Research on Social Credit System and Venture Capital Investment

Efficiency

Yifan Gong and Yingkai Yin

School of Economics, Shanghai University, Shanghai, China

Keywords: Social Credit System, Credit Information Sharing, Venture Capital.

Abstract: The research of the social credit system can not only analyze the problem of information asymmetry

theoretically, but also provide more entrepreneurial characteristics information for entrepreneurial investors

in practice. This article selects Changsha as a sample city and uses data from 2010 to 2019 to regress and

analyze the relationship between the social credit system and the scale of venture capital. The empirical

analysis results show that if a city’s credit score increases, the proportion of venture investment that a city

receives in the country’s venture capital will increase; the proportion of a city’s credit ranking decline in the

amount of venture investment in the country’s venture capital will also decrease accordingly. Moreover, the

increase in the absolute level of the social credit system has a greater impact on the scale of venture capital

than the increase in the relative level.

1 INTRODUCTION

Mass entrepreneurship and innovation have become

an inevitable requirement for economic development

transformation and improving the quality of

economic development. The “Opinions of the State

Council on Promoting the High-quality Development

of Innovation and Entrepreneurship and Creating an

Upgraded Version of ‘Double Entrepreneurship’”

clearly stated that the role of venture capital in

supporting innovation and entrepreneurship should

be fully utilized. The information asymmetry faced

by venture capital institutions often faces obstacles to

expanding the scale of venture capital when

investing. Therefore, in-depth study of the

relationship between the improvement of the social

credit system and the expansion of the scale of

venture capital has a positive guiding significance for

giving full play to the role of credit as a new

production factor, optimizing the allocation of

resources, and improving the efficiency of investment

and financing.

In 2014, the State Council issued the “Plan for the

Construction of a Social Credit System (2014-2020)”

to vigorously promote the construction of social

credit system. Changsha is located in the central

region of China. In 2020, the amount of venture

capital investment in Changsha is 21.286 billion

yuan, ranking 10 in China, which is far from the

previous ones (Table 1). In terms of proportions,

Changsha only accounts for 2.2% of the total

investment amount in the country, indicating that the

scale of venture capital in Changsha is relatively

small. Judging from the number of investment cases,

there were 4,567 investment events in China in 2020,

of which only 35 were in Changsha,

accounting for

0.8%

of the country. At the same time, we found that

the number of investment events in Changsha reached

the

highest in 2015 and 2016, and continued to

Table 1: Top 10 City Venture Capital Private Equity

Amount in 2020.

Rank City

Amount

(100 million yuan)

1 Beijing 2075.51

2 Shanghai 1049.37

3 Jinan 1019.56

4 Shenzhen 501.05

5 Hefei 492.32

6 Guangzhou 391.82

7 Suzhou 345.62

8 Hangzhou 337.81

9 Nanjing 234.28

10 Changsha 212.86

Gong, Y. and Yin, Y.

Research on Social Credit System and Venture Capital Investment Efﬁciency.

DOI: 10.5220/0011161900003440

In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 99-104

ISBN: 978-989-758-593-7

Figure 1: Venture Capital Private Equity Amount and Investment Events of Changsha in 2010-2020

decline in the next few years, reflecting the low level

of overall investment development in Changsha (Fig.

1). This article uses empirical analysis and selects

relevant data from Changsha from 2010 to 2019 to

study the relationship between the construction of the

social credit system and the efficiency of Changsha’s

venture capital, aiming to promote the further

improvement of China’s social credit system.

2 LITERATURE REVIEW

2.1 Social Credit System and Function

The core of the credit system is credit reporting, the

essence of which is the sharing of credit information.

The credit information sharing mechanism alleviates

the problem of information asymmetry in the

transaction process between the borrower and the

lender by sharing the borrower’s information

between lenders.

In China, the traditional credit evaluation system

based on quantitative financial data and hard

information is not suitable for small and medium-

sized enterprises, causing them to face a large

financing gap. In order to solve this problem, the

Social Credit System (SCS) was formally proposed in

2014 (Síthigh, Siems, 2019). The system combines

the economic aspects of credit (involving financial

creditworthiness and trust in the market) with

measures to promote social harmony and effective

government. Its core function is to establish a system

to supervise the credit behavior of individuals and

enterprises, and the untrustworthy behavior will be

punished quickly and effectively (Chen, Grossklags,

2020). The current research shows that the use of big

data, blockchain and other technologies to

comprehensively evaluate the credit status of SMEs

can effectively reduce the information costs and

transaction costs between SMEs and lenders (Sun,

2021).

2.2 Factors That Affect the Decision-

Making of Venture Capital

The current literature on such issues mainly uses two

methods. The first method is the multiple regression

method. By using multiple regression model, scholar

empirically finds that financial literacy has a positive

relationship with investment decision-making to a

certain extent because of its ability to accurately

control information quality and rationally select

investment sectors (Alaaraj, Bakri, 2020).

The second method is to use statistical analysis.

With a survey of 749 venture capitalists, research

used experimental conjoint analysis to find that

corporate revenue growth is the most important

investment criterion, followed by the added value of

products/services, the performance and profitability

of the management team (Block, et al, 2019). 885

institutional venture capitalists were surveyed and the

results show that when choosing investments, venture

capitalists believe that the management team is more

important than business-related features such as

products or technology, and the ultimate success or

failure of investments depends on the team rather

than the business. Moreover, although deal sourcing,

deal selection, and post-investment value-added all

28,2

37,42

9,09

8,41

8,07

14,33

30,86

64,28

47,19

107,17

212,86

108

113

100

120

100

150

200

250

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Amount(100 million yuan) Events

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

100

contribute to value creation, VCs believe that deal

selection is the most important of the three

(Grompers, et al, 2020).

2.3 Social Credit System and

Investment and Financing

Efficiency

Scholars mostly use a combination of qualitative

analysis and quantitative analysis to study such

problems. An empirical research based on XBRL

(Internet-based computer language dedicated to the

preparation, disclosure and use of financial reports,

which can be entered once and used multiple times)

shows that the higher the efficiency of credit

information processing, the more efficient the

company’s investment efficiency (Cheng,

et al, 2021).

By taking top 500 listed companies in China as the

research object, the research constructs a DID model

to study the relationship between information sharing

and corporate investment efficiency. Research shows

that credit information sharing can effectively

improve the investment efficiency of enterprises (Ye,

et al, 2020).

3 MODEL SETTING

3.1 Research Hypothesis

If a social credit system, including the credit

investigation system, has established a shared credit

information collection, and with the continuous

improvement of this system, the availability of

corporate credit information and even more

characteristic information continues to increase, then

adverse selection The problem will be alleviated.

With a relatively complete social credit system, it is

easier for venture investors to screen start-ups and

make investment decisions. This means that the

improvement of the social credit system also means

that the default cost of start-ups will increase, and the

problem of moral hazard will also be alleviated.

Therefore, this article makes the following

assumptions:

H1: The higher the degree of Changsha’s social

credit system, the larger the scale of venture capital.

3.2 Sample Selection and Data Sources

Considering the representativeness of the sample

cities’ regional geographic location and venture

capital scale, as well as the availability and

completeness of data, this article chooses Changsha

as the analysis object. All the investment events of all

venture capital institutions in the sample cities from

2010 to 2019, the per capita GDP data of the sample

cities from 2010 to 2019, and the proportion of the

output value of the secondary and tertiary industries

in GDP are all taken from the wind database. The City

Business Credit Environment Index (CEI) is mainly

taken from the “CEI Blue Book”. For years with

missing data, the mean value of two consecutive

years is used as an interpolation substitute. In

particular, due to the lack of data in 2014, data of

2013 is the average of previous years, and the 2014’s

is the average of 2013 and 2015.

3.3 Variable Definition and Model

Setting

According to the research purpose and related

literature, this paper chooses the ratio of venture

capital investment to the national total venture capital

investment to measure the scale of entrepreneurship.

This article chooses city’s commercial credit

index as the measure of the level of social credit

system construction. The Urban Commercial Credit

Index is jointly compiled by the Integrity Research

Center of the Chinese Academy of Management

Science and other institutions. It is based on the

theory of social credit system, urban credit system,

and enterprise credit management theory. It provides

financial credit instruments, commercial credit sales,

and enterprise comprehensive evaluation of factors.

Finally get the social credit score of each city and

rank it. The social credit score ranges from 1 to 100.

The higher the score, the higher the construction level

of the city’s social credit system. Existing research

results show that CEI is a reliable indicator to

measure the degree of perfection of the city’s credit

system and the results of its operation. Considering

that the changes in the social credit system may not

have an immediate impact on the decision-making of

venture investors, this article chooses the first-order

lag and second-order lag of CEI as explanatory

variables.

The model established in this article is as follows:

112 23

ttt tt

a b CEI b CEI b Controls

−−

=+ + + +

(1)

Among them, is the explained variable, which

measures the scale of venture capital.

CEI

−

CEI

−

two explanatory variables, they measure the degree of

perfection of the city’s social credit system. This

paper selects per capita GDP (ten thousand yuan), the

proportion of secondary industry output

value in GDP,

and the proportion of tertiary industry output value in

Research on Social Credit System and Venture Capital Investment Efﬁciency

101

Table 2: Variable Definitions.

Variable types Variable names Variable symbols Variable definitions

Explained variable Scale of venture investment y

The permillage of venture

capital amount to total

venture capital amoun

(‰)

Explanatory variables

CEI ranking cei_ran

Ranking of CEI

CEI score cei_score Score of CEI

First order lag of CEI score L1_score First order lag of CEI score

Second order lag of CEI score L2_score

Second order lag of CEI

score

First order lag of CEI ranking L1_ran

First order lag of CEI ranking

Second order lag of CEI score ranking L2_rank

Second order lag of CEI

score ranking

Control variables

GDP per capita gdp_pc

GDP per capita (ten thousand

yuan)

Proportion of secondary industry second_pro

Proportion of secondary

industry output value in GDP

Proportion of tertiary industry tetiary_pro

Proportion of tertiary

industry output value in GDP

Table 3: Descriptive Statistics of Variables.

mean std min max

y 4.45 3.05 1.69 10.96

CEI_score 73.78 1.23 71.27 75.52

CEI_rank 27.84 2.46 23 32

gdp_pc 10.64 2.33 6.72 13.99

second_pro 50.20 5.82 38.36 56.13

tertiary_pro 45.83 6.23 39.58 58.54

GDP as control variables

Controls

in order to exclude

the influence of local economic development level

and industrial structure on the scale of venture capital.

The variable names, symbols and definitions in

model (1) are shown in Table 2.

4 EMPIRICAL ANALYSIS

In the sample cities selected in this article, the average

value of the city’s venture capital investment to the

country’s total venture capital is 4.45‰. The

maximum value is 10.96‰, and the minimum value

is only 1.69‰, which is quite different. The average

CEI score of the city is 73.78, and the standard

deviation is 1.23. The average value of CEI ranking

is 27.84, and the standard deviation is 2.46. The social

credit scores of the selected sample cities are

relatively close. The average per capita GDP is

106,400 yuan, and the standard deviation is 2.33,

which is a small difference. From the perspective of

industrial structure, the average value of the

secondary industry’s output value in GDP is 50.20%,

the minimum is 38.36%, and the maximum is

56.13%.

The average value of the output value of the tertiary

industry as a proportion of GDP is 45.83%, the

minimum is 39.58%, and the maximum is 58.54%.

The secondary and tertiary industries in the sample

cities have a relatively large proportion, and the

output value of the secondary industry is relatively

higher.

4.1 Regression Results

Table 4 shows the multiple regression results of

Changsha’s social credit system and venture capital

scale.

4.2 Descriptive Statistical Analysis

It can be seen from Table 4 that in the case of column

(5), the city credit ranking lags first, the city credit

ranking lags first, per capita GDP, the proportion of

the secondary industry, and the tertiary industry can

all be reached at the 1% significant level.

The coefficients of the proportion of the

secondary industry and the proportion of the tertiary

industry are 7.107 and 5.992 respectively. This means

that when the proportion of the secondary industry

increases by 1, it will increase the proportion of

venture capital received by Changsha to the national

venture capital by 0.71% when the proportion of the

tertiary industry increases by 1, it will increase the

proportion of venture capital investment obtained by

Changsha

City to the national venture capital

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

102

Table 4: Regression Results of Changsha.

(1) (2) (3) (4) (5) (6) (7)

y y y y y y y

L1_score

-2.16**

(0.56)

-0.22

(1.10)

-0.97

(0.51)

gdp_pc

-0.45*

(0.1768)

-0.43

(0.16)

-0.43**

(0.13)

-1.33***

(0.18)

-1.35***

(0.03)

-1.02**

(0.22)

-0.66*

(0.32)

second_pro

-4.42

(3.99)

-1.73

(3.47)

-1.38

(2.48)

6.75*

(3.11)

7.11***

(0.32)

2.58

(3.27)

0.43

(6.92)

tetiary_pro

-4.25

(3.71)

-1.53

(3.28)

-1.19

(2.30)

5.79

(2.86)

5.99***

(0.28)

2.02

(2.98)

0.25

(6.38)

L2_score

0.29

(0.46)

0.28

(0.38)

L1_rank

-0.44***

(0.08)

-0.43***

(0.02)

-0.29*

(0.09)

L2_rank

-0.14***

(0.01)

cei_score

-0.711

(1.054)

_cons

584.64

(383.98)

158.95

(369.07)

110.15

(229.85)

-571.84

(285.10)

-593.92***

(27.70)

-126.08

(323.15)

29.74

(621.43)

Obs. 9 8 8 9 8 9 10

0.88 0.90 0.90 0.937 0.99 0.97 0.54

Adj-R

0.77 0.64 0.75 0.873 0.99 0.92 0.17

F 7.62 3.48 6.38 14.78 1548.9 20.23 1.45

Standard errors are in parenthesis

*** p<0.01, ** p<0.05, * p<0.1

investment by 0.60%. The improvement of the urban

industrial structure will also expand the scale of

venture capital.

The coefficients of L1_rank and L2_rank are -

0.432 and -0.138, respectively. When the first-order

lag and second-order lag of a city’s credit ranking

increase by one place (ranking decline), the

proportion of venture capital investment obtained by

Changsha in the country’s venture capital investment

declines 0. 0432%, 0.0138%.

The coefficient of gdp_pc is -1.352, which means

that when the per capita GDP increases by 10,000

yuan, the proportion of venture capital investment in

Changsha will decrease by 0.135%.

5 CONCLUSIONS

This paper selects Changsha as the sample city and

uses data from 2010 to 2019 to conduct empirical

tests on the research hypotheses. The results show

that if the city’s credit score increases, the proportion

of the city’s venture capital investment in the

country’s venture capital will increase; the city’s

credit ranking declines the proportion of the city’s

venture capital investment in the country’s venture

capital will also decrease accordingly. Moreover, the

increase in the absolute level of the social credit

system has a greater impact on the scale of venture

capital than the increase in the relative level. The

empirical results also show that the relationship

between urban GDP per capita and the scale of

venture capital presents a more complicated form,

which requires further research to determine.

Based on the above conclusions, this article has

the following three policy recommendations:

5.1 Improving the Credit Guarantee

System

The credit guarantee system is an important way to

solve the problem of corporate financing, and it is

also an important link to perfect and perfect the social

credit system. Relevant departments should improve

laws and regulations to provide policy guarantees for

the credit guarantee mechanism. By building a credit

information sharing platform, strengthen the

circulation of credit data, alleviate the problem of

information asymmetry, and promote the smooth

flow of the guarantee process. In addition, make good

use of the advantages of supervision, streamline

handling procedures, implement classified

supervision, dynamic supervision, and precise

supervision of credit subjects; strengthen the whole-

process supervision before and after the event,

improve the credit commitment before the event, the

Research on Social Credit System and Venture Capital Investment Efﬁciency

103

credit supervision during the event, and the credit

reward and punishment mechanism after the event.

5.2 Improving the Construction of

Credit Information Sharing System

Whether the credit data is perfect or not is related to

the long-term and healthy development of China’s

credit service agencies, and the construction of the

credit data system can promote the improvement of

China’s entire credit system. Currently, information

between government departments cannot be shared,

social information is not transparent enough, and it is

difficult for credit service agencies to obtain relevant

credit data. Therefore, we should vigorously support

the construction of industry credit databases,

encourage credit service agencies to cooperate with

Internet platforms, and promote the construction of

credit data. Second, establish a characteristic credit

database. Credit service agencies should study the

credit data that is urgently needed in China, especially

the data that is urgently needed by national

development strategies, and the current credit data

needed for corporate development, and establish a

distinctive professional credit database to be more

effective to improve the social credit system.

5.3 Guiding Enterprises to Enhance

Their Credit Awareness

Corporate credit awareness is the fundamental

support point of China’s credit system construction.

By continuously enhancing its credit awareness,

encouraging them to improve their credit records,

accelerating credit accumulation, enhancing mutual

trust between banks and enterprises, and reducing

information opacity. At the same time, speed up the

development of the credit guarantee industry, speed

up the establishment of corresponding circulation and

disposal platforms for intellectual property, accounts

receivable and other intangible assets and current

assets, and standardize its evaluation, registration,

and management procedures. In addition, financial

product innovation should also be increased. When

banks design financial products, they should develop

and design products in a targeted manner according

to the characteristics of the investment and financing

activities of different types of enterprises. Promote

the orderly development of digital finance, improve

the digital financial credit system, and create

diversified digital financial products on the premise

of abiding by financial ethics.

ACKNOWLEDGEMENT

The paper is supported by the National Social Science

Foundation of China (Grant No. 17BJY062).

REFERENCES

Alaaraj, H., & Bakri, A. (2020). The effect of financial

literacy on investment decision making in southern

lebanon. J. International Business and Accounting

Research Journal, 4(1), 37-43.

Block, J., Fisch, C., Vismara, S., & Andres, R. (2019).

Private equity investment criteria: An experimental

conjoint analysis of venture capital, business angels,

and family offices. J. Journal of Corporate Finance, 58,

329-352.

Chen, M., & Grossklags, J. (2020). An analysis of the

current state of the consumer credit reporting system in

China. J. Proceedings on Privacy Enhancing

Technologies, 2020(4), 89-110.

Cheng, X., Huang, F., Palmon, D., & Yin, C. (2021). How

does information processing efficiency relate to

investment efficiency? Evidence from XBRL adoption.

J. Journal of Information Systems, 35(1), 1-25.

Gompers, P. A., Gornall, W., Kaplan, S. N., & Strebulaev,

I. A. (2020). How do venture capitalists make

decisions? J. Journal of Financial Economics, 135(1),

169-190.

Mac Síthigh, D., & Siems, M. (2019). The Chinese social

credit system: A model for other countries? J. The

Modern Law Review, 82(6), 1034-1071.

Sun, Y., Zeng, X., Cui, X., Zhang, G., & Bie, R. (2021). An

active and dynamic credit reporting system for SMEs

in China. J. Personal and Ubiquitous Computing, 25(6),

989-1000.

Ye, S. Q., Huang, J., & Liao, F. M. (2020). Information

Sharing, Financing Methods and Corporate Investment

Efficiency. J. Journal of Jiangxi University of Finance

and Economics, (4), 40-52.

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

104