Research on the Impact of Relationship Networks on Farmers’ Formal Credit Constraints: The Case of China

International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume-11, Issue-4 (August 2021)
www.ijemr.net https://doi.org/10.31033/ijemr.11.4.13
101 This Work is under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Research on the Impact of Relationship Networks on Farmers’ Formal
Credit Constraints: The Case of China
Lun-song Chen1
and Bi-Lin Sun2
1
Postgraduate student, College of Economics and Management, Zhejiang Agricultural and Forestry University, Hangzhou
311300, CHINA
2
Postgraduate student, College of Economics and Management, Zhejiang Agricultural and Forestry University, Hangzhou
311300, CHINA
2
Corresponding Author: 394442317@qq.com
ABSTRACT
Based on the survey data of Lishui City, Zhejiang
Province, this paper uses the Heckman two-stage model to
construct a credit constraint function without selection bias,
and explores the relationship between the scale and quality of
the relationship network and the credit constraints of rural
households. Research shows that the scale of the relationship
network is affected adversely by urbanization and
networking, having a weaker impact on the formal credit
constraints of rural households. The quality of the
relationship networks can improve farmers’ awareness of
formal credit, reduce transaction exposure, regulate farmers’
behavior and act as a “guarantee”, thereby effectively
alleviating farmers’ formal credit constraints. At the same
time, the relationship network of farmers is gradually
becoming more structured, where farmers' social interests
are becoming more purposeful. Additionally, formal financial
institutions have set a threshold for farmers’ credit, which
requires a certain amount of securities for money.
Keywords-- Relationship Network Quality, Relationship
Network Scale, Heckman Model, Formal Credit
Constraint, Information Cognition
I. INTRODUCTION
For a long time, China's rural credit market has
been composed of a dual structure of formal credit markets
and informal credit markets. However, as the traditional
network structure of geographical, kinship and kinship
farmer households has been broken by the process of
urbanization, formal credit has gradually become the main
method of financing for farmer households(Li et al., 2011;
Sun et al., 2013). Scholars universally agree that the
relationship networks have positive effects on the formal
credit behavior of farmers, and the relationship network
includes two aspects: quality and scale(Lin et al., 1981;
Zhao, 2012). In the formal farmer credit market, it is hard
to collect complete and effective formal credit information
through the scale of the relationship network. Besides,
with the popularization of big data networks, the influence
of information transmission through the scale of the
relationship network as a medium has gradually weakened
the influence of the formal credit behavior of farmers (Sun
et al., 2013). The quality of the relationship network not
only plays a role in information transmission of the
relationship network, but also works as a "collateral" or a
"guarantor" when farmers make formal credit, which is of
positive significance for farmers to obtain formal
credit(Wang et al., 2014; Jiang, 2016).
This paper follows the practice of Guo and
Chen(2002), uses the entropy method to calculate the
weights of the various indicators of the relationship
network quality, and then uses the survey data of Lishui
City to conduct empirical tests, and finally constructs the
farmers' credit constraint function, exploring the effects of
relationship networks’ quality on the farmers' credit
constraints. Although scholars have studied the credit
constraints of farmers in recent years, the popularization of
the Internet and the transformation of urbanization have
impacted the structure of the original relationship network
of farmers owing to that the social environment has
changed. Moreover, scholars in the past have rarely
explored the influence of the quality of the relationship
network on the formal credit constraints of farmers. This
article not only effectively makes up for this shortcoming,
but also uses Heckman's two-stage estimation method to
eliminate subjective selectivity bias, and provides a useful
reference for formal financial institutions to further
promote rural financial services and release the formal
credit constraints of rural households, having largely
theoretical and practical significance.
II. THEORETICAL ANALYSIS AND
HYPOTHESIS
The farmer relationship network is an
organizational network established by farmer households
in social activities. It plays an important role in
information communication, mortgage guarantee,
supervision and restraint, and regulation of farmer

households' behavior in farmer's credit behavior. The scale
of the organization network formed by farmers in social
activities encompasses a variety of information. When
farmers have a demand for credit, they can obtain relevant
information through the members of the network of
relationships. However, the scale of this kind of network
has "weaknesses", and the members of the relationship
network are easy to lose track of each other due to changes
in the external environment, that is, the "man in the iron
mask" that farmers often lament on. Therefore, it is
difficult for farmers to raise their formal credit awareness
from the scale of their relationship network((Li et al.,
2011). The farmers’ relationship network includes scale
and quality. Although the quality of the relationship
network of farmers depends on the size of the relationship
network, the latter plays a "characteristic" role in the
formal credit behavior of farmers(Xun et al., 2018; Zhang,
2008).Firstly, farmers will consciously maintain the core
members of the relationship network in social actions, and
the members in the core position of the relationship
network can bring more high-quality information to
farmers, thereby raising the farmers' awareness of formal
credit and alleviating the demand-based credit constraints
of farmers. Secondly, farmers generate formal credit
demand. The higher the quality of the relationship
network, the more members the farmers can find to
provide mortgage guarantees for farmers, thereby
alleviating the supply-oriented credit constraints of
farmers. When a farmer breaches a contract, the farmer
may be squeezed out by the members of the relationship
network, and the core relationship network members play a
more obvious role in this interaction process(Yan,2014).
Moreover, the more frequently the farmers transfer
information and the closer the relationship network
structure, the less likely it is for farmers to breach
contracts. Finally, a high-quality relationship network has
a certain regulatory effect on farmers' behavior. When
farmers generate formal credit demand, formal financial
institutions need to assess the behavioral qualifications of
farmers. The higher the quality of the relationship network,
the stronger the normative effect on farmers' behavior., in
order to cater to the relationship network, meeting their
own social needs, farmers may reduce the corresponding
adverse selection, thereby meeting the qualification review
requirements of formal financial institutions.
To sum up, the quality of the relationship network
can effectively alleviate the formal credit constraints of
farmers, and can transforin formation, supervise, restrict
and regulate behavior of farmers' social activities. The
scale of the relationship network is susceptible to changes
in the external environment, and has a weaker impact on
the formal credit constraints of farmers. See Figure 1 for
details.
Figure 1: Diagram of the relationship network affecting the formal credit restriction mechanism of rural households
Based on the above analysis, this article proposes:
Hypothesis 1: The scale of the relationship network has a
weak influence on the formal credit constraints of farmers.
Hypothesis 2: The quality of the relationship network can
alleviate the formal credit constraints of farmers.
III. DATA AND MODEL SETTING
3.1 Data Source and Processing
3.1.1 Data Source and Description
This article uses research data to explore the
impact of relationship network quality on farmer credit

constraints. The project team members of the School of
Economics and Management of Zhejiang Agriculture and
Forestry University conducted field investigations in
Lishui City, Zhejiang Province from March 2017 to
December 2019, and eliminated farmers without credit
needs. Later, 208 questionnaires were finally obtained. The
questionnaire follows the Direct Inquiry Method (DEM)
adopted by Boucher et al. (2005) to classify the types of
credit constraints of farmers in order to more accurately
reflect the types of credit constraints faced by farmers in
the formal credit market. The contents of the questionnaire
include: whether you have credit needs, whether you have
applied for loans from banks, credit unions and other
formal financial institutions, the reasons for not applying
for loans, whether you have obtained a full loan, and the
amount of loans you have obtained; The trust level of the
surrounding people, whether there are people working in
the bank or the government among relatives and friends;
age, education level, physical health, woodland area,
residential area and other characteristics.
When farmers have credit demands, who have not
applied for loans from formal financial institutions, it is
called a demand-based credit constraint. Among them, the
abandonment of formal credit due to the long distance and
complicated procedures is called cost-based credit
constraint, and the abandonment of formal credit due to
fear of loss of collateral rights and operating losses is
called risk-based credit constraint. The product conditions
set too high to give up formal credit is called price-based
credit constraints. When farmers have credit demand and
apply for loans from formal financial institutions, they
have not obtained loans in full, which is a supply-based
credit constraint. Among them, the complete rejection of
farmers' loan demand is called full quantitative credit
constraint, and the partial amount of loans obtained by
farmers' loans is called partial quantitative credit
constraint. When a farmer obtains a full loan, it is called no
credit constraints.
From the analysis of statistical results, demand-
based credit constraints are the main factor restricting the
credit behavior of rural households. 74.52% of rural
households have not participated in formal credit and
voluntarily give up loans. Demand-based credit constraints
are the main factor restricting the development of rural
credit.
Table 1: Types of rural household credit constraints
Types of Credit Constraints Reason Frequency percentage
Supply-based credit
constraints
Full quantity constraint 7 3.37%
Partial quantity constraint 25 12.02%
Demand-based credit
constraints
Cost credit constraint 59 28.37%
Risk credit constraints 43 20.67%
Price credit constraint 53 25.48%
No credit constraints Get a loan in full 28 10.09%
total —— 208 100.00%
Note: According to data sorting and calculation.
3.1.2 Data Description and Estimation
Farmers are subject to different levels of full
quantitative credit constraints, partial quantitative credit
constraints, and demand-based credit constraints.
Therefore, this article draws on Yu et al.(2014)to construct
a credit constraint function.
（1）
Among them, represents the credit constraint
degree of the i-th farmer household, represents the
demand limit of the i-th farmer household, and
represents the loan limit obtained by the i-th farmer
household.
Based on Yang et al.(2019), this paper selects
whether the farmers have relatives and friends working in
formal financial institutions or the government and their
trust in relatives and friends as the main indicator to
measure the influence of the quality of the relationship
network of farmers on the credit constraints, the entropy
method being used to calculate the weight of the indicator.
Construct a basic matrix , where
represents the j-th index value of the i-th farmer,
， . The extreme value standardization is
used to avoid the dimensional difference between the
indicators, and the resulting standardization matrix is :
（2）

Perform column-wise normalization on the
standardized matrix , and the specific gravity matrix
can be obtained. The corresponding relationship
between the elements in the matrix and the above-
mentioned matrix elements is as follows:
（3）
The calculation formula for the information
entropy and information utility value of the j-th
indicator is as follows, where the constant :
（4）
（5）
Based on the information utility value, we
calculate the indicator weight , the formula is as follows:
（6）
By substituting standardized data , the
indicator weight turns into the following formula, the
capital endowment is calculated:
（7）
3.2 Index Selection and Research Methods
3.2.1 Index Selection
Farmer households obtain loans from formal
financial institutions depending on their own
characteristics and family economic foundation. This
article selects the farmer’s age(age); education status(edu);
physical fitness(health). Good health=5, good health=4,
average health=3, poor health Good=2, poor health=1;
whether you have loan experience(loan), with loan
experience=1, no loan experience=0; family house
value(house), unit is 10,000 yuan; family woodland
area(area forst), unit is hectare; family income(comeback),
unit is 10,000 Yuan; family population(family); and the
proportion of the family’s effective labor force(elabor).
The family’s effective labor force ratio is the labor force
involved in the family’s labor/the total family population.
3.2.2 Research methods
Since the demand limit of farmers under demand-
based credit cannot be observed, this paper uses the
Heckman two-stage model to estimate the real credit
demand of farmers, calculating the formal credit constraint
degree R_i of farmers on this basis.
The first step is to use the Probit model and take
"whether famers are subject to credit constraints" as the
dependent variable to estimate the factors that affect
farmers' credit constraints.
（8）
Construct the inverse mills ratio based on the
results of the Probit model ，
，The numerator is
the density function of the standard normal distribution,
and the denominator is the cumulative distribution
function.
The second step is to bring the inverse mills ratio
into the credit demand line equation(Chen,2014;Dai et
al.2020), and use OLS to estimate the amount of credit on
the basis of farmers without credit constraints. After the
estimated coefficient is obtained, the amount of credit
demand of all farmers is estimated, and the degree of credit
constraint is calculated on this basis.
（9）
IV. EMPIRICAL ANALYSIS
4.1 Heckman Two-Stage Regression Results
This paper uses Heckman's two-stage regression
analysis to construct the inverse mills ratio, eliminating the
selection bias of farmers, and constructing a constraint
equation close to the real farmers' credit demands.
According to the regression results, the inverse mills
coefficient attaches importance to a significance level of
1%, and there is a selective bias between the farmers
subject to credit constraints and those not subject to credit
constraints. Therefore, it is suitable for this paper using
Heckman two-stage to find the real credit demand of
farmers.
Both the age and education level of the farmer
households will force the farmer households to be subject
to different degrees of formal credit constraints, owing to
that the farmer households are generally older and their
willingness to credit and ability to repay loans are lowered.
Whether the farmers have credit experience, the value of
houses and the area of forest land can significantly
alleviate the credit constraints of farmers. When farm
households have credit experience, they are less likely to
reject formal credit when they generate formal credit
demand. At the same time, due to the past creditworthiness
of rural households, formal financial institutions will have
relatively lower loan conditions. The value of farmer
households’ houses and the area of forest land represents
their economic conditions. When farmers generate formal
credit demand, the more items that can be mortgaged, and
the easier it is to obtain loans from formal financial
institutions.

Table 2: Two-stage regression results of Heckman
（1）（2）
age
0.031*
(1.81)
0.324***
(23.48)
edu
0.104**
(2.42)
0.437***
(15.98)
healthy
0.030
(0.19)
5.419***
(67.51)
loan
-0.917**
(-2.50)
-4.834***
(-18.60)
house
-0.005**
(-2.33)
-0.111***
(-39.57)
area forst
-0.013**
(-2.12)
-0.262***
(-38.44)
comeback
0.009
(1.20)
0.385***
(50.78)
people
-0.031
(-0.27)
-0.389***
(-4.72)
elabor
-1.077
(-1.52)
-36.280***
(-73.68)
34.590***
(51.15)
常数
0.635
(0.46)
-2.895***
(-2.91)
R2
0.245 0.964
Note: ***, ** and * respectively indicate that the regression coefficient is significant at the level of 1%, 5% and 10%.The
figures in brackets are standard errors.
After eliminating the subjective selection bias, the
average actual credit amount of farmers is 195,200,000
yuan, which is 58.82 million yuan higher than Dai et al.,
research. Forestry production investment is higher than
agricultural production. The research data in this article
comes from Lishui, Zhejiang Province. Collective forest
area in the south of the city. At the same time, Zhejiang's
economy is more advanced than the Northeast, and the
demands for living consumption of farmers are also higher
than the Northeast. Therefore, the estimated result is in line
with the actual needs of farmers.
Table 3: Credit constraint table for rural households
Actual amount of credit obtained (mean) 4.119
Real credit demand(mean) 19.532
Degree of credit constraint(mean) 78.911%
4.2 Regression Results of Relationship Network Quality
and Rural Household Credit Constraints
This paper uses the credit constraint equation to
find the real credit demand of farmers under non-selective
bias, so as to calculate the credit constraint degree of
farmers. Since the degree of credit constraints of farmers is
a continuous variable, this paper constructs an OLS model
for regression analysis. The quality (wi) and size (size) of
the rural household relationship network are the core
explanatory variables of this article, and personal
characteristics and family characteristics are used as
control variables. Considering that there is a certain
correlation between the quality of the relationship network
and the scale of the relationship network, the quality in the
relationship network is first regressed to obtain the model
(3) and the model (4), and then the model (5) is jointly
estimated.
Based on the analysis of model (3) to model (5),
when farmers apply for formal credit, the role of
information transmission played by the scale of the
relationship networks is gradually weakens, the effect of
alleviating the formal credit constraints of farmers is
limited. With the development of informatization,
urbanization and the popularization of big data, the

information barriers between formal financial institutions
and farmers are gradually broken, and the scale of the
relationship network can hardly play its role in information
transmission. Hypothesis 1 has been verified. However,
the restriction, regulation and supervision of the
relationship network quality on farmers can effectively
reduce the adverse selection of farmers. At the same time,
farmers pay attention to establishing good relationships
with core members of the relationship network, and they
can obtain a certain degree of mortgage guarantee when
they generate credit demand. The quality of the
relationship network can effectively alleviate the credit
constraints of farmers, hypothesis 2 has been verified.
Table 4: OLS regression results
(3) (4) (5)
size
0.001
(0.83)
0.001
(0.91)
wi
-0.126**
(-2.03)
-0.128**
(-2.06)
age
-0.001
(-0.14)
0.000
(0.11)
0.000
(0.09)
edu
0.032*
(1.92)
0.034**
(2.06)
0.033**
(1.98)
healthy
-0.051
(-1.10)
-0.050
(-1.07)
-0.050
(-1.07)
loan
-0.238***
(-4.83)
-0.227***
(-4.56)
-0.230***
(-4.61)
house
-0.002***
(-3.13)
-0.002***
(-2.71)
-0.002***
(-2.65)
area forst
0.001
(0.22)
0.001
(0.27)
0.001
(0.24)
comeback
0.001
(0.81)
0.001
(0.98)
0.001
(0.85)
people
-0.038
(-1.63)
-0.048**
(-2.11)
-0.048**
(-2.10)
elabor
-0.179
(-1.51)
-0.140
(-1.25)
-0.150
(-1.31)
常数
1.295***
(3.64)
1.303***
(3.67)
1.297***
(3.64)
R2
0.203 0.215 0.217
Note: ***, ** and * respectively indicate that the regression coefficient is significant at the level of 1%, 5% and 10%.The
4.3 Robustness Test
Since this paper employs the method of
constructing the inverse mills coefficient to calculate the
real farmer's credit demand, there are maximum and
minimum values that violate the objective conditions of
farmer households. Therefore, this paper adopts the
deletion of5% of the samples before and after to verify
whether the estimation can achieve consistent and robust
estimation results.
According to the analysis of the estimation results, the
scale of the relationship network has a weak influence on
the credit constraints of farmers, the coefficient changing
little, the fluctuation of labeling errors being small, and the
scale of the relationship network hasing a weaker impact
on the formal credit constraints of farmers. The quality of
the relationship network has a significant alleviating effect
on the credit constraints of farmers, and it is significant at
the 5% significance level, the coefficient changing little.
The robustness test results show that the above estimation
results are reasonable and have a certain degree of
robustness. See Table 5 for details.

Table 5: Robustness test results
coefficient Standard error T-value P-value
size 0.001 0.002 0.33 0.744
wi -0.143** 0.066 -2.17 0.032
Control variable control
Note: ***, ** and * respectively indicate that the regression coefficient is significant at the level of 1%, 5% and 10%. The
V. CONCLUSION
With the development of urbanization and
informatization, the dual credit market in traditional rural
areas is gradually dominated by the formal credit market.
The information transmission function of the relationship
networks gradually lose its effect under the influence of
urbanization and informatization. This paper uses the
research data of the research group to explore the influence
of the relationship networks on the credit constraints of
farmers. The Heckman two-stage model is employed to
construct the inverse mills coefficient to eliminate the
selection bias of farmers. The study found that: Firstly,
there is a certain "weakness" in the scale of the relationship
network, whose information transmission function is
gradually squeezed by network information, and it is
difficult to alleviate the formal credit constraints of
farmers. Secondly, the quality of the relationship network
can effectively regulate and supervise farmers’ credit
behavior, improving farmers’ awareness of formal credit,
avoiding farmers’ adverse selection, and simultaneously
acting as a “mortgage” and “guarantee” to alleviate
farmers’ concerns overformal credit constraints to a certain
extent. Thirdly, the involvement of the scale of the
relationship network caused a small change in the error of
the relationship network quality standard, indicating that
the structure of the relationship network of farmers has
gradually differentiated, and the social purpose of farmers
has increased. Fourthly, rural households with more
physical assets are less restricted by formal credit,
indicating that formal financial institutions need certain
qualification certification and guarantees for lending to
rural households, and a certain threshold is set for rural
households' formal credit.
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