Discussion Paper No.262

Relationship lending and the role of loan officers in China:
Empirical analysis based on the data on companies inindustrial clusters
Cheng Tang
Chuo University

February 2016

INSTITUTE OF ECONOMIC RESEARCH
Chuo University
Tokyo, Japan1

Relationship lending and the role of loan officers in China:
Empirical analysis based on the data on companies in industrial clusters

Cheng Tang*

Abstract
Relationship lending refers to lending behavior in which a financial institution makes loan decisions
based on the accumulated soft information on the borrower. Empirical studies have revealed its positiveeffect on lending. However, questions concerning who generates and accumulates soft information andin what ways in China have never been analyzed empirically. This paper aims to explain empiricallywhether loan officers play a role in generating soft information in relationship lending by using datacollected through survey conducted among SMEs located in the apparel industry clusters in Zhejiang
and Jiangsu provinces. Based on the results of this study, relationship banking in China pertaining to therole of loan officers seems even more important than generally recognized.

Key words: Relationship lending; Soft information; Small-and medium-sized enterprises
JEL classification : G21,G31*

Professor, Faculty of Economics Chuo University; Email:tang@tamacc.chuo-u.ac.jp

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1. Introduction
A vast amount of research on relationship banking—or relationship lending—accumulated since the1990s shows the importance of this business model (e.g., positive impact on lending). For example, Boot
(2000), who is known for corporate finance research, argues that relationship banking is actually the best
approach to solving information asymmetry. In Japan, specific action items for regional financial
institutions were laid out in the “Action Program Concerning the Enhancement of Relationship Banking
Functions”, which was released by the Financial Services Agency in March 2003. The purpose of the
program was to enhance the function of relationship banking.
Lending through relationship banking as a business model has also generated research interest among
academics in China in recent years. For example, Hé (2010) used World Bank’s 2003 survey data on
2,400 companies to show that as interest rate lowered and the number of banks with which thesecompanies do business with increases, the length of business relationship with a particular bank does not
significantly affect the lending terms. Hé (2013) also showed that a stronger relationship reduced thecollateral cost at the time of obtaining a loan. In addition, Tang (2011, 2012) conducted an empiricalanalysis on relationship banking based on the data collected on small and medium-sized enterprises(SMEs) located mainly in three regions, namely, Ningbo, Shaoxing, Jiaxing/Jiashan, and Huzhou, where
textile industry clusters in Zhejiang Province are situated. His research showed that dealing with asmaller number of banks and cultivating a long term relationship with them had the positive effect of
reducing the level of financial constraints on the borrowing parties. He also demonstrated the tendency
that as the relationship between borrowing industry and the lending banks grew closer and the possibility
for more intimate relationship lending in terms of financing for SMEs within industry clusters increases,
the terms and conditions under which the borrowers conducted business with the banks (e.g., collateral)
improved.
Previous studies on the benefit of relationship lending in China often focus on the impact on loweredinterest rate, reduced funding constraints, and the availability of funding. As a result, these analyses tend
to affirm theoretically expected benefits, that is, the effectiveness of relationship lending, such as
reduced information asymmetry, attributed to soft information that lenders obtain through their close
relationship with borrowers.
However, while relationship lending is a lending behavior in which loan decisions are made based on
soft information that the financial institution has accumulated on borrowers, it is also important to
explore who generates and accumulates such information and in what ways. Muramoto (2010) points outthat soft information is generated more by individuals than by organizations and accumulated by loan2

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officers in many cases. Xu (2010) showed that the financing for SMEs by commercial banks is generally
broken down into five stages: accepting the loan application, researching, screening, approving, andfinancing. It is conceivable that loan officers play an important role in producing and accumulating soft
information during these processes.1 However, even though the importance of loan officer’s role wasrecognized, to date, no one has demonstrated this empirically.
Thus, using a survey conducted among SMEs located in the apparel industry clusters in the Zhejiang
and Jiangsu provinces, this paper demonstrates empirically whether loan officers are playing a role inproducing soft information under relationship lending in China and sheds light on our understanding of
the role of loan officer in concrete terms.2 There are two critical issues this paper hopes to address. First,
what is the actual financing behavior of loan officers and whether they are playing an important role as
generators of soft information on companies. Second, once we establish that loan officers are importantand the soft information they generate actually has a positive effect on relationship lending, then we cansay that relationship lending has its merit.
With these two objectives in mind, I will review previous studies on loan officers in Section 2 toshow that this paper is the first empirical study on China. In Section 3, I will propose two hypotheses forthe empirical study, provide an overview of the individual company data used in this paper, and analyze
independent variables and descriptive statistics. In Section 4, I will conduct empirical analyses on those
two hypotheses to explain the role of loan officers in China. Finally, I will summarize the main findingsof this paper in Section 5 and describe future research tasks.

2. Previous Studies
Among the few previous studies on the role of loan officers, Scott (2006), for example, analyzed that
financing could worsen when the loan officer changes. Garica-Appendini (2007) pointed out that soft
information becomes critical when the bank and the company have a close business relationship. Basedon the importance of using soft information, Godbillon-Camus and Godlewski (2007) noted that it isdesirable to provide loan officers with incentives in the form of appropriate salary valuation anddelegated authority. Cerqueiro (2009) discussed the issue with loan officers’ discretion vs. rules insetting interest rates. The study by Berger and Black (2011) indicated that smaller financial institutions
tend to emphasize relationship lending. Uchida, Udell, and Yamori (2012) empirically demonstrated thatthe role of loan officers is important to some extent in Japan.

1

The role of loan officers was also verified in the interview that we conducted with S City commercial bank in January
2016.
2This type of analysis method is based on Uchida, Udell, and Yamori (2012).

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Meanwhile, our review indicated that no empirical study has been conducted on the activities andcharacteristics of loan officers relative to relationship banking, even though there are case studies. Xu(2010) looked at the Tailong Commercial Bank in Taizhou City, Zhejiang Province and found that their
management model in valuing soft information has resulted in a successful bank business at Tailong
Bank, which mainly serves small and micro enterprises. In their business model, Tailong Bank has aunique screening system called “three qualities and three figures.” The term “three qualities” refers topersonality, collateral, and products; they indirectly check the intention to repay loans by asking
employees, business partners, and people who are indirectly involved with the company about thepersonality of the business operator and assess a company’s actual repayment capability by examining
the quality of a company’s products, sales situation, and outlook. The bank also considers it important toverify the collateral to cover the risk against the loan. Furthermore, they understand the actual
production activities of the company by analyzing “three figures,” which refer to the amount ofelectricity usage, the amount of water usage, and customs declarations, to reduce information
asymmetry.
That said, it became clear from the interview that we conducted in March 2011 with the assistantgeneral manager of the financing division of a state-owned commercial bank branch in S City that
Tailong Bank is not the only bank that focuses on soft information relative to actual financing behavior.
Tang (2012) showed that banks do not necessarily trust information, such as a company’s credit rating,tax reports, and financial statements. This is because it is known that there are frequent cases ofunderreporting in the State Administration of Taxation filings by SMEs. Therefore, it is essential forbanks to obtain information on companies through their own investigation. When doing so, the due
diligence report prepared by the loan officer (project manager) becomes an important reference material
to determine whether to finance. This report mainly includes the following items: 1) personal dispositionof the business operator (personality, creditworthiness, etc.), 2) management tenure and position in theindustry, 3) personal assets, 4) foreign investments, 5) future prospects of the industry, 6) information
obtained from business partners and competitors, 7) verification of the inventory, and 8) verification of
the electricity and water bills. In other words, these are what they consider important. We can see thatloan officers generate and accumulate various types of soft information on the business operator and thecompany in the form of a report at the time of loan screening.

3. Hypotheses and Descriptive Analysis of the Data
Thus, in order to test whether such a role of loan officers actually leads to the theoretical outcome, asurvey on the textile industry clusters in China was conducted between February and April of 2012

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(hereinafter referred to as “2012 Survey”). It is an independent face-to-face survey with a total of 157
companies in the cotton-spinning-related industry in regions located in the Zhejiang and Jiangsu
provinces. Notably, the 2012 Survey asked a wide range of questions relating to the activities of the loan
officer, soft information (attributes on the company and the business operator), business relationshipwith financial institutions, company’s business challenges, initiatives for innovation, and desired
government policies, in addition to “hard information” (e.g., financial data) of the company. The analysis
in this paper will focus particularly on the role of loan officers in relationship lending based on suchindividual company data. Notably, the survey was also conducted in 2010 and 2011, although somequestions are different; therefore, the quality of the survey can be considered consistent.
Given that soft information is considered important in relationship lending as described above, wewould like to propose the following two hypotheses regarding the role of loan officers. The first
hypothesis (Hypothesis 1) is that loan officers play an important role in generating soft information in
relationship lending. For this hypothesis, we will use our company survey data and aim to explain whatkind of activities loan officers actually undertake during the process of generating soft information, as
well as to verify their role. It is represented by Equation (1) as follows:
Amount of soft information = f (Characteristic and activities of the loan officer, strength of therelationship, control variable)(1)

In other words, the dependent variable in Hypothesis 1 is soft information (SOFT). Independent
variables include loan officer’s characteristics and activities to produce soft information, various
variables to represent the strength of the relationship, the variables to remove the hard information on thecompany, and various control variables to represent the characteristics of the company.
The next hypothesis (Hypothesis 2) is that if soft information is being accumulated, it must provide
some kind of benefit to a company through the role of loan officers in obtaining financing. This isexpressed in Equation (2) as follows:
Benefit of the relationship = g (amount of soft information, control variables)(2)

In other words, the dependent variable of Hypothesis 2 is the benefit of the relationship. As
independent variables, the equation uses soft information (SOFT), which is the dependent variable in
Hypothesis 1, as well as a variety of variables to represent the strength of the relationship and various
control variables that represent the characteristics of the company.
Under these two hypotheses, the important thing is how to measure soft information (SOFT).
Following Scott (2004) and Uchida, Udell, and Yamori (2012), we prepared a set of six questions to havecompanies rated as to how well financial institutions know about them. They are: 1) whether they know
your company, 2) whether they know your company’s management team and owner, 3) whether they5

6know the industry your company belongs to, 4) whether they know the market your company is involvedin, 5) whether they know the local community your company belongs to, and 6) the frequency of daily
contact between your company and the loan officer. We decided to use the responses to each question
based on a five-point scale, where 1 means “doesn’t know at all” and 5 means “knows very well,” toperform the principal component analysis and create variables for soft information.3
Table 1 shows the percentages as to how each company rated the six items based on a five-point scale.
According to the table, the most chosen rating category was 3 (knows) for all items, except for Item 5; ingeneral, many companies rated financial institutions 3 or 4. Furthermore, among the six items, Item 1
had the highest mean score of 3.43, whereas Item 4 had the lowest mean score of 3.04. The firstprincipal component obtained from the principal component analysis using the rating scores for the six
items as described above is the dependent variable of soft information (SOFT).4

Table 1: Items for Companies to Rate Financial Institutions

Rating Items

1.
Doesn’tknow atall2.
Doesn’t
know
well

3.
Knows

4.
Knows
well

5. Knows
very well

Total(Mean)

1) Knows your company

2.3

13.2

36.4

35.7

12.4

100%
(3.43)

1.6

18.6

41.1

26.4

12.4

100%
(3.29)

1.6

22.5

41.1

14.0

20.9

1.6

26.4

41.9

27.1

3.1

3.1

17.1

33.3

39.5

7.0

100%
(3.30)

0.8

14.7

48.8

28.7

7.0

100%
(3.26)

2) Knows your company’sowner and managementteam3) Knows your company’s
industry4) Knows the markets
related to your company
5) Knows the local
community your company
belongs to6) Frequency of daily
contacts between yourcompany and the loan
officer
Note: Sample size =129100%
(3.30)
100%(3.04)

3

Such a verification method is based on Uchida, Udell, and Yamori (2012).

4

In addition, we have created dummy variables, which were assigned the value of 1 when the response was “Quite

excellent,” and we performed the principal component analysis using those six dummy variables to create the dependent
variable SOFT2 by using the first principal component obtained as a result. However, given that SOFT produced more
results with better quality, we have determined to proceed with the analysis by using SOFT as the dependent variable.

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Table 2: Business Activities and Relationship with the Loan Officer

Replacement of theloan officer

Age of the loan
officer

Meeting place

Contact method

Temporal distance tomeet

Choices

Sample size(companies)

Percentage
(%)

None

104

75.4

Once

24

17.4

Twice or more

10

7.3

Total

138

100.0

In his/her 20s

18

13.1

In his/her 30s

88

64.2

In his/her 40s or older

31

22.6

Total

137

100.0

At the company

44

32.4

At the bank

90

66.2

Other

2

1.5

Total

136

100.0

Meet in person

54

39.4

Telephone

79

57.7

E-mail, etc.

4

2.9

Total

137

100.0

5 minutes or less

43

34.1

6 to 10 minutes

48

38.1

More than 10 minutes

33

26.2

Total

126

100.0

Next, the variables given below are used as independent variables. First, the following variables
related to the characteristics and activities of the loan officer could come to mind as variables that affectthe production and accumulation of soft information by the loan officer. In other words, the loan
officer-replacement dummy variable can be used to indicate that the loan officer was never replaced in
the past two years (replacement = 1 and no replacement = 0; however, the coding was reversed at the78time of analysis).5 Theoretically, the variable is expected to have a positive sign in relation to SOFT,
given that the amount of information generated would increase when no loan officer replacement occurs.
Another one is the age group dummy variable (in his/her 30s = 1), which represents the experience ofthe loan officer. The variable is expected to be positive, given that those in a younger age group are
generally less knowledgeable and experienced with various aspects of their clients, whereas those in
their 40s or older have a richer collection of soft information on their clients. However, Uchida, Udell,and Yamori (2012) pointed out that the sign is not always clear because younger loan officers might visit
their clients more frequently and strive to collect soft information.
The questionnaire also asks about the frequency of contact with the bank in terms of how many times
they meet each other per month or per year. With the dummy variable for the place for meeting the loan
officer (at the company = 1, at the bank = 0), having the loan officer visit the company in person rather
than meeting at the bank is probably a plus for generating soft information. As for the most frequently
used contact method (in person = 1, telephone, fax, QQ, etc. = 0), in-person communication should havea better effect on generating soft information than telecommunication.
The characteristics and activities of the loan officer, as described above, are shown in Table 1. Basedon the table, loan officers were not replaced in approximately 75.4% of all cases, whereas the cases of
one-time replacement accounted for 17.4%, and the cases with twice or more replacements accounted
only for 7.3%. In the case of China, a system to rotate personnel every two to three years, similar to theone found in Japan, is rarely seen, and there is usually a system in which loan officers are held
accountable until their financing projects are completely paid off.6 As for the age group of loan officers,
those in their 20s and 30s account for 13.1% and 64.2%, respectively, for a total of 77.3%. In addition,
66.2% meet at the bank, 57.7% use the telephone, and 39.4% meet in person in terms of contact method.
In addition, we can see that 72.3% are located within a 10-minute drive in terms of the temporal distance
between the company and bank. This is probably attributable to the fact that many of the companies westudied are located in the suburbs or rural areas.

Table 3: Descriptive Statistics
Sample Size

Mean(companies)
5

SD

Min

Max

The interval based on which the loan officer is rotated probably differs by bank; however, given that the interviews weconducted indicated that loan officers at commercial banks are sometimes transferred and replaced every two to three
years, we use the period of two years in this paper.
6
In many cases, compensation, such as bonuses for the loan officer, is dependent on the repayment status of the company(according to an interview with a bank branch manager conducted by the authors in May 2015).

89(1) Company and Operator Attributes
Age of the company(years)Capital (10,000 yuan)Management tenure of theCEO (years)
Number of employees(individuals)
Asset size (10,000 yuan)

144

9.514

5.238

2

26

141

549.175

1407.424

15

10000

144

7.951

4.891

1

24

144

168.819139.055

15

820

139

2441.424

4679.646

100

31061

(2) Main Variables
Frequency of visit (per135

15.58533.174

0

240

126

10.901

8.886

1

80

138

0.754

0.432

0

1

Age of the loan officer

137

0.774

0.420

0

1

Meeting place

136

0.324

0.470

0

1

Contact method

137

0.394

0.490

0

1

116

1.690

0.807

1

5

136

4.390

3.089

1

23

133

3.195

0.957

1

6

year)
Distance (minutes)
Replacement of the loan
officer (times)

Number of banks financing
the company
Longest relationship with abank
Diversification of businessIn addition, the frequency of financial statement submissions to the bank is categorized as (1) at least
once a month, (2) once every three months, (3) once every six months, and (4) once a year. Here, thedummy variable is coded so that “(1) at least once a month” has the value of 1. This can be regarded as
an important control variable to remove hard information because we can say that a higher frequency inwhich the lender company submits financial statements to the bank implies that the bank is making sucha demand to the lender company and monitoring the company’s financial situation carefully. By doing
this, the frequency of contact with the loan officer increases, thus helping produce soft information. As
regards variables to represent the scale of the company, we asked for information about the size of9

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capital, size of total assets, sales, number of employees, age of company, and sales performance in thepast two years, which is categorized into (1) consecutive surplus, (2) surplus to deficit, (3) deficit tosurplus, and (4) consecutive deficit. Notably, the dummy variable assigns the value of 1 to “(1)consecutive surplus.”
Furthermore, as regards the characteristics of the business operator, we asked for information about
the operator’s tenure as a manager, educational background, and whether he/she is a founder. It is
probably easier for the loan officer to collect soft information when the company has been in thebusiness longer. A company’s creditworthiness should also be higher if the business operator is a founder
who is better educated. However, these variables do not have a considerable effect on the quantitative
analysis because more than 90% of our surveyed companies’ business operators are founders, and morethan 70% of them have an educational background of high school or less.
Finally, as a control variable, the temporal distance dummy variable (DISTANCE, within 10 minutes= 1, 10 minutes or more = 0) is used as an independent variable that represents the strength of therelationship between the company and the bank.7 According to the relationship theory, the frequency ofcontact between an SME and their bank increases as the distance between them becomes shorter to makeit easier for them to build a close relationship. For example, Alessandrini, Presbitero, and Zazzaro (2008)
analyzed and found that the amount of funding provided to the company increases as the distance
between the company and bank becomes shorter. As regards the number of banks from which a company
received loans, although we can expect the relationship to be stronger when there are fewer banks, themean among SMEs remains at 1.69 banks, as shown in Table 2. The means in Japan and the UnitedStates have been reported as 4.1 to 4.2 (Ono and Uesugi, 2009) and 1.2 (Brick and Palia, 2007),
respectively. In addition, the longer the company does business with their main bank, the stronger theirrelationship should become. Such analyses on the strength of relationship have shown the significance ofthe length of relationship in empirical studies, such as that by Tang (2012).
The diversity of business services also represents the strength of relationship lending because
financial transactions include not only lending to companies but also such items as settlement, wealthmanagement product investment, and foreign currency procurement. Looking at Table 2, our study
shows that the mean of a bank’s business services to the company is 3.1. However, as shown by Tang
(2012), it seems banks often impose that companies take a portion of the loan financed by the bank andredeposit or reinvest in wealth management products when obtaining a loan from a bank. In fact, 38
companies, or 28.6% of all companies we surveyed, are receiving wealth management service.

7

We have also asked about spatial distance.

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According to Ono and Uesugi (2009), the mean number of business services (excluding lendingtransactions) that SMEs receive from their main bank is 4.2. We can say that SMEs in Japan actuallyconduct more diversified financial transactions with their banks.

4. Estimation Results

The estimation result for Hypothesis 1 is shown in Table 4. This is the result of analyzing a loan
officer’s role in accumulating soft information. Based on this, the result is positive and significant when
the loan officer was not replaced. It implies that the amount of soft information produced increases when
the same loan officer remains in charge of the lender company on a long-term basis. The reference age

Table 4: Information Production and Activities of the Loan Officer
CoefficientStandard
error

p-valueConstant term

-6.348***

1.890

0.001

Tenure as CEO

0.046

0.028

0.105

Asset size (log)2.919***

0.941

0.003

Frequency of visit-0.005

0.004

0.235

Replacement of the loan officer (no = 0, once or more = 0)0.758**

0.332

0.025

In his/her 20s

0.534

0.413

0.202

In his/her 40s or older

0.232

0.327

0.944

Meeting place (at the company = 1; at the bank = 0)

0.046

0.300

0.988

Contact method (in person = 1; telephone, fax, QQ, etc. =0)-0.095

0.270

0.726

Frequency of financial statement submission (once a monthor more = 1; once every three months or less = 0)1.274***

0.301

0.000

Number of banks with loans-0.563***

0.181

0.003

(Dependent Variable = SOFT)

Age group of the loan officer (in his/her 30s =1)

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Regional bank (regional bank = 1; state-owned bank;commercial bank = 0)
Temporal distance (within 5 minutes = 1; more than 5
minutes = 0)
Sample size

0.369

0.263

0.163

0.605**

0.269

0.027

101

Adjusted coefficient of determination
0.315
Note: ***, **, and * indicate that the coefficient is significant at the 1%, 5%, and 10% levels,
respectively.
group of the loan officer is “in his/her 30s,” and the dummy variables were not significant for “in his/her
20s” or “in his/her 40s or older,” indicating that the collection of soft information is not associated withthe level of a loan officer’s skill. In addition, it became clear that factors such as meeting place andcontact method were unrelated.
We consider the results for other main independent variables. The dummy variable for submitting
financial statements at least once a month was positive and significant. This suggests that frequent
submissions of financial statements by the company led the bank to understand the company’s financial
situation well and/or to increase the frequency of contact with the loan officer through visitation. In
addition, the dummy variable of temporal distance was positive and significant, thus implying that therelationship becomes closer as the distance between the bank and the company becomes shorter.
Furthermore, the number of banks the company does business with was negative and significant, thus
suggesting that the relationship becomes weak because information on the borrowing company might
not be sufficiently generated.
To sum up the above analysis results for Hypothesis 1, a certain level of the role played by loan
officers in generating and accumulating soft information was confirmed. The next analysis task is to test
Hypothesis 2 as to what is the significance of soft information. That is, if soft information is being
accumulated, it is necessary to demonstrate that it has a positive impact on obtaining funding. Here, thedependent variable to represent the benefit of relationship is the question item indicating the level ofdifficulty that the company encounters while being evaluated for a loan. In particular, this question
concerns the current screening process when they want a loan, and we provided answer options,
including (1) very strict, (2) slightly strict, (3) relatively easy, and (4) don’t know (however, given that
there were only several companies that said “don’t know,” (4) was excluded). Notably, among the valid
sample of 134 companies, 35.8% (48 companies) chose (1), whereas 52.2% (70 companies) and 11.9%(16 companies) chose (2) and (3), respectively. If soft information had a positive impact on relationshiplending, companies should choose (3), or at least (2), in terms of the level of difficulty they encounter at
the time of loan screening.

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Given that the choices for this question are related to preference, we are going to measure thedeterminants of the benefit in procuring funding in Hypothesis 2 by using an ordinal choice model. In
doing so, we will also add ordinal probit analysis, which has almost the same process. This aims to test
the benefit of relationship lending by incorporating the amount of soft information used in Hypothesis 1
as an independent variable.8
The following estimation model is conceivable:( : latent variable,: independent variable,: coefficient vector, and : error term)

Furthermore, observed independent variable

and latent variable

relationship based on the mechanism of thresholdwould have the following

:

・
1.

・
・

The threshold and the coefficient vector that satisfy the above relationship are determined by leastsquares estimation. There are two thresholds,

and, to be determined to satisfy= 3 in thispaper’s case. If there are three answer choices for the independent variable at that time, the probability ofchoosing each option is also expressed specifically as follows:
１

Σ

))

１

-１

-１
As for the estimation for the probit model, it can be described as follows: If the choice of theeconomic agent
variable, andis expressed as,would be observable or would be possible latent

would be the error term. Given that, in the case of above equation, is unobservable,

is defined to meet the following conditions:＝＝
Assuming that

follows a standard normal distribution, the parameter vector

8

is estimated.

We also used a dummy variable that coded only “(1) very strict” as 1 to indicate the level of difficulty encountered by acompany at the time of obtaining a loan and then performed probit estimation (marginal effects); however, the results
were almost the same.

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Table 5: Determinants for the Level of Difficulty in Loan Screening
Ordinal Logit (1)Ordinal Probit (2)Coefficient

z-value

Coefficient

z-value

Soft information

0.433***

2.82

0.252***

2.92

Tenure as CEO-0.057-1.62-0.028-1.51

Sales (log)-8.371**-2.27-4.781**-2.32

Regional bank dummy-0.002-0.05-0.035-0.15

Years doing business with themain bank

0.235***

2.72

0.129***

2.74

Temporal distance

0.474

1.09

0.246

0.99

0.252

0.56

0.080

0.31

-0.001-0.21-0.004-0.21

Frequency of financial statement
submission
Two-year consecutive surplus
dummy
cut1

-5.8492.489-3.362

1.446

cut2

-2.8442.434-1.6191.425

Sample size

116

116

LR chi2(8)

28.31

27.97

Log likelihood-97.110-97.282

Pseudo R2
0.127
0.126
Note) ***, **, and * indicate that the coefficient is significant at the 1%, 5%, and 10% levels,
respectively.

The results of the ordinal logit and ordinal probit estimations are shown in Table 5. First, looking at
Equation (1) in terms of the significance and sign of the parameters, ones such as log of sales and years
doing business with the main bank are significant and generally agree with the predictions made prior to
the estimation. In particular, the coefficient of soft information as an independent variable was positiveand significant as we expected, thus suggesting that the more soft information is accumulated, the more
benefits the company enjoys when being screened for a loan. The number of years doing business withthe main bank is also positive and significant, as the theory predicted. This suggests that the relationshipbecomes closer, which results in a positive impact on financing as the number of years doing businesswith the bank becomes higher. However, the temporal distance dummy variable, which was significant
in Table 5, is not significant despite being positive. We can see that the significant variables and their

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signs, as shown in the results of the probit model that used the same variables as Equation (1), generally
agree between the equations.9

5. Conclusion
This paper demonstrates empirically whether loan officers at a bank play a role in generating andaccumulating soft information when they perform financial transactions with SMEs. We used our own
survey data on companies, set two analytical goals, proposed two corresponding hypotheses, and thentested them. The empirical results indicated that when a dedicated loan officer handles the sameborrower on a long-term basis, he/she contributes to the generation of soft information to some extent. It
also became clear that the soft information obtained by the loan officer provided benefits to theborrowing company because such information made funds easier to obtain and reduced funding
restrictions.
Furthermore, the finding that the strength of the relationship had a positive effect on accumulating
soft information and the actual lending was consistent with earlier studies by Hé (2010) and Tang (2012).
Thus, our survey results demonstrated that the role of loan officers is important in relationship lending in
China, particularly when a loan officer continuously handles the same company on a long-term basis(defined as no replacement for two years). Conversely, the results suggested that the potential for acompany to obtain funding becomes worse when the loan officer is replaced. This is also consistent withthe results shown by Scott (2006).
Based on the analysis of this paper, the importance of relationship banking in China and the role ofloan officers should be appreciated more than they currently are. That being said, this paper has notconsidered the following two questions. First, although it is possible that the loan decision system, towhich loan officers belong, varies from bank to bank, the significance of the dummy variable that
distinguished state-owned banks and regional commercial banks from local financial institutions (citycommercial banks, village and township banks, trust and investment corporations, etc.) was not clearlytested in this paper. Secondly, unlike Deng and Hé (2014) who have dealt with the personality andgender dimension of loan officers in their studies, this paper has left out the gender and personality inloan decision making process. Going forward, it is essential to increase the sample size of banking
organizations and conduct analyses based on more detailed classifications. In addition, if soft9

We also performed probit estimation (marginal effects) by using the dummy variable that assigned the value of 1 to theanswer “(1) very strict” relative the question on the level of difficulty in obtaining a loan, which is the dependent variable
in Hypothesis 2. The results also indicated that the coefficients representing the accumulation of soft information and thenumber of years doing business with the main bank also had negative effects at the 1% significance level.

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information is generated by loan officers, and it has a positive lending effect on the borrowing company,as the analysis results in this paper suggested, it is probably necessary to clarify what banks’ actual
lending technology is based on.

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