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Abstract and Figures

In 2014, the Chinese government proposed to build a social credit system (SCS) to better collect and evaluate citizens’ creditworthiness, and grant rewards and punishments based on one’s social credit. Since then, various SCS pilots have been enacted. While current media and scholars often perceive SCS as a single and unified system, this paper argues that there are in fact multiple SCSs in China. I identify four main types of SCS and articulate the relationships among them. Each SCS has different assumptions, operationalizations, and implementations. China's central bank, People's Bank of China and the macroeconomic management agency National Development and Reform Commission are the two most important actors in the design and implementation of the multiple SCSs. Yet their distinctive views about what a "credit" is and what an SCS should be produced great tensions on the SCS landscape. I also historize current SCSs and show that many elements and assumptions of SCSs can be traced back to a broader People’s Republic of China’s (PRC) political history. At last, I propose an alternative theoretical framework to understand Chinese SCSs as a symbolic system with performative power that is more than a simple repressive and direct political project. [Citation format]: Liu, Chuncheng. 2019. “Multiple Social Credit Systems in China.” Economic Sociology: The European Electronic Newsletter 21 (1): 22–32.
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economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
22
Multiple social
credit systems
in China
Chuncheng Liu
Background
In 2014, the State Council of the People’s Republic
of China (State Council) issued a blueprint, the
“Planning Outline for the Construction of a Social
Credit System (2014–2020)” (Planning Outline), aim-
ing to build a national social credit system (SCS) in six
years. e Planning Outline claimed that many of so-
ciety’s current social problems, from food safety acci-
dents to academic dishonesty, result from the lack of
trust and strict regulation of those people who break
social trust (xinyong). To solve these problems, an SCS
is needed that systematically collects data about every
persons and every institution’s creditworthiness and
trustworthiness and can serve as a basis for a strong
reward and punishment system.
Since the Planning Outline came out in 2014,
various projects have been generated in the name of
SCS. For example, governmental agencies
regularly publicize information of people on
the “discredited judgment debtor list” (shixin
bei zhixingren mingdan) on governmental
websites and limit their access to things such
as ight tickets. Some cities published their
own municipal score system, which evalu-
ates residents’ trustworthiness, including
data such as “attitudes toward parents,” and
gives people with a high score rewards like
public transportation discounts. Many mobile appli-
cations launched their score systems and extend these
scores’ use into everyday life, such as on the dating
market and for foreign visa applications.
Scholars and media in both China and the West
commonly see these diverse practices as dierent as-
pects of one unied system. While the Chinese media
respond predominantly with praise without critical
inquiry (Ohlberg, Ahmed, and Lang 2017), Western
media and scholars oen depict the Chinese SCS as a
centralized surveillance tool of governmental control
that collects people’s biodata, online speech, and social
networking. ey view it as a crucial part of the Chi-
nese technoscience dystopia that connects commer-
cial systems with governmental datasets and makes
automatic detection and punishment possible (Bots-
man 2017; Falkvinge 2015; Liang et al. 2018; Mosher
2019; Qiang 2019).
However, a closer look at the Chinese SCS would
debunk these visions as misconceived and exaggerat-
ed. e Planning Outline did not propose “a” unied
and ubiquitous SCS that covers everything, but rather
various SCSs in dierent social localities. In practice,
as many scholars’ recent works have shown, dierent
SCS experiments have been conducted and have re-
sulted in a very messy and complicated reality (Ahmed
2019; Gan 2019; Kobie 2019). In this paper, I will show
that there has never been a single and unied SCS in
China. Instead, there are multiple co-existing SCSs at
dierent levels and in dierent elds that oen do not
mutually aggregate. Meanwhile, the Chinese SCSs are
still constantly developing and evolving, making
changes in designs and implementations at dierent
locations. e question we urgently need to answer is
not “What is the Chinese SCS?” but “What are Chi-
nese SCSs, and how do they work?”
e main body of current literature on Chinese
SCS is conducted by legal scholars and based on the
central governments published policy documents.
ey show a wide range of data collection, aggrega-
tion, and analytics plans with poor privacy protection
in policy designs (Y.-J. Chen, Lin, and Liu 2018; Y.
Chen and Cheung 2017; Liang et al. 2018). Some
scholars also examine media and public opinions to-
ward SCSs, both quantitatively and qualitatively,
showing general support without any fundamental
challenges (Kostka 2018; Lee 2019; Ohlberg, Ahmed,
and Lang 2017). e multiplicity of Chinese SCSs has
been more acknowledged in recent publications. Par-
ticularly, Ohlberg, Ahmed, and Lang (2017) identify
two kinds of pilot program for SCSs (commercial and
local governmental), which provide a useful distinc-
tion for this paper to further develop. Creemers (2018)
oers a historical review of the development of multi-
ple Chinese SCSs in dierent elds. Using data from
Beijing’s SCS websites, Engelmann et al. (2019) show
Chuncheng Liu is a Ph.D. student in the UC San Diego Department of Sociolo-
gy. His current project aims to examine the multiplicity of Chinese social credit
systems and its social impacts. His general interests include social classication
and quantication of people, science and technologies studies, political
economy, and HIV/AIDS (more information: chunchengliu.com). c6liu@ucsd.edu
economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
23Multiple social credit systems in China by Chuncheng Liu
what kinds of behaviors the local government tries to
promote and discipline.
Yet, when scholars discuss the multiplicity of
SCSs, they oen simply use examples from dierent
places without systematically examining the whole
landscape. ey also lack a clear demonstration of the
dierent logics and theories behind dierent SCSs, as
well as relationships among them. us, they overlook
the conicting contested process of dierent institu-
tions, from dierent governmental agencies to com-
mercial entities, in the development of the multiple
SCSs. To better understand current SCSs’ social im-
pact and future potentialities, we need to gain more
systematic and accurate knowledge about what SCSs
are doing. Based on the data I have collected from gov-
ernmental policies (both central and municipal) and
newspaper articles, I adopt a more realistic approach
and goal in this paper. I aim to explore and articulate
the multiplicity of current Chinese SCSs, examine di-
verse logics and operationalization strategies behind
them, and then explore the relationships among them.
Currently, there are four main kinds of SCS
emerging from two approaches. e rst approach
sees SCS as an infrastructure for economic and nan-
cial activities, which is led by the Peoples Bank of Chi-
na (PBOC), China’s central bank. PBOC designs and
implements a nationwide governmental nancial
credit system. ere are also commercial credit score
and rating systems developed by private corporations,
such as the Sesame score, which are under the super-
vision of PBOC. e second approach sees SCS as a
potentially useful tool for social governance, which is
led by the National Development and Reform Com-
mission (NDRC), a macroeconomic management
governmental agency under the State Council. SCSs
created under this approach include nationwide gov-
ernmental blacklists/redlists developed by dierent
central governmental agencies and municipal govern-
mental SCSs that are piloted at the local level.
I then historicize current SCSs and show that
many elements and assumptions of SCSs aer 2014
can be traced back to China’s political history. Finally,
I propose an alternative theoretical framework to un-
derstand Chinese SCSs as symbolic systems with per-
formative power that is more than a simple repressive
and direct political project.
Nationwide governmental
nancial credit system
e nationwide governmental nancial credit system
that PBOC has developed focuses on dealing with the
risks and uncertainties that information asymmetry
brings in the economic and nancial elds (Rona-Tas
and Guseva 2018). When “social credit” was rst men-
tioned in a Chinese national policy document in 2002,
it was this more narrowly understood nancial credit
system that the Chinese government discussed. PBOC’s
credit system covered both natural persons and corpo-
rations. e rst-generation nancial credit system
was launched in the early 2000s and produced credit
reports that for individuals contained merely nancial
and economic information such as the number of
credit cards, mortgage history, and delayed payment.
Aer the State Council published the Planning
Outline in 2014, PBOC started to develop the sec-
ond-generation nancial credit system, which is to be
launched in the middle of 2019. e second-genera-
tion credit system oers credit scores, like the FICO
system in the United States. Both generations of this
system collected most of their data from banks and
other nancial institutions and were only used in the
nancial eld by lenders.
Commercial credit rating and
score systems
Commercial credit rating for businesses had existed in
China long before the emergence of credit rating and
score systems for natural persons and the “social”
credit system. Since the 1990s, credit rating compa-
nies, such as China Chengxin, Dongfang Jincheng and
Dagong, were established to grant credit ratings for
businesses in the market. Like their international
counterparts, such as Moody’s and Standard & Poor’s,
these credit rating companies merely focus on the
market behavior of corporations and their ability to
pay back debts.
China launched its individual credit score mar-
ket on January 5, 2015, granting trial licenses to eight
commercial companies, mostly tech companies, to
build their own individual credit rating and score sys-
tem. Sesame credit score (zhima xinyongfen), built by
Ant Financial (mayi jinfu), a company aliated with
Chinese tech giant Alibaba, was launched on January
28, 2015, and has been the most commonly used com-
mercial credit system to date. Alibaba has more than
800 million users for its two platforms: Taobao, the
biggest online commerce platform in China; and Ali-
pay, the biggest mobile payment platform in China.
e Sesame credit score, like some other com-
mercial SCSs, diers in many ways from the PBOC’s
nancial credit system and other governmental SCSs
that I will elaborate on in the following section. First,
it includes personal data, such as educational level and
ownership of cars, in the credit score calculation. Us-
economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
24Multiple social credit systems in China by Chuncheng Liu
ers can upload their certicates and legal documents
for Ant Financial to verify their information. Second,
it includes ones social network relational data on Ali-
baba’s platforms. Yet, contrary to popular claims that a
Sesame score will be aected by a persons political
views on social media (Falkvinge 2015), Ant Financial
claimed that they do not have access to any content of
an individual’s social media posts (Hu 2017). ird, it
includes detailed consumption information, which is
incorporated into its model. A famous example is that
diaper consumption would lead to a higher score
while video game consumption would result in a low-
er score, as the former indicates more social responsi-
bility. Lastly, its model is more complicated than
PBOC’s nancial credit system and other publicized
governmental credit systems, claiming to use machine
learning to model more than ten thousand dierent
dimensions of data (Li 2015), while governmental
SCSs are still relatively primitive and based on points
accumulation.
e Sesame credit score soon became extremely
inuential and widely used, with the company’s large
user base and extensive promotion. A high Sesame
credit score would allow people such conveniences as
deposit-free public bikes, hotels, or renting services.
Meanwhile, it also became commonly used “o-label
(Rona-Tas 2017) in other social contexts, such as on
online dating platforms and for travel visa applica-
tions, which were intentionally promoted by Ant Fi-
nancial to increase the Sesame credit scores impact.
However, such uses, alongside other issues, resulted in
criticism from the PBOC, Sesame’s supervisor.
Aer the trial period of the commercial individ-
ual credit system ended in 2017, none of the eight
companies had their license renewed. PBOC’s ocials
criticized these companies for lack of data sharing
across dierent platforms, conicts of interests, and
lack of understanding of what should be considered as
credit” (Wu and Sun 2018). In early 2018, the Nation-
al Internet Finance Association of China, a govern-
mental agency under the PBOC, and these eight com-
panies became funders and shareholders of one com-
mercial individual credit score and rating company,
Baihang Credit. It became the only commercial com-
pany to receive an ocial license for conducting busi-
ness in individual credit score and rating in China.
According to Cunzhi Wan, director of the PBOC cred-
it bureau, once Baihang started to launch its services,
all the current commercial individual credit rating
services should be suspended. Although Baihang has
not provided any products or services since its estab-
lishment, Ant Financial and other companies have al-
ready withdrawn their credit score’s implementation
in the nancial market and shied priorities away
from scoring (Y. Zhang 2018).
Nationwide governmental
blacklist/redlist systems
e nationwide “social” credit system that most peo-
ple discussed aer 2014, however, is a system that
combines “discredited subject blacklist” and “credited
redlist” (shouxin hongmindan). A new cyberinfra-
structure, Credit China (https://www.creditchina.gov.
cn/) was launched in 2015 to publicize information of
people and institutions that are on dierent blacklists
and redlists and to promote policies and news about
SCSs and social trust. Its municipal versions, such as
Credit Beijing and Credit Shanghai, have also been
constructed. Currently, almost every city in China has
its own SCS website.
Although the centralized cyberinfrastructure
seems to indicate a unied blacklist/redlist system,
again, there is no such single system. Various black-
lists/redlists exist based on dierent central govern-
mental agency jurisdictions, while NDRC oversees
and/or coordinates their design and implementation.
Each blacklist has dierent inclusion criteria. For ex-
ample, the Oce of the Central Cyberspace Aairs
Commission (CCAC) proposed to include those peo-
ple who spread rumors online into its “Internet service
discredited subject blacklist.” While the Civil Aviation
Administration (CAA) put people who are disorderly
on ights on its blacklist. e consequence of getting
on dierent blacklists varies, even aer 44 central gov-
ernmental agencies signed an agreement in 2016 to
share data and punish jointly people on dierent
blacklists. Publicizing personal information, such as
name, address, along with the reasons why the person
is on the backlist, on SCS websites might be the only
unied punishment across dierent backlists. Taking
CCAC and CAA as an example, punishment for peo-
ple on the CCAC blacklist is merely a limitation of
their internet use, while punishment for people on the
CAA blacklist could be limitation of their air travel.
Among the dierent blacklist systems, the rst
and most mature is the discredited judgment debtor
list, which was launched on July 16, 2013 by the Su-
preme People’s Court (SPC) to deal with the problem
of the enforcement of court judgments. People on this
blacklist are included predominantly in connection
with nonpayment of debts in economic disputes aer
a court ruling. e typical case is a person (or busi-
ness) who owes others money but refuses to repay,
even though they have the economic capacity, aer the
court has ruled that they should. Courts, from local to
the supreme, are the main institutions in determining
who should be put on this list.
e maturity of the discredited judgment debtor
list is apparent in many respects. First, it is the most
economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
25Multiple social credit systems in China by Chuncheng Liu
widely used blacklist system so far. In January 2019,
for example, 215,582 people were on national discred-
ited lists. Among them, 578 were on the railway cor-
poration blacklist, 862 were on CAAs, and one was on
the Tax Bureau’s, while all the rest were on the discred-
ited judgment debtor list. A study of public records on
the Beijing SCS website also supports this point (En-
gelmann et al. 2019). Second, it has the most success-
ful implementation of joint sanctions. In the begin-
ning, the SPC already cooperated with dierent gov-
ernmental agencies to impose joint sanctions to limit
purchases by people on this list, including things like
rst-class train and ight tickets, real estate, and vaca-
tion-related expenses. Blacklist status would also in-
uence a persons children, as they cannot attend pri-
vate schools. In subsequent years, SPC and NDRC
have built more connections and strengthened their
power of joint sanction. Besides consumption con-
straints, rights related to working in the government
or promotion in public institutions are now all limited
in the new plan. In addition, people on the discredited
judgment debtor list would even be called dierently,
as laolai, which means “very dishonest person who re-
fused to pay his/her debts.” No specic name is given
to people on other discredited blacklists.
Discredited blacklists and credited redlists tar-
geted both natural persons and institutions such as
non-governmental organizations, business corpora-
tions, and governments. Institutions’ legal representa-
tives and key personnel in charge of the legal and -
nancial obligations would also be aected. Taking the
discredited judgment debtor list as an example, if an
organization refused to meet a court ruling (usually
nonpayment of nancial obligations), the organiza-
tion, plus its legal representatives and key personnel in
charge of the legal obligation, might be classied as
discredited judgment debtors. e most striking exam-
ples of the implementation of this system are in its ap-
plication to governments. In April 2017, media found
that more than 480 city, county, and country govern-
ments were classied as discredited parties (H. Zhang
2017). Governmental leaders of these places experi-
enced punishments such as limitations on plane and
train travel, while their governments’ borrowing and
investment activities were also signicantly limited.
Municipal governmental systems
e central governmental agencies designed the na-
tional discredited blacklist and credited redlist system,
constructed the cyberinfrastructure to publicize in-
formation, and built the multi-agency joint sanction
cooperation to punish discredited people. Yet it is
mostly local governmental agencies that implement
these policies: collecting and uploading data, classify-
ing and punishing people. Enforcement has not always
been very active. For example, one city had 11,000 dis-
credited judgment debtors in the system, but only en-
forced punishment 50 times (Rao 2018). Some other
cities are more active and innovative in the enforce-
ment of the national SCS. For example, the court in
Luoyuan, a small city in Fujian province, publicizes
discredited judgment debtors’ personal information
(name, photo, address, and money owed) at the begin-
ning of movies played at local cinemas. e court in
Qichun, a mid-sized city by Chinese standards in Hu-
bei province, even works with local mobile companies
to give discredited persons unique ringtones so that
people know from the tone if the caller is a laolai.
e multiplicity of SCSs is not only about the
various ways to implement punishment for people in
the discredited judgment debtor list. Many local gov-
ernments also construct their own municipal SCSs
and recongure the meaning of “trustworthiness” and
credit” in their local practice. Unlike the severe frag-
mentation among dierent agencies in the central
government, local governmental authority can better
coordinate (or force) dierent departments to work
together at the local level. is dierence is reected
in the organizational arrangements. While there is still
no cross-ministry SCS agency at the central govern-
mental level, municipal governments commonly es-
tablish a new municipal governmental agency, oen
named “XX SCS center/oce,” to design and imple-
ment municipal SCSs. Although some cities’ munici-
pal SCS for businesses is divided according to the dif-
ferent social elds under dierent governmental juris-
diction, the municipal SCS for natural persons is al-
ways united into one system on the local level. Some
municipal SCSs, such as Ningbo’s, produce credit re-
ports, while the most innovating and arresting munic-
ipal SCSs are based on quantied scores.
Suining, a county-level city in Jiangsu, was the
rst city to construct a quantied SCS for natural per-
sons. In 2010, Suining released a system called “mass
credit” (dazhong xinyong), which granted each resi-
dent a credit score. Misconduct such as jaywalking
would result in a score deduction. Suining’s mass cred-
it system soon faced a huge backlash from the domes-
tic media, which argued that the government should
not score their citizens in general and worried that
such practices were abuses of the government’s power.
Some even denounced Suining’s SCS as a system for
rigid social control akin to the “Good Citizenship Cer-
ticate” (liangminzheng) issued by Japanese colonizers
during Chinas occupation (Creemers 2018; Ju 2010).
e county government claimed to have revised the
system due to the controversy, yet it has not responded
to any other inquiries since then.
economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
26Multiple social credit systems in China by Chuncheng Liu
Rongcheng, a seaport county-level city in Shan-
dong, became the rst city to launch its own quanti-
ed SCS since the Planning Outline was issued in
2014, and with far less media exposure and controver-
sy than Suining. More cities followed this kind of
quantied SCS model. By May 1, 2019, 21 Chinese cit-
ies had published their own municipal quantied SCS,
and 27 more cities were in the process of preparing
quantied SCSs. We can observe a signicant increase
in the speed with which new municipal SCS turned to
quantication: 16 out of 21 have been launched since
2018 (Table 1). e dierent municipal SCSs have
commonalities as well as dierences. Some municipal
systems are more alike than others. For example, SCSs
of Ruzhou, Ankang, and Suifenhe have largely adopt-
ed Rongcheng’s 2016 SCS framework and indicators
(Rongcheng updated its metric in both 2016 and 2019)
with little local variation.
Cities with quantied SCSs are located predom-
inantly in the east coast provinces (Figure 1). Most of
them have a population of more than one million
(17/21, 81%) and occupy critical economic or political
roles. For example, Shanghai is the biggest city in Chi-
na, while Suzhou, Xiamen, and Hangzhou are cities
with the largest GDP in their provinces. Fuzhou,
Hangzhou, and Shenyang are capitals of their provinc-
es. Among the 21 cities, the majority (15/21) publi-
cized their metrics and indicators. Fuzhou, the capital
of Fujian province, only publicized its positive indica-
tors that reward credit score, keeping secret its nega-
tive indicators that deduct from a person’s credit score.
e number of indicators in publicized municipal
quantitative SCS metrics ranges
from 49 (Ordos) to 1503 (Weihai).
Most quantied municipal SCSs
also construct classication based
on a persons score. For example, in
Rongcheng, people with scores
≥960, 850–959, 600–849, and ≤599
will be classied as A, B, C, and D,
respectively.
Achieving good classica-
tions or high scores in the munici-
pal SCS will result in various bene-
ts supported by governmental
agencies and commercial organi-
zations. e most common reward
is public transportation discounts,
increased borrowing limits in pub-
lic libraries, and fast track for gov-
ernmental services. Some cities,
like Hangzhou and Weihai, also
give loan discounts for people with
a high municipal SCS score. Pun-
ishments for low municipal SCS
scores are smaller in scope and items. Most cities do
not even elaborate specic punishments, and in those
cities that do, punishments are mostly about honor
and suspending promotions for people who work in
public institutions. Suifenhe city government also in-
dicates that it suspends or decreases social welfare
payments for people with a very bad credit score.
Data sources of municipal SCSs are varied. Most
of these municipal SCSs are largely based on the ag-
gregation of pre-existing legal rules and regulations
from dierent governmental agencies. Yet dierent
municipal SCSs may include rules from dierent gov-
ernmental agencies. For example, Yiwu’s 2018 metric
explicitly includes 41 governmental agencies and pub-
lic institutions, while the SCS in Suqian only had ten
governmental agencies and public institutions. Courts,
the oce of procurators, police departments, trans-
portation departments, tax bureaus, and state-owned
utility companies are included in all publicized mu-
nicipal SCSs. Yet participation by health and educa-
tion departments, for example, is absent in some mu-
nicipal SCSs. In addition, some cities incorporate data
beyond pre-existing governmental rules and regula-
tions. e most salient example is Rongcheng, which
extends to cover social and moral behavior such as
conducting activities of superstition” (deduct 10
points out of 1000) in its SCS metric.
e kinds of data collected in the municipal
SCS vary. Still, most municipal SCSs focus merely on
individual behavior and do not include socioeconom-
ic or biological characteristics. Shanghai and Puyang,
for example, explicitly claim that collecting data such
Table 1. Chinese cities with municipal quantied SCS (by May 1, 2019; N = 21)
City Province Populationa
(million)
GDPb
(billion RMB)
Launch date Number of
indicators
Rongcheng Shandong 0.7 121.1 1/1/2014 391
Shanghai Shanghai 24.2 3267.9 4/30/2014 N/A
Suzhou Jiangsu 10.7 1859.7 1/23/2016 243
Yiwu Zhejiang 1.3 124.8 8/10/2017 175
Wuhu Anhui 3.7 327.9 11/1/2017 N/A
Weifang Shandong 9.4 680.5 1/9/2018 N/A
Suqian Jiangsu 2.9 277.1 3/23/2018 80
Suifenhe Heilongjiang 0.1 1.1 3/26/2018 236
Fuzhou Fujian 7.7 785.6 6/4/2018 68
Xiamen Fujian 4.0 479.1 7/5/2018 750
Ankang Shanxi 2.7 113.4 8/20/2018 210
Wulian Shandong 5.1 25.8 9/1/2018 305
Weihai Shandong 2.8 394.9 11/2/2018 1503
Hangzhou Zhejiang 9.5 1350.0 11/16/2018 N/A
Fuzhou Jiangxi 4.0 138.2 11/16/2018 N/A
Jiangyin Jiangsu 1.7 380.6 11/19/2018 112
Ruzhou Henan 0.9 43.4 11/29/2018 220
Taicang Jiangsu 0.7 124.1 12/4/2018 54
Puyang Henan 4.0 165.4 12/28/2018 83
Shenyang Liaoning 8.3 635.0 1/15/2019 N/A
Ordos Inner Mongolia 2.1 376.3 3/15/2019 49
Note: Data collected from the National, provincial, and municipal Statistics Bureau;
a Data date: 2017; b Data date: 2018, 1 RMB = 0.14 USD = 0.13 EURO
economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
27Multiple social credit systems in China by Chuncheng Liu
as ethnicity, religious beliefs, party membership, body
shape, genetic information, ngerprints, and medical
history in the name of SCS is illegal. Yet some cities,
such as Taicang, collect individual education, em-
ployment, and marriage data. For Rongcheng and
those cities that adopt Rongcheng’s framework, party
membership information, at least Chinese Commu-
nist Party (CCP) membership, will be collected, as
there is a specic section in their SCS metric that reg-
ulates party members’ behavior. Social relationships
would not inuence a person’s score. e only excep-
tion is in Rongcheng SCS, which punishes the guar-
antor of another who fails to repay a loan. More social
relation considerations were included in the reward
section but were limited to family level. For example,
in Rongcheng SCS, family members of a military per-
son will be rewarded with 5 points; family members
of a body/organ donor will be rewarded with 100
points.
Relationships among multiple
SCSs for natural persons
In the sections above I presented the four main kinds
of SCSs in two groups. ese multiple SCSs are not
necessarily interconnected. In general, the nationwide
governmental discredited blacklist, and particularly
the discredited judgment debtor list, is more connect-
ed than others, mostly through data input to other
SCSs (Figure 2).
Most of the nationwide governmental SCSs are
controlled separately by dierent central government
agencies and do not connect with each other. e only
exception is the relationship between PBOCs nan-
cial credit system and the discredited judgment debtor
blacklist. Discredited judgment debtor information
would appear in the PBOC’s credit report, which may
inuence the debtors’ relationship with banks and
other nancial sectors that use PBOC’s credit report as
a reference. e relationship among municipal and
commercial SCSs and the discredited judgment debt-
or list operates in the same one-way direction. If some-
one was classied as discredited in the judgment debt-
or list, in most municipal SCS rules, that person would
immediately be reclassied into the lowest credit level
with corresponding credit score deduction. For com-
mercial SCSs, Chinese SPC has sent discredited judg-
ment debtor information to Ant Financial since 2015,
so the people on the list would have a signicantly
lower Sesame score. Yet low municipal or commercial
SCS scores or levels would not inuence the nation-
wide discredited blacklist system.
Figure 1. Number of Chinese cities with municipal quantied SCS by mainland China provinces (by May 1, 2019, N=21)
economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
28Multiple social credit systems in China by Chuncheng Liu
Relationships and commensurability among dif-
ferent governmental municipal SCSs are more compli-
cated, given the diverse situations and metrics dier-
ent cities have. is issue limits the implementation of
municipal SCSs, and actions are now being taken to
solve it. For example, Shanghai, Jiangsu, Zhejiang, and
Anhui province published a cooperation action plan
last year, which mentioned the building of a mutual
recognition mechanism for dierent municipal SCSs
(Shanghai Development and Reform Commission
2018), yet we still need more evidence to understand
the process. Although some commercial companies,
such as Ant Financial and Liulian Technology (Shen-
yang), helped dierent governmental agencies to build
their own SCS models or cyberinfrastructures, there is
no evidence that commercial SCS data is included in
any municipal governmental SCS calculation.
Similar incommensurability could be found
among commercial SCSs. Before Baihang Credit was
established, each commercial SCS only used their own
data and public records with models designed by
themselves. As a result, dierent commercial credit
scores are dicult to compare with each other. is is
one of the critiques that PBOC ocials made about
commercial SCSs, and one of the important reasons
why Baihang Credit was established. PBOC wants to
aggregate data from all these companies to produce a
single credit score/rating through Baihang. In an in-
terview last year, a PBOC’s ocial indicated that Bai-
hang Credit, like PBOC’s own credit system, would
focus on the nancial eld and resist the potential
abuse in other social areas (Y. Zhang 2018). e con-
nection with the blacklist/redlist system and munici-
pal SCSs might, therefore, be very limited.
Historicizing social credit systems
As I showed above, although the SCS Planning Out-
line was published in 2014, many policies, platforms,
and practices that were later considered critical parts
of SCS were, in fact, proposed or enacted earlier. Look-
ing further back in history could oer us some insights
into SCSs. Scholars have connected current SCSs to
the personal le system (renshi dang’an), a traditional
governmental documenting practice that collects citi-
zens’ important information (such as education and
employment history, award, crime and misconduct re-
cords, and evaluations from dierent institutions) into
a le that is then stored in a government archive (Y.-J.
Chen, Lin, and Liu 2018; Liang et al. 2018). While the
connection between SCSs and dang’an highlights the
data collection and surveillance aspects of SCS, this
historicization does not capture another, and perhaps
more important, of SCSs’ functions: symbolically clas-
sifying people into dierent categories and granting
dierent social labels and life opportunities.
Bourdieu (2014) argues that the state has “the
monopoly of the legitimate use of physical and sym-
bolic violence over a denite territory and over the to-
tality of the corresponding population.” One of the
most important functions of the state, then, is to pro-
duce and canonize social classication. With this per-
spective, current SCSs are closer to the other two Chi-
Figure 2. Relationships among social credit systems for natural persons in China
economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
29Multiple social credit systems in China by Chuncheng Liu
nese systems: class of origin status (jieji chengfen) and
household registration (hukou).
From 1950 to 2004, every Chinese citizen was
assigned a “class of origin” label from a classication
system that conceptualizes the individual’s class status,
which included 45 labels such as “worker,” “landlord,
or “counter-revolutionist.” As a classication system,
the class of origin system was directly connected to the
political ideology of Marxism-Leninism that pre-
scribed who should and should not be trusted. It was
based purely on history and family relations: one’s
class status was determined by the economic status
and political activities of ones family’s male household
head before 1949 when the PRC was established (Trei-
man and Walder 2019). e state monopolized the
power to classify people under dierent class status.
People under dierent categories had signicantly dif-
ferent life chances. For example, people who had
“worker” or “poor peasant” class origins were able to
access more social resources, while people who had
“landlord” or “counter-revolutionist” class origins
were highly stigmatized and did not even have the
right to receive higher education during the Cultural
Revolution (1966–1976).
Another signicant classication system was
the household registration (hukou) system, which was
initiated in 1958. Every hukou had two pieces of infor-
mation: 1) location of registered residence; and 2) “ru-
ral hukou” or “non-rural hukou” classication status.
e initial information is based on place of birth. A
persons hukou information was hard to change aer
its assignment, although it was not prohibited (Chan
2019). Dierent hukous were associated with dierent
social resources and welfare, such as medical insur-
ance (Liu et al. 2018).
Both the class of origin and hukou classication
had the function to manage populations and redistrib-
ute resources, yet they were also symbolic. On the one
hand, their existence and implementation relied on
the control of the symbolic violence of the PRC state:
the government promotes such classications in poli-
cy documents, newspapers, and public speeches with
the strategic use of the historical discourse and narra-
tives. On the other, they had symbolic functions to
sustain a specic social order and legitimate the gov-
ernance of the CCP. On the individual level, being
classied into dierent categories also had a signi-
cant symbolic inuence on people. For example, being
a “rural hukou” was not only about ones place of ori-
gin. It also implies a backward, uneducated, and poor
symbolic identity showing subordinate social status
(Chan 2019). Class of origin classication faded from
Chinese daily life aer the Cultural Revolution, while
the hukou system became less important aer the ear-
ly 2010s, and the distinction between rural and
non-rural status was abolished in 2016. eir impact
on Chinese social life still persists.
Discussion
It has been ve years since the State Council issued the
Planning Outline, and 2020 is the deadline that the
State Council planned to establish the “basic legal and
standardization foundation of social credits and credit
infrastructure that covers the whole society.” In this
paper, I have systematically reviewed the multiplicity
of Chinese SCSs and interactions among them. is
multiplicity reminds us not to mistake dierent SCS
practices for parts of “the” unied Chinese SCS, but to
recognize them as various SCSs that are produced and
utilized in a specic social context. From national to
municipal, from governmental to commercial, there
are diverse SCS regimes with dierent criteria, scopes,
and implementation (Table 2).
It is hard to foresee if a nationwide, unied, and
quantied SCS that can cover every aspect of social life
will ever be designed and implemented in the future. It
is true that China is an authoritarian country that
could forcefully mobilize various state apparatuses and
the society to construct social projects no other coun-
tries easily could. e recent establishment of Baihang
Credit and withdrawal of other commercial SCSs did
show the government’s power and capacity to unify dif-
Table 2. Multiple Social Credit Systems in China
Category Leading agencies Main purpose Subject
Natural person Institution
Nationwide
governmental
People’s Bank of China (PBOC) Market infrastructure Personal credit report
and score Corporate credit report
National Development and Reform
Commission (NDRC) and other
central govern mental agencies
Reinforce social
governance
Discredited blacklist and
credited redlist systems based on dierent
governmental jurisdictions
Municipal
governmental
Supervised by NDRC, designed
by municipal authorities
Reinforce social
governance
Quantied score system
or credit report system
Quantied score system
for dierent elds
Commercial Supervised by PBOC, designed
by commercial companies
Market infrastructure
and prot gaining Credit score for individual Credit rating for corporations
economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
30Multiple social credit systems in China by Chuncheng Liu
ferent systems. However, we need to also remember
that Chinas authoritarianism is fragmented, especially
aer Maos death and the end of the Cultural Revolu-
tion: dierent governmental agencies have dierent
interests, logics, and traditions that may not easily be
aggregated (Lei 2017; Lieberthal and Lampton 1992).
Every time the central government proposes some
new but vague ideas or instruments, dierent govern-
mental agencies try to maximize their own interests
and power, and conict with others. Aer all, all com-
mercial SCSs are under the regulation of one govern-
mental agency, PBOC, while governmental SCSs are
inuenced by political conicts between multiple gov-
ernmental agencies and therefore show discrepancies
(Table 3). Dierent central governmental agencies
keep proposing their own blacklists, while dierent
municipal governments keep designing dierent local
SCS metrics. e emerging mutual recognition mech-
anism for dierent municipal SCSs is more like evi-
dence to show that the multiplicity of SCSs will last,
rather than the trend of a potential unication.
Tensions between the two key governmental agen-
cies in SCSs, PBOC and NDRC, further complicate
the situation. ey have dierent understandings of
what “credit” is about and what a “credit system”
should be. PBOC focuses on a narrow denition of
credit” and dierentiates it from “honest” or “trust-
worthy” (Wu and Sun 2018), which is exactly what
NDRC tries to promote through SCS. On the one
hand, PBOC’s SCS and commercial SCSs under its su-
pervision have a specic aim. Like other nancially
centered credit systems, scores produced by these
SCSs are about the possibility of one’s debt payment
behaviors in the future (Rona-Tas and Guseva 2018).
As a result, indicators act as predictors in these SCSs.
ey are not necessarily normative or even directly as-
sociated with the outcome independently (such as di-
aper purchase history), as long as they make sense in a
statistical way and produce useful results. In other
words, these SCSs are “forward-looking.
On the other hand, those SCSs under the NDRC’s
lead reward good behavior and punish misconduct
and try to discipline people to be trustworthy citizens,
yet they do not aim to predict a specic outcome in
the future, as no clear denition of “trustworthy citi-
zen” has ever existed. Scores and classications in
these SCSs are summaries of what people did in the
past. In other words, SCSs under NDRC are “back-
ward-looking.” As a result, each indicator in these
SCSs has specic and moralized meaning and must
directly associate with the general goal of these sys-
tems. Otherwise, people will challenge the legitimacy
of specic indicators or even the whole system.
Chinese SCSs should be historicized not as simple
extensions of the previous personal archive system,
but as an attempt to classify people and regulate their
social life. Of course, compared with the symbolic vi-
olence of the previous state classication, SCSs are sig-
nicantly more humanized, exible, and transfer the
responsibility for one’s classication status from fami-
ly to individual. Aer all, SCSs are based on peoples
achieved, not ascriptive, qualities. ey evaluate peo-
ple based on their own behavior instead of unalterable
family background; SCS metrics are more diverse than
single political considerations, and the implementa-
tion of SCSs are not associated with severe social ex-
clusion as previous systems were. Yet the fundamental
symbolic characteristics in SCSs that are based on
classication and quantication require a theoretical
framework that is beyond mere toolkits for active sur-
veillance for repressive authoritarian politics.
We need to conceptualize Chinese SCSs not as a
dystopian technology that could only exist in authori-
tarian societies, for its fundamental assumptions,
practices, and implications – quantifying, sorting,
classifying, and treating people dierently based on
their scores – are not that far away from the Western
democratic societies (Foucault 1995; Fourcade and
Healy 2016; Lee 2019; Lyon 2018). Fourcade and Healy
(2013) proposed the concept of “classication situa-
tions,” which captures the reality that prevailing uses
of the market classication, particularly credit score,
have produced a new social reality in which a persons
position in the credit market are consequential for
Table 3. Timeline for social credit system development in China
Time Event
1990s Many commercial credit rating companies for corporates established
2002 “Social credit” was rst mentioned in the 16th National Congress of the Communist Party of China
2006 People’s Bank of China launched its credit report system for individuals and corporates
2007 “Social credit system” (SCS) was rst mentioned in central government document
2010 Suining launched its quantied mass credit system and met with controversies
2013 Supreme People’s Court launched the discredited judgment debtor list
2014 Planning Outline for the Construction of a Social Credit System (2014–2020) published
2014 Rongcheng launched its quantied municipal SCS
2015 PBOC issued trial licenses for commercial personal credit rating and scoring business; Sesame score launched
2015 Credit China website launched; municipal credit websites followed
2018 Baihang Credit company established and received formal license for commercial personal credit rating business
2019 PBOC credit report system updated
economic sociology_the european electronic newsletter Volume 21 · Number 1 · November 2019
31Multiple social credit systems in China by Chuncheng Liu
their life chances. As a result, the social classication
may produce self-fullling prophecies and moralized
inequality (Fourcade and Healy 2013; Rona-Tas 2017).
SCSs are not only tools that classify people into dif-
ferent categories based on seemingly objective metrics
for rewards or punishments. ese classications are
symbolic and performative: they not only classify what
reality is, but also actively engage in changing society
and the subjects they have classied (Callon 2007;
Foucault 1995). Meanwhile, people under SCSs are
not compliant subjects without any agency. Classica-
tion, aer all, is about constant struggles (Bourdieu
1984), where dynamic social relations could be re-
vealed. As Rona-Tas (2017) shows, the o-label use of
credit scores may destabilize the classications’ legiti-
macy and nally destroy them.
We need more studies to engage in this eld from
dierent perspectives, and particularly more empirical
research. First, we need more studies on how SCS pol-
icies were designed at dierent levels, in particular lo-
cally. How were the inclusion criteria of national
blacklists/redlists established? How were dierent gov-
ernmental agencies and non-governmental actors in-
volved in translating regulations and moral standards
into numbers and producing quantied metrics? What
kinds of expertise and positionality were involved in
the process of operationalizing “trustworthiness,
creditworthiness,” and “honesty”? How were various
interests balanced? In addition, we need more studies
on how SCSs were implemented by the governmental
agencies and experienced by citizens. How do people
understand SCSs and make sense of them? Particular-
ly, what kinds of problems come up in these processes,
and how do people solve them? While it is true that we
have heard little about Chinese citizens’ systematic re-
sistance to SCSs, it does not mean problems do not ex-
ist. Do people game the system, or simply not care?
e multiplicity that I showed in this paper further
complicates these issues: How do dierent SCSs trans-
late, and/or produce dierent life experiences?
More importantly, as sociologists, we need to
ask what the social consequences of the SCSs are. How
performative are SCSs? Do SCSs work as a self-fulll-
ing prophecy, not reecting, but (re)producing one’s
creditworthiness? How may dierent SCSs (re)pro-
duce dierent social relationships and inequalities?
We need to not think of Chinese SCSs as a unique case
that is conned within the boundaries of a nation, but
to connect its design and practice to increasing imple-
mentation of similar surveillance, sorting and classify-
ing systems globally to understand the profound im-
plications of such algorithmic governance.
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Article
Is the convergence of new technologies and an authoritarian state bound to create an all-encompassing surveillance system? Is this happening in China with the Social Credit System (shehui xinyong tixi, abb. SCS)? Grounded in the field of Science and Technology Studies (STS), this article aims to describe the nature of the project by focusing on its inception and retracing how the initial visions materialized into the system that is now in place. It will do so by seeking to identify the sociotechnical imaginaries rooted in the SCS with the premise that these imaginaries, in particular the ones proposed by authoritative actors, shape the development trajectory of the SCS. Next, it asks whether the dominant sociotechnical imaginaries are control and power legitimation. By touching upon the role of officials, academics, private companies, and citizens in negotiating what is practicable and what is desirable, this article argues that the SCS does not follow a determined trajectory toward technologically enabled dictatorship. It is the result of a process that Sheila Jasanoff has described as co-production, as the various actors embed their values into the project by imagining, engineering, using or even rejecting elements of the SCS. This article finds that before even knowing all the possibilities offered by new technologies, a certain future was envisioned and shared. Rather than the need for control and surveillance, actors emphasized the importance of trustworthiness, the advancement of a post-industrial society, quality of life, and a sense of community. In a certain way, technology was expected to offer a solution to most, if not all social problems. The room left for experimentation supports the argument that sociotechnical imaginaries have the potential to impact the development trajectory of the SCS project. The article concludes that, after more than 20 years since its inception, the SCS is still a policy under construction, whose interpretation and use is yet to be stabilized.
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In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence (AI), entitled ‘New Generation Artificial Intelligence Development Plan’ (新一代人工智能发展规划). This strategy outlined China’s aims to become a world leader in AI by 2030, to monetise AI into a trillion-yuan ($150 billion) industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents.
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Xi Jinping’s ascent to power as Chairman of the Chinese Communist Party (CCP) was accompanied by changes in national governance strategies in the People’s Republic of China (PRC) which have progressively incorporated the use of big data. Shortly after, in May 2015, the Chinese State Council released a set of policy reforms under the abbreviation fang guan fu 放管服 (decentralise, manage and service). These reforms promoted big data led (1) market regulation, (2) supervision and management systems, and (3) service provision processes. By applying a case study analytical approach, this paper canvasses how advancements in big data contributed to these reforms of centralising information. Combining the joint knowledge of surveillance and China studies scholarship this paper offers evidence of big data surveillance streamlining China’s fragmented intergovernmental policy system. We build on David Murakami Wood’s 2017 outline of a political theory of surveillance and argue that decentralisation of data collection points and centralisation of both bureaucratic and public access to information is a key component of the Party-state’s regulatory governance strategy incorporating the use of big data and comprehensive surveillance. Our findings have implications for future analyses of the relationship between political organisation and surveillance within other nation-state contexts, particularly in situations where Chinese technologies and systems are being adopted and adapted.
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The book discusses not only how surveillance is done to people - the 'operator' perspective - but how in everyday life people encounter, engage and even initiate surveillance. In the latter, ordinary people do their own surveillance especially but not only on social media. The culture of surveillance, discussed in relation to a range of research, is thought of as a combination of surveillant 'imaginaries' and 'practices'. These are not innocent, however, but are imbricated with surveillance capitalism. Novels such as Dave Eggers' The Circle illustrate the culture of surveillance.