.032** 0.109** 1.000
GRO -0.025** -0.086** 0.011 1.000
** ** **
LLT 0.065 0.060 -0.080 -0.086** 1.000
CGI -0.025** -0.020** -0.033** -0.003 -0.095** 1.000
TAR -0.132** -0.009** -0.088** 0.062** -0.071** 0.129** 1.000
BLI -0.014* -0.034** 0.020** 0.000 -0.009 -0.004 -0.021** 1.000
NLL 0.110** 0.074** 0.068** 0.000 -0.062** 0.153** -0.223** 0.002 1.000
** ** ** ** ** **
CRS -0.097 0.010 0.000 -0.031 0.173 -0.214 -0.098 -0.022 -0.026** 1.000
OCF -0.081** -0.013 0.281** -0.025** 0.016** 0.009 0.185** 0.011 -0.030** -0.014* 1.000
SIZE 0.030** -0.115** -0.174** -0.008 0.244** -0.019** -0.115** -0.013 0.164** 0.139** -0.061** 1.000
AGE 0.024** 0.010 -0.084** -0.058** 0.622** -0.012 -0.024** -0.006 -0.057** 0.040** 0.040** 0.288** 1.000
Note: ** 1%, * 5% significance level
Table 3 shows meaningful positive correlation between CDS and variables of LLT, NLL,
SIZE, and AGE while CDS and variables of RAB, INR, GRO, CGI, TAR BLI, and OCF,
have negative correlations.
4.2 Results of analysis
Table 4 shows the results analyzing the effects of relationship banking on the level of
interest charged and the size of interest payments.
7
Table 4
Effects on the level of interest charged and the size of interest payments
according to the size of SME capitalization
Level of Interest Rates Charged Size of Interest Payments
Variables
Total G1 G2 G3 G4 Total G1 G2 G3 G4
3.696 -5.659 7.655 8.880 1.875 0.198 0.210 0.028 0.216 0.198
Coeff.
(16.638)*** (-4.63)*** (10.531)*** (5.791)*** (1.218) (4.431)*** (3.840)*** (1.789)* (0.583) (4.431)***
0.697 0.864 0.776 0.501 0.237 -0.006 0.016 0.010 0.030 -0.006
LLT
(11.922)*** (6.692)*** (9.809)*** (3.677)*** (1.188) (-1.075) (2.908)*** (6.172)*** (0.920) (-1.075)
-0.385 0.391 -0.537 -0.551 -0.406 -0.009 -0.012 -0.007 0.040 -0.009
CGI
(-6.894)*** (2.700)*** (-6.964)*** (-4.639)*** (-2.294)** (-1.648)* (-1.971)** (-4.194)*** (1.409) (-1.648)*
-0.911 -0.757 -1.017 -1.220 -0.731 -0.051 -0.028 -0.034 -0.064 -0.051
TAR
(-14.82)*** (-5.57)*** (-12.15)*** (-8.412)*** (-2.93)*** (-6.65)*** (-4.34)*** (-18.05)*** (-1.743)* (-6.510)***
-0.001 -0.001 -0.001 -0.002 -0.001 0.000 8.308 8.004 0.013 0.000
BLI
(-2.807)*** (-0.658) (-1.737)* (-2.184)** (-0.259) (1.444) (0.133) (4.148)*** (3.436)*** (1.444)
0.503 0.617 0.709 0.265 0.473 0.005 0.025 0.009 -0.001 0.005
NLL
(13.528)*** (5.075)*** (12.237)*** (4.582)*** (3.386)*** (1.201) (4.679)*** (7.387)*** (-0.059) (1.201)
-2.185 -0.747 -2.407 -2.616 -3.005 -0.002 -0.002 0.000 -0.013 -0.002
CRS
(-19.62)*** (-3.24)*** (-15.24)*** (-10.220)*** (-7.92)*** (-0.684) (-1.064) (0.427) (-1.159) (-0.684)
-0.774 -0.212 -0.605 -1.227 -2.675 0.029 0.025 0.041 0.076 0.029
OCF
(-8.827)*** (-1.450) (-4.616)*** (-5.571)*** (-7.26)*** (2.587)*** (3.869)*** (14.872)*** (1.421) (2.587)***
0.007 1.145 -0.607 -0.542 0.484 -0.014 -0.022 0.006 -0.017 -0.014
SIZE
(0.210) (5.665)*** (-5.471)*** (-2.492)** (2.478)** (-2.351)** (-2.408)** (2.645)*** (-0.330) (-2.351)**
-0.241 0.530 -0.021 -0.719 -0.813 0.005 -0.014 0.003 -0.025 0.005
AGE
(-3.339)*** (3.063)*** (-0.213) (-4.524)*** (-3.45)*** (0.662) (-1.839)* (1.660)* (-0.645) (0.508)
0.019 0.108 0.047 -0.218 -0.386 0.003 0.000 -0.002 0.002 0.003
DBANK
(0.596) (1.699)* (1.127) (-2.554)** (-2.74)*** (0.508) (-0.007) (-2.636)*** (0.084) (0.508)
∑DYEAR - - - - - - - - - -
Adj.R2 0.049 0.054 0.062 0.058 0.088 0.022 0.031 0.066 0.002 0.022
*** *** *** *** *** *** *** *** ***
F-Value 117.305 21.235 81.683 31.156 22.228 5.998 12.293 86.820 2.196 5.998***
Durbin
1.982 2.006 2.006 1.981 1.953 1.974 1.995 1.988 1.995 1.974
-Watson
Note: 1) G 1- Less than 2 billion KRW, G 2 - 2 billion ~ 7 billion KRW, G 3 - 7 billion ~ 20 billion KRW, G 4
– more than 20 billion KRW
2) t-value is given in parenthesis with *** 1%, ** 5%, * 10% significance level
Table 5
Effects on the level of interest charged and the size of interest payments
according to the age of SMEs
Level of Interest Rates Charged Size of Interest Payments
Variable
Total G1 G2 G3 G4 Total G1 G2 G3 G4
3.696 -0.202 0.314 4.955 7.169 0.056 0.157 0.087 0.033 0.078
Coeff.
(16.638)*** (-0.197) (0.658) (11.318)*** (11.687)*** (2.232)** (4.672)*** (6.374)*** (0.421) (5.569)***
0.697 0.517 0.754 0.552 0.679 0.013 0.023 0.018 0.013 0.003
LLT
(11.922)*** (1.916)* (6.619)*** (6.459)*** (5.733)*** (1.934)* (2.789)*** (5.476)*** (0.842) (1.012)
8
-0.385 -0.976 -0.486 -0.234 -0.222 0.003 0.003 -0.006 0.018 -0.013
CGI
(-6.894)*** (-4.26)*** (-5.048)*** (-2.704)*** (-1.803)* (0.433) (0.375) (-2.039)** (1.155) (-4.751)***
-0.911 -0.587 -0.674 -1.120 -1.173 -0.043 -0.040 -0.034 -0.055 -0.026
TAR
(-14.82)*** (-2.467)** (-6.424)*** (-11.791)*** (-7.765)*** (-5.78)*** (-4.86)*** (-10.55)*** (-3.024)*** (-7.093)***
-0.001 0.001 -0.001 -0.001 -0.002 9.858 0.000 0.000 7.867 0.007
BLI
(-2.807)*** (0.171) (-1.327) (-1.740)* (-2.163)** (1.097)*** (-0.723) (3.682)*** (0.553) (11.849)***
0.503 0.717 0.813 0.312 0.555 0.005 0.022 0.014 -0.002 0.009
NLL
(13.528)*** (3.707)*** (10.031)*** (6.185)*** (7.220)*** (1.121) (3.538)*** (6.033)*** (-0.164) (4.714)***
-2.185 -0.795 -1.915 -2.542 -2.541 -0.003 0.004 -0.001 -0.006 -0.001
CRS
(-19.63)*** (-1.951)* (-10.004)*** (-14.565)*** (-10.06)*** (-1.182) (1.540) (-1.125) (-1.020) (-0.978)
-0.774 -1.211 -0.889 -0.524 -1.111 0.044 0.045 0.045 0.050 0.024
OCF
(-8.827)*** (-3.72)*** (-5.785)*** (-4.031)*** (-5.195)*** (4.316)*** (4.257)*** (9.968)*** (2.085)** (4.752)***
0.007 0.061 0.279 0.009 -0.260 0.004 -0.012 -0.003 0.014 0.001
SIZE
(0.210) (0.414) (4.658)*** (1.172) (-3.752)*** (0.979) (-2.58)*** (-1.697)* (1.453) (0.521)
-0.241 2.722 1.133 -0.834 -1.237 -0.005 -0.026 -0.001 -0.038 -0.004
AGE
(-3.339)*** (3.737)*** (3.639)*** (-3.396)*** (-3.868)*** (-0.628) (-1.111) (-0.138) (-0.852) (-0.525)
0.019 -0.094 0.106 0.045 -0.053 -0.001 -0.001 -0.002 3.498 -0.001
DBANK
(0.596) (-0.763) (1.930)* (0.901) (-0.701) (-0.387) (-0.256) (-1.357) (0.004) (-0.453)
∑DYEAR - - - - - - - - - -
2
Adj.R 0.049 0.068 0.052 0.050 0.068 0.003 0.054 0.041 0.001 0.062
*** *** *** *** *** *** *** *** **
F-값 117.305 10.322 46.287 48.840 31.421 7.385 8.320 36.470 2.209 28.390***
Durbin
1.982 1.508 1.307 1.204 1.241 1.999 1.998 1.984 1.999 2.036
-Watson
Note: 1) G 1- 1 through 5 years, G 2 - 6 through 10 years, G 3 - 11 through 20 years, G 4 – more than 21 years.
2) t-value is given in parenthesis with *** 1%, ** 5%, * 10% significance level
Table 6
Effects of the relationship banking on profitability and growth
according to the size of SME capitalization
Profitability Growth
Variable
Total G1 G2 G3 G4 Total G1 G2 G3 G4
0.290 0.591 0.345 0.211 0.074 -0.108 1.064 1.078 -0.914 0.263
Coeff.
(39.976)***(11.378)***(15.955)*** (3.873)*** (1.540) (0.499) (1.649)* (4.631)*** (-0.307) (0.370)
-0.008 0.006 -0.011 -0.010 1.951 -0.511 -0.490 -0.277 -1.351 -0.077
LLT
(-3.927)*** (1.090) (-4.706)*** (-2.075)*** (0.000) (-9.115)*** (-7.515)*** (-11.155)*** (-5.169)*** (-0.807)
-0.011 0.001 -0.009 -0.028 -0.034 -0.158 -0.201 -0.085 -0.419 -0.194
CGI
(-5.781)*** (0.243) (-4.164)*** (-6.858)*** (-5.946)*** (-2.969)*** (-2.752)*** (-3.526)*** (-1.837)** (-2.245)**
-0.046 -0.034 -0.031 -0.080 -0.085 0.657 0.909 0.487 0.824 0.399
TAR
(-21.232)***(-5.337)***(-11.647)***(-14.852)*** (-10.103)*** (10.327)*** (11.929)*** (17.150)*** (2.747)*** (3.144)***
7.710 9.762 3.518 0.001 0.000 0.000 0.000 0.000 0.031 0.001
BLI
(2.962)*** (1.654)* (1.302) (2.047)** (1.967)** (0.226) (-0.342) (1.358) (1.039) (0.328)
0.014 0.033 0.012 0.006 0.021 0.038 0.124 0.098 -0.021 0.066
NLL
(11.036)*** (6.347)*** (7.097)*** (3.092)*** (4.583)*** (1.030) (1.977)** (5.218)*** (-0.183) (0.982)
0.001 0.002 0.003 0.000 0.000 -0.061 -0.080 -0.029 -0.072 -0.141
CRS
(1.559) (0.738) (3.330)*** (-0.271) (0.181) (-2.977)*** (-3.089)*** (-3.210)*** (-0.792) (-3.992)***
0.141 0.083 0.128 0.215 0.220 -0.539 -0.627 -0.371 -0.505 -0.691
OCF
(47.609)***(13.466)***(32.614)*** (27.322)*** (18.169)*** (-6.034)*** (-8.166)*** (-8.518)*** (-1.094) (-3.593)***
-0.029 -0.084 -0.039 -0.012 0.004 0.102 -0.017 -0.054 0.196 0.048
SIZE
(-26.148)***(-9.937)***(-11.945)*** (-1.524) (0.654) (3.164) (-0.165) (-1.534) (0.463) (0.515)
9
-0.007 0.017 -0.009 -0.023 -0.011 -0.081 -0.471 -0.252 0.760 -0.331
AGE
(-2.857)*** (2.298)** (-3.160)*** (-4.163)*** (-1.627) (-1.172) (-5.367)*** (-8.124)*** (2.481)** (-2.916)***
-0.005 -0.012 -0.004 -0.006 0.003 -0.008 -0.027 0.002 -0.122 0.099
DBANK
(-4.340)*** (-4.548)*** (-3.456)** (-1.814)* (0.463) (-0.243) (-0.814) (0.132) (-0.737) (0.956)
∑DYEAR - - - - - - - - - -
-0.001 0.000 0.000 -0.003 -0/004 -0.013 -0.011 -0.003 -0.019 -0.012
CDS
(-4.633)*** (0.284) (1.285) (-6.082)*** (-6.498)*** (-1.983)** (-1.258) (-1.078)*** (-0.698) (-1.195)
0.004 0.058 0.066 0.002 -0.027 -0.079 -1.026 -2.748 -0.016 0.008
RAB
(2.246)** (3.627)*** (5.068)*** (0.923) (-1.202) (-1.429) (-5.271)*** (-20.226)*** (-0.138) (0.025)
0.671 0.140 0.521 1.017 1.050
INR
(3.517)*** (0.681) (5.392)*** (1.300) (3.321)***
0.001 0.001 0.005 0.000 0.005
GRO
(3.517)*** (0.681) (5.392)*** (1.300) (3.321)***
Adj.R2 0.139 0.132 0.112 0.179 0.192 0.013 0.105 0.117 0.006 0.026
*** *** *** *** *** *** *** *** ***
F-값 284.831 42.630 119.507 82.758 41.151 24.538 32.918 123.474 3.427 5.564***
Durbin-
1.951 1.939 1.999 1.950 1.999 2.002 2.025 2.017 2.003 1.952
Watson
Note: 1) G 1- Less than 2 billion KRW, G 2 - 2 billion ~ 7 billion KRW, G 3 - 7 billion ~ 20 billion KRW, G 4
– more than 20 billion KRW
2) t-value is given in parenthesis with *** 1%, ** 5%, * 10% significance level
Table 7
Effects of the relationship banking on profitability and growth
according to the age of SMEs
Profitability Growth
Variable
Total G1 G2 G3 G4 Total G1 G2 G3 G4
0.290 0.348 0.271 0.309 0.280 -0.108 2.418 0.518 -1.063 -0.314
Coeff
(39.976)*** (9.182)*** (16.248)*** (23.031)*** (13.425)*** (-0.499) (3.554)*** (2.682)*** (-1.617) (-2.807)***
-0.008 -0.011 -0.011 -0.006 -0.002 -0.511 -0.690 -0.459 -0.702 -0.076
LLT
(-3.927)*** (-1.176) (-2.849)*** (-2.381)** (-0.544) (-9.115)*** (-4.318)***(-10.086)*** (-5.572)*** (-3.581)***
-0.011 -0.020 -0.016 -0.010 0.001 -0.158 -0.537 -0.145 -0.206 -0.063
CGI
(-5.781)*** (-2.522)** (-4.735)*** (-3.645)*** (0.278) (-2.969)*** (-3.851)*** (-3.797)*** (-1.631) (-2.887)***
-0.046 -0.078 -0.055 -0.036 -0.046 0.657 1.520 0.636 0.687 0.314
TAR
(-21.232)*** (-7.990)*** (-13.694)***(-11.441)*** (-8.362)*** (10.327)*** (9.024)*** (13.955)*** (4.566)*** (11.006)***
7.710 0.003 0.000 5.908 0.001 0.000 0.016 0.003 9.106 0.012
BLI
(2.962)*** (4.041)*** (2.869)*** (2.451)** (1.559) (0.226) (1.324) (1.523) (0.079) (2.643)***
0.014 0.032 0.017 0.011 0.011 0.038 -0.031 0.131 0.011 0.051
NLL
(11.036)*** (4.579)*** (5.681)*** (7.021)*** (4.155)*** (1.030) (-0.253) (3.955)*** (0.148) (3.581)***
0.001 0.011 0.001 0.000 0.003 -0.061 -0.216 -0.052 -0.059 -0.042
CRS
(1.559) (3.233)*** (0.928) (0.200) (1.737)* (-2.977)*** (-3.816)*** (-3.673)*** (-1.212) (-4.702)***
0.141 0.130 0.154 0.109 0.204 -0.539 -0.616 -0.514 -0.526 -0.385
OCF
(47.609)*** (10.987)*** (27.926)*** (26.872)*** (27.370)*** (-6.034)*** (-2.857)*** (-7.846)*** (-2.619)*** (-9.117)***
-0.029 -0.037 -0.027 -0.033 -0.018 0.102 -0.257 0.015 0.184 0.099
SIZE
(-26.148)*** (-7.129)*** (-12.578)***(-20.693)*** (-7.661)*** (3.164)*** (-2.756)*** (0.612) (2.364)** (7.843)***
-0.007 0.002 -0.001 -0.001 -0.062 -0.081 -0.182 -0.162 0.438 -0.162
AGE
(-2.857)*** (0.096) (-0.055) (-0.104) (-5.594)*** (-1.172) (-0.403) (-1.290) (1.200) (-2.794)***
-0.005 -0.004 -0.002 -0.006 -0.006 -0.008 -0.140 -0.003 0.007 0.012
DBANK
(-4.340)*** (-1.006) (-1.093) (-3.776)*** (-2.274)** (-0.243) (-1.845)* (-0.114) (0.090) (0.896)
∑DYEAR - - - - - - - - - -
10
-0.001 -0.001 -0.002 -0.001 0.000 -0.013 0.038 -0.008 -0.022 0.008
CDS
(-4.633)*** (-1.108) (-4.137)*** (-2.856)*** (-0.887) (-1.984)** (2.086)** (-1.806)* (-1.418) (2.854)***
0.004 0.034 0.047 0.004 0.005 -0.079 -3.441 -1.273 -0.029 -1.084
RAB
(2.246)** (1.066)*** (3.464)*** (1.980)** (0.205) (-1.429) (-6.312)*** (-8.277)*** (-0.338) (-8.991)***
0.671 0.473 0.743 0.703 0.628
INR
(3.517)*** (0.963) (5.919)*** (1.411) (7.754)***
0.001 0.002 0.006 0.000 0.023
GRO
(3.517)*** (0.963) (5.919)*** (1.411) (7.754)***
Adj.R2 0.139 0.220 0.134 0.128 0.183 0.013 0.128 0.057 0.006 0.082
*** *** *** *** *** *** *** *** ***
F-값 284.831 28.761 98.920 104.347 72.847 24.538 15.439 39.390 5.118 29.827***
Durbin-
1.919 1.949 1.904 1.930 1.934 1.992 1.981 1.944 2.001 1.999
Watson
Note: 1) G 1- 1 through 5 years, G 2 - 6 through 10 years, G 3 - 11 through 20 years, G 4 – more than 21 years.
2) t-value is given in parenthesis with *** 1%, ** 5%, * 10% significance level
5. Conclusion
The result showed that the relationship banking has differential effects on the level of interest
charged and the size of interest payment depending upon capital sizes and ages of SMEs. The
effects of relationship banking were not clearly found in other areas, but we noticed that
small start-up companies have motivation to make a close relationship with banks.
The effects of Main bank relationship (or one-bank relationship) lowering the level of interest
charged was more apparent for the group of medium-size companies with more than
capitalization of 6 million US dollars (7 billion Korean won) and an obligation of external
auditing comparing to smaller company groups. It was also found that the
11
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