Abstract：The banking sector thinks that micro borrowers do not have much experience in getting loans
from banks and cannot provide collateral, guarantee or financial statement in the process of applying for
loans. It is difficult for banks to decide whether they should lend to micro borrowers because they are
not sure whether they can repay on time. Still, scholars focus their studies on the breach of loan
contracts of micro borrowers. They think that it is difficult for banks to acquire, identify, and process the
soft information of micro borrowers because it is high in cost, low in income and big in risks. In fact,
difficulties in lending and borrowing from both sides co-exist at the same time. Academic studies should
not ignore the fact that different types of banks have their own advantages in dealing with different
types of information. Micro banks, rather than other types of commercial banks, might do well in
dealing with the soft information of micro borrowers. It might be a good way out to get out of the
dilemma of the above obstacles by encouraging small and micro banks to grant appropriate credit lines
to micro borrowers with real production and operation businesses.
Specifically, this paper constructs a theoretical model on micro borrower’s credit line, which
creatively introduces soft information and credit technology into the equation system and tries to find a
solution to an appropriate credit line by maximizing the utility of both suppliers and demanders of micro
credits. An empirical analysis is made based on the production and operation micro credit data manually
collected from the micro credit centers of those banks in cooperation with the Zhejiang University AFR micro credit project. Theoretical research shows that there are appropriate credit lines for any micro
borrowers who have real production and operation loan demands. Empirical research shows that the soft
information of micro borrowers and the credit technical level of customer managers play a significant
role in determining the appropriate credit lines. For micro borrowers with general operating conditions
or insufficient assets, their soft information has a greater effect on the determination of the appropriate
credit lines. For micro borrowers with general character or original ecology, the credit technical level of
the customer manager has a decisive role in determining the appropriate credit lines.
This paper first contributes to the literature in the following ways: First, the study finds that banks
should provide appropriate credit lines to micro borrowers with real business operations. For banks, the
issue of how much to lend is more important than that of whether to lend or not. Second, the
determinants of appropriate credit lines include the credit technical levels of the supply side, while
previous studies mostly start from the demand side. Third, the paper demonstrates that the credit
technology improvement of customer managers in small and micro banks is good for soft information
mining and helps increase the appropriate credit line to meet the credit demands of micro borrowers
without causing the risk of default. Four, unlike previous papers, it differentiates clearly the distinctive
differences of micro credit from small credit in terms of credit line, final purposes and the entity of
supply and demand.
The paper suggests that small and micro banks should improve in the following two aspects. On the
one hand, banks should innovate on the policy and procedures of the employee training system. They
can improve credit technology by establishing a craftsman-style internal training system for new and
young employees. On the other hand, banks should expand their soft information collection channels
regarding micro borrowers. With the help of neighborhood committees, village committees, relevant
government departments and banks can improve the availability and reliability of the soft information of
those potential clients by developing an agriculture-related database and integrating the unique
information about the permanent residents and villagers. The information to be counted on includes such
factors as the alternative ways of collecting money, neighborhood relationship, reputation and
popularity, willingness to pay utility bills, property management fees, and penalty for traffic violations.
朱燕建 何琛. 信贷技术、软信息与适宜贷款额度——基于1493 份微小贷款调查报告的实证研究[J]. 浙江大学学报(人文社会科学版), 0, (): 1-.
Zhu Yanjian He Chen. Credit Technology, Soft Information and Appropriate Credit Line:
An Empirical Study based on 1 493 Reports of Micro Credit Surveys. JOURNAL OF ZHEJIANG UNIVERSITY, 0, (): 1-.
