SOCIAL CREDIT AND THE QUANTIFICATION OF EVERYDAY LIFE - SESAME CREDIT'S MEDIATION OF POWER RELATIONS IN CHINA'S CREDIT CULTURE - UVA SCRIPTIES
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Social Credit and the Quantification of Everyday Life
Sesame Credit’s Mediation of Power Relations
in China’s Credit Culture
Mick Vierbergen
1
University of Amsterdam
RMA Thesis Cultural Analysis
24 February 2019
Student
Mick Vierbergen
Student number: 10662235
mick@vierbergen.net
Supervisor
Prof. dr. ir. Jeroen de Kloet
b.j.dekloet@uva.nl
Second reader
Dr. Daan Wesselman
d.v.wesselman@uva.nl
2
Table of Contents
Introduction .............................................................................................................................. 4
Chapter 1: Alibaba and the Chinese government ................................................................... 13
1.1 Introduction ........................................................................................................................ 13
1.2 China’s 2020 National Social Credit System ....................................................................... 13
1.3 Alibaba’s business ecosystem............................................................................................. 21
1.4 China’s credit culture.......................................................................................................... 26
Chapter 2: Sesame Credit as a Technological Platform .......................................................... 29
2.1 Introduction ........................................................................................................................ 29
2.2 A technical walkthrough of Sesame Credit ......................................................................... 32
2.3 Disassembling Sesame Credit: technology and content ..................................................... 37
2.4 Reassembling social credit.................................................................................................. 48
Chapter 3: Explicit Users of Sesame Credit ............................................................................. 52
3.1 Introduction ........................................................................................................................ 52
3.2 Self-reflection ..................................................................................................................... 55
3.3 Everyday user practices ...................................................................................................... 58
3.4 Motivations for usage......................................................................................................... 65
List of Abbreviations ................................................................... Error! Bookmark not defined.
3
Introduction
In early 2015, China’s central bank, the People’s Bank of China, requested eight leading tech
companies in the Chinese market to develop credit scoring systems, including Alipay’s
parent company Ant Financial and WeChat’s Tencent. This incentive was intended to restore
trust in market transactions between consumers and businesses, and, on a grander scale, in
China’s economy as a whole. As the economy has until recently primarily been cash-based,
the credit scoring initiatives were solutions to the shortage of personal credit records in
China (Creemers 23). Credit scores would presumably allow lower waged citizens, who lack
a credit history, to apply for small loans (S. Ahmed). This way, the government attempted to
‘catch up’ with credit economies like that of the United States.
The commercial credit systems were initially licenced as ‘pilots’, next to local
government-run pilots, for a nation-wide credit system that is to be constructed by 2020.
This system encompasses a broader meaning of ‘credit’; in short, the national Social Credit
System (社会信用系统 / shèhuì xìnyòng xìtǒng; hereafter ‘SCS’) not only aims to monitor
and manage financial credibility of citizens and companies, but also moral credibility
(Ohlberg, Ahmed and Lang 6). The Social Credit System evaluates individuals and institutions
– commercial as well as governmental – on their economic and social behaviour based on
massive data collection (Meissner 2). These ratings are the basis of a reward/punishment
system that fosters ‘good’ behaviour and penalises ‘bad’ behaviour. As such, the SCS is a
tool that allows the Chinese government to excite fine-tuned economic and social control.
In June 2017, however, the People’s Bank of China (PBoC) did not extend the official
licences to any of the commercial credit systems to serve as a pilot under the government
project of the SCS. This was partly due to differences in interest (Ohlberg, Ahmed and Lang
12) and an apparent lack of quality of the credit systems in the eyes of the central bank.
Nevertheless, there are still close ties between the government and the eight companies.
After refusing to extend the licences, the PBoC established a united credit scoring bureau
with the eight companies, called Baihang Credit (百行征信 / bǎixíng zhēng xìn). With this,
the Chinese government can keep a close watch on the commercial scoring systems and
remains relatively in control over the initiatives (Creemers 25).
4
The most notable and successful of these commercial pilots – and the object of
analysis in this thesis – is Sesame Credit (芝麻信用 / zhīma xìnyòng; also called Zhima
Credit), developed by Alibaba’s subsidiary Ant Financial. Sesame Credit is a consumer credit
scoring service that indicates the financial credibility of users, much like FICO scores in the
US. It is an opt-in service within the mobile payment application Alipay Wallet. As traditional
credit information is scarce, Sesame Credit uses data from the payment platform and
Alibaba affiliated businesses – such as the e-commerce websites and Alipay-connected
restaurants, third-party companies, and even certain government bureaus – to compute a
personal score between 350 and 950 (S. Ahmed). Although the algorithmic calculation is
blackboxed, the application identifies five categories of data that are used to compute the
score: credit history (e.g. from borrowed loans); contract fulfilment capacity (e.g. electricity
and gas bills); personal characteristics (age, education, occupation, place of residence, etc.);
behaviour and preference (e.g. usage of the Alipay app and shopping behaviour on Alibaba-
owned commercial websites such as Taobao); and interpersonal relations (based on
contacts in the Alipay app). Users with high Sesame scores are rewarded with privileges
such as cheap loans, deposit waivers on bike or car rentals, and priority lanes at airports,
and even expedited visa applications for certain countries (Ohlberg, Ahmed and Lang 12).
After the People’s Bank of China did not grant Sesame Credit a licence, it functions –
at least for the time being – as an autonomous commercial credit system, relatively
disconnected from the Social Credit System. Several scholars have pointed out, however,
that the relation between commercial actors such as Ant Financial and the government is
still ambiguous, and the development of this relationship requires close attention as the
construction of the SCS unfolds (Ohlberg, Ahmed and Lang 13; Creemers 27). Even at this
moment, while the two credit systems are allegedly separate from each other, close ties
between Sesame Credit and the government remain. Local governments, for example, use
Sesame scores to waiver deposits on public services such as healthcare and social housing
(Creemers 24), and conversely, Sesame Credit denies people blacklisted by the Supreme
People’s Court from purchasing luxury goods at Alibaba owned e-commerce websites
Taobao and Tmall (S. Ahmed). Furthermore, as state interventions on companies and
internet platforms are not uncommon in China, it remains unclear what the role of Sesame
Credit will be in relation to the SCS or other government regulations and how its user base
will be affected.
5Journalistic and academic context
While these developments have sparked nightmarish imaginations in English language
media, Chinese media seem to be less openly critical. One report of the Mercator Institute
for China Studies (MERICS) – a think tank focused on policy-oriented research – provides an
insightful examination of the Chinese media coverage of the Social Credit System in the first
half of 2017. They found that "[n]either official nor private media fundamentally question
the need for the Social Credit System” (Ohlberg, Ahmed and Lang 7). Chinese media
generally approach the topic of the Social Credit System quite even-handedly, affirming the
state’s opinions that it will cure societal issues, such as food security and consumer rights.
Critical voices mainly focus on the technical issues of the Social Credit System, such as
infrastructure and data quality, and privacy issues of corporate credit systems. Some articles
raise questions surrounding the hackability and ‘objectivity’ of commercial credit scores;
one mentions growing "data black markets" where hackers raise Sesame scores (Ohlberg,
Ahmed and Lang 8). While Chinese media often frame the state as a trustworthy authority,
there is a general tendency of distrust towards commercial enterprises such as Ant Financial
that have access to vast amounts of personal data. Censorship and self-censorship might be
one reason why Chinese media have been reluctant in raising critical questions towards the
State. Another reason might be that commercial credit systems are currently way more
common than the state-run pilots, and thus receive more critical attention.
In line with the passive attitude of journalistic media, the topic has not raised much
critical debate on Chinese social media either. Ohlberg, Ahmed and Lang note that social
media coverage mainly consists of reposting of journalistic articles (5). Manya Koetse, a
Chinese social media expert from the social trend-watching website What’s on Weibo,
agrees that the Social Credit System currently is not a hot topic on the internet. On the
contrary, she writes that when Sesame Credit is mentioned, it “is mostly linked to fun extras
and the Chinese sharing economy.” (Koetse) To whatever extend censorship plays a role
here too, it remains evident that the Social Credit System is not a topic much-discussed
under Chinese citizens.
Conversely, English-language journalism generally has a more dystopian view on the
construction of the Social Credit System. Two comparisons pop up regularly in popular
6media: George Orwell’s 1984 and the episode ‘Nosedive’ of the sci-fi series Black Mirror.1
Although concerns about increasing surveillance and centralisation of power are pressing
indeed, these dystopian comparisons often simplify the real-world situation and provide a
one-sided view. Moreover, factual information is often lacking or incorrect. Quotes by
Alibaba executives are decontextualised and exaggerated through echo-chamber effects2,
and many articles confuse Sesame Credit with the Social Credit System. They often frame
Sesame Credit as a potential forerunner of a ‘national citizen rating mechanism’ (it is
unclear if the SCS will even use a numeric scale for evaluation), while it should rather be
viewed as part of one of the Social Credit System’s multiple goals: to foster the
development credit economy.3
Academic literature has responded to the construction of the Social Credit System
and media coverage primarily by providing an as-accurate-as-possible view on the current
state of affairs. There is still little academic coverage on the topic of social credit, and there
is a need for more research from various disciplines. One of the main challenges of this
thesis is therefore that there is little academic literature and it is hard to tell fact from
fiction. Some of the primary academic sources of this thesis were published, or yet to be
published, during the time of writing.4
Rogier Creemers, one of the leading law scholars researching the SCS, translated
government policy documents and published a detailed description of the current situation
in May 2018. Shazeda Ahmed is an expert on commercial and state pilot projects and
1
See for example: “Big Data Meets Big Brother as China Moves to Rate its Citizens” (Botsman); “Sesame
Credit, Fintech and Social Credit Scores in China” (Borak); “China: When Big Data Meets Big Brother” (Clover);
“Open Sesame? China’s Social Credit Revolution Hits A Roadblock” (Perkins); “Black Mirror Is Coming True in
China, Where Your 'Rating' Affects Your Home, Transport and Social Circle” (Vincent).
2
One statement is of Ant Fiancial’s technology director, Li Yingyun: “Someone who plays video games for 10
hours a day, for example, would be considered an idle person, and someone who frequently buys diapers
would be considered as probably a parent, who on balance is more likely to have a sense of responsibility.”
The statement was originally published by the Chinese journal Caixin in 2015, but has been copied in
numerous English-language articles. See for example: “Big Data Meets Big Brother as China Moves to Rate its
Citizens” (Botsman); “How China Wants to Rate Its Citizens” (Fan); “China’s “Social Credit System” Will Rate
How Valuable You Are as a Human” (Galeon); “China 'Social Credit': Beijing Sets Up Huge System” (Hatton).
3
See for example: “Big Data Meets Big Brother as China Moves to Rate its Citizens” (Botsman); “China Wants
to Give All of its Citizens a Score – And Those Who Fall Short Will Be denied Basic Privileges” (Denyer).
4
For my research, I attended the 16th Chinese Internet Research Conference – themed ‘Modes of Connection',
in Leiden (22-23 May 2018) – which dedicated one panel to the Social Credit System. The presentations
focused on advances in pilot projects and their role in the construction of the SCS. Audience questions
addressed issues of clarification of rumours from the media. The presentations at the conference and the
subsequent one-day workshop on the SCS show that research on social credit systems is still in an early stage
that mainly approaches the SCS and its pilots descriptively.
7Sesame Credit-related issues. She published an article at The Citizen Lab in 2017 on security
considerations of Sesame Credit and, together with Mareike Ohlberg and Bertram Lang, the
MERICS report on media coverage and pilot projects discussed above. Genia Kotska has
recently published a quantitative study on public acceptance and user perceptions of social
credit pilots. Other essential publications are Mirjam Meissner’s 2017 MERICS report on the
implications of the SCS for businesses and Packin, Lev-Aretz’s article on the SCS and the right
to be ‘unnetworked’, and Xin Dai’s descriptive analysis of the Social Credit System Project in
China’s emerging reputation state (draft paper).
The academic corpus on the Social Credit System and affiliated pilots so far has
primarily approached the issues from a legal and economic perspective, as much of the
research is policy-oriented. With the notable exception of Kotska’s research, user
perspectives are underrepresented. Besides Hao Wang’s (unpublished) research on
disciplinary techniques of credit systems through algorithmic transparency, a critical
approach to social credit has lacked in this corpus. Humanities-grounded Chinese media and
internet studies has so far neglected social credit-related issues. While state influence on
social media platforms is well-discussed in this field – focusing on issues of censorship and
contention (Poell, de Kloet and Zeng; Yang, Power), civility (Yang, Emotions; De Seta), and
‘networked authoritarianism’ (MacKinnon) – critical research on the relation between credit
platforms and the Chinese government has been lacking. Jia and Winseck have, however,
argued that “to better understand the Chinese internet, we must grasp not just the tight
relationship between the state and business but the emergent three-way ties between the
state, internet companies, and finance capital.” (Jia and Winseck 32) Instead of a political
perspective on the Chinese Internet, the field must also take the view of political economy.
This thesis also relates to platform studies and critical data studies, as it quite closely
follows José van Dijcks analytical model to examine platforms through the lens of political
economy and Actor-Network-Theory (ANT) described in her book The Culture of
Connectivity. Her methodology approaches online platforms both as socioeconomic
structures – focusing on ownership, governance, and business model – and as sociotechnical
constructs – by analysing technology, users and content. Although Van Dijck uses her
method to study how social network sites and platforms for user-generated content
mediate sociality, I employ her model to analyse the power structures of the trading and
marketing platform Sesame Credit, and, consequently, also its effects on sociality.
8Moreover, platform studies, and particularly Van Dijcks book The Culture of
Connectivity, has focussed mainly on ‘Western’ platforms (see also Srnicek’s Platform
Capitalism and Van Dijck, Poell and De Waal’s new book The Platform Society) and could
benefit from case studies of platforms in Chinese contexts. Conversely, academic and non-
academic discourse should regard the social credit systems in China in the global context of
increasing platformisation and datafication, and growing reputation economies.
This thesis positions itself at the intersection of these academic debates – adding a
critical perspective to the law and economy-dominated discussion of Chinese social credit
and introducing the topic to Chinese internet and media studies – by analysing how Sesame
Credit mediates power relations as a technological platform. Building on the policy-based
research of social credit studies, it takes a ‘top-down’ approach of what José van Dijck calls
“platform strategies” and contrasts this with a bottom-up perspective of “user tactics” (20).
I use qualitative in-depth interviews with users to complement Kotska’s quantitative
analysis. The main question of this thesis is “How does Sesame Credit mediate power
relations between Alibaba and its users?” This research question is split up into three sub-
questions: “How is Sesame Credit positioned in a network of institutional power structures
of Alibaba and the Chinese government?”; “How are Alibaba’s norms inscribed in Sesame
Credit as a technological platform?”; and “How does disciplinary power operate through the
everyday user practices of Sesame Credit and what are the user’s motivations of use?”
Theory and methodology
Methodologically speaking, this thesis follows the approach of ‘cultural analysis’ (Bal).
Cultural analysis typically aims to ‘conduct a meeting’ between an object and a concept and
uses close reading, derived from textual analysis, to analyse cultural objects. Cultural
analysis aims to take on a critical perspective on cultural phenomena and adopts a self-
reflexive stance. As cultural analysis aims for a dialogue between object and concept, the
theory will be introduced – and thoroughly discussed in relation to the objects – in the
chapters whenever the analysis asks for it. Hence, this introduction does not provide an in-
depth overview of the theoretical framework to avoid ‘applying’ theory ‘on’ the object.
It seems fit, however, to note in advance that this thesis adopts a Foucauldian
conception of power to answer the above-mentioned research questions. For Foucault,
power is not a quality that an entity can possess but is relational. This allows us to see how
9Sesame Credit is positioned in a network of power relations; between Alibaba, the Chinese
government, users, and merchants connected to the platform. Actor Network Theory offers
a productive view on Sesame Credit’s practice of mediation of these power relations, as it
recognises non-human forms of agency: the technological actors within the credit system
and the platform as a whole.
The methodology of cultural analysis does not come without complications. First of
all, Sesame Credit is not a clearly demarcated cultural object like a text or an artwork, which
makes it difficult to close read. And, as might be clear by now, academic literature is still
struggling to define its characteristics and boundaries. Practically speaking, this means that
this thesis frames Sesame Credit in different ways – as a technology of power, a
technological platform, and an everyday consumer credit system – and close reads relevant
objects, such as policy documents, the Sesame Credit application and its interface, and user
experiences and motivations. Besides this, Sesame Credit is defined by another cultural
object, that is the Social Credit System. As the Chinese government initiated the
development of Sesame Credit as a pilot under the project of the SCS, an analysis of Sesame
Credit cannot exclude the examination of its relation to the national Social Credit System.
Furthermore, many parts of the Sesame Credit are ‘blackboxed’, i.e. only the inputs
and outputs are visible while the inner workings remain opaque. Both on a technical level
and the level of business strategies, these mechanics are hidden. This problem is
unavoidable when analysing a financial platform such as Sesame Credit, as trade secrets
prevent businesses to reveal these inner workings. To circumvent this problem for close
reading, I resort to reverse engineering techniques – looking at inputs and outputs to
uncover parts of these inner structures – and rely on existing academic literature to analyse
technical aspects from the ‘outside’.
To examine how users engage with Sesame Credit in everyday life, I made a research
trip to Shanghai, where I stayed for four months. In this time, I used Alipay and Sesame
Credit on a daily basis and spoke to multiple Chinese users of the credit system. I set up a
personal Sesame Credit account for this research, which allowed me to study how my daily
actions influence the score (and to try reverse-engineering the algorithmic decision making).
I also performed six in-depth semi-structured interviews (Bernard 212) with Chinese users
within my social circle. Of the six informants, three were male and three female, aged
10between 21 and 27. Most came from and lived in Shanghai or other urban areas in China.
The interviews were performed in English, as my Chinese is insufficient for conversations.
The interviews were set in an informal setting, at their home, in a café, or over the
phone, and structured like informal conversations. The interviews are audio recorded5 and
transcribed afterwards. For ethical reasons the participants were noted in advance about
the purposes of the research and were aware that the conversation was being recorded.6
They were also informed that the interview was on a voluntary basis and that they were not
obliged to answer any questions they did not want to answer, as some questions ask for
their opinion on politically sensitive topics. I also replaced their names with common given
names in China to ensure their anonymity.
The ethnographic study on users and usage of Sesame Credit is limited in scope, due
to restrictions of time and resources. Its aim is not to provide a full ethnography of social
credit in everyday life. Rather, it functions as a pilot study that identifies the most general
uses and motivations. As such, it forms a base for further research on the topic. In line with
this, and because of the little academic coverage about Chinese social credit, this thesis as a
whole is a pilot study that explores the power relations of social credit systems. Hence, it
focuses on three diverse aspects: a political economy of Sesame Credit, the technological
structure of the platform, and an audience ethnography.
Chapter structure
The first chapter aims to answer the question “How is Sesame Credit positioned in a
network of institutional power structures of Alibaba and the Chinese government?” This
chapter builds on the policy-based research of social credit studies and presents an
overview of the current state of affairs of the national Social Credit System. It then frames
the object of Sesame Credit within this context. I close read one policy document that
describes the government’s plans most clearly and has been central to social credit studies.
Then, I discuss Sesame Credit within the ‘ecosystem of connective media’ (Van Dijck,
Culture) and the ‘business ecosystem’ (Moore) of Alibaba along the lines three concepts
from Manuel Castells’ theory of political economy: ownership, business model, and
5
One interview is partly video recorded to follow the user’s walkthrough, as was proposed by the participant
himself.
6
This research project is approved by the Ethics Committee of the University of Amsterdam.
11governance. Building on Foucauldian theory, I argue here that Sesame Credit is a
‘technology of power’. The chapter ends with a discussion on how Sesame Credit relates to
Chinese media and Internet studies.
The second chapter analyses Sesame Credit through a close reading of the
smartphone application and its interface. The research question is “How are Alibaba’s
norms inscribed in Sesame Credit as a technological platform?” Here, I use Actor-Network-
Theory to account for non-human actors that mediate power relations. I use Light, Burgess
and Duguay’s ‘walkthrough method’, to first give an outline of how the system works. In
their method, the researcher walks the reader through an application, focusing on the
functions and features and semiotic structures. Following José van Dijck, the analysis uses
four intersecting concepts: data, algorithm, interface and content. This chapter aims to
explain the “implicit participation”, or the usage inscribed in the technology itself (Van Dijck,
Culture 33).
The third chapter is themed around the user of Sesame Credit and aims to answer
the question “How does disciplinary power operate through the everyday user practices of
Sesame Credit and what are the user’s motivations for use?” It focuses on the intersection
between implicit and “explicit use” of the application (Van Dijck, Culture 33). Explicit use
refers to the actual use of technological platforms in situ. This chapter approaches explicit
users as ethnographic subjects, and their everyday usage practices and motivations for use
are the objects of analysis. I analyse this through a close reading of in-depth interviews.
During the interviews, I asked the participants to walk me through their everyday usage of
Sesame Credit, following the guidelines of Light, Burgess and Duguay’s ‘walkthrough
method’. The chapter first offers a reflection on my position as a researcher before going
into the user practices and their motivations for use.
12Chapter 1: Alibaba and the Chinese government
1.1 Introduction
The question “How is Sesame Credit positioned in a network of institutional power
structures of Alibaba and the Chinese government” is central in this chapter. To answer this,
I first elaborate on the Chinese government’s plans to construct a national Social Credit
System (SCS), under which China’s central bank initially licenced Sesame Credit as a
commercial pilot. The next section focuses on the function of Sesame Credit in Alibaba’s
ecosystem by analysing the structures of ownership, business model, and governance.
Following a Foucauldian theoretical framework, it argues that Sesame Credit is a technology
of power that mediates (financial, social, behavioural (etc.) norms to its user-base that
support Alibaba’s business model. Lastly, I look at the relation between Sesame Credit and
what I term an ideology of ‘credit culture’ that the Chinese government aims to establish,
basing my analysis on literature from Chinese media and Internet studies.
1.2 China’s 2020 National Social Credit System
This section gives an overview of the current status of implementation of the national Social
Credit System. It first discusses an essential policy document released in 2014 that gives an
outline of what the Social Credit System will be and its how it is to be constructed.
Hereafter, I focus on the mechanisms that are already in place.
The Construction of a Social Credit System
In June 2014, the State Council released the “Planning Outline for the Construction of a
Social Credit System (2014-2020)” (“社会信用体系建设规划纲要(2014-2020年)" / "shèhuì
xìnyòng tǐxì jiànshè guīhuà gāngyào (2014-2020 nián)") This document is the first of a series
of government policy publications and is still the most comprehensive overview of what the
SCS will look like. It has been a cornerstone in policy-oriented research in social credit
studies.
Rogier Creemers, who made a translation of the policy document, comments that
the document “put forward a timetable until 2020 for the realization of five major
objectives: creating a legal and regulatory framework for the SCS, building credit
13investigation and oversight, fostering a flourishing market built on credit services, and
completing incentive and punishment mechanisms” (12). The prime objective of the SCS is
the promotion of sincerity on four different levels of society: it aims to make government
affairs more ‘sincere’, to increase commercial sincerity, social sincerity, and enhance
credibility in juridical affairs (China, State Council). The “Planning Outline” also emphasises
the SCS’s dual goal of social management and economic regulation. The creation of the
Social Credit System is paralleled with an ideological transformation; it goes hand-in-hand
with the promotion of ‘Core Socialist Values’ (社会主义核心价值观 / shèhuì zhǔyì héxīn
jiàzhí guān). On the other side, it will be an important mechanism that strengthens the
Socialist Market Economy (社会主义市场经济 / shèhuì zhǔyì shìchǎng jīngjì).
The Socialist Core Value System (社会主义核心价值体系 / shèhuì zhǔyì héxīn jiàzhí
tǐxì) is a set of twelve moral principles – of which integrity is one (诚信 / chéngxìn;
etymologically close to ‘credit’/’sincerity’) – that is introduced by the State in 2012 to fight
the ‘moral decay’ of the past decades of increasing individualization. While these twelve
values are being promoted in education, via propaganda and the strategic use of different
media (China, State Council, Planning Outline, Part III.1), the Social Credit System will give
another impulse in the moral schooling of Chinese citizens. In particular, the SCS aims to
establish a ‘sincerity culture’7 (诚信文化 / chéngxìn wénhuà) and create a “thick
atmosphere in the entire society that keeping trust is glorious and breaking trust is
disgraceful and ensure that sincerity and trustworthiness become conscious norms of action
among all the people.” (sic) (China, State Council, Planning Outline, Part I.3)
On the other hand, the Social Credit System is an essential component of the
Socialist Market Economy. The Socialist Market Economy was introduced in Deng Xiaoping’s
economic reformations of the late 1970s and merges a planned Socialist economy with
market elements. It is part of his idea of ‘material civilisation’, which he proposed alongside
the concept of ‘Socialist spiritual civilisation’ (Yang, Emotions 1949). The SCS allows the
State to effectively regulate the behaviour of market participants and reach industrial and
technological targets (Meissner 4). As the evaluations of customers and companies will
drastically influence their market positions, the SCS can infuse regulations in market
7
Rogier Creemers has translated this as ‘sincerity’ but bear in mind that it is the same word as the Socialist
Core Value of ‘integrity’.
14exchange. In other words, planning will be implemented in the market economy itself and
State influence will be visible on the level of ‘liberal’ trade as credit scores become part of
competition.
The “Planning Outline” states that governmental sincerity is the core of the creation
of the Social Credit System (part II, section 1). Without a trustworthy network of
administration and honest enforcement of regulations, the implementation of the SCS on
other levels will not be possible. In line with this, the SCS also aims to improve judicial
credibility to assure an honest prosecutorial basis for punishment systems. The government
also has an exemplary role in society; raising honesty, accuracy, efficiency in government
affairs, and even transparency of policies and regulations, will be a model of sincere
behaviour for the rest of society.
In the commercial sector, the Social Credit System will evaluate the economic and
social behaviour of companies. The State Council hopes to resolve societal problems such as
issues with food security, tax fraud, violation of consumer rights, and environmental
pollution (China, State Council, Planning Outline, Part II.2). Meissner’s 2017 MERICS report
shows a comprehensive diagram that shows what data is used to compile credit scores and
what possible consequences are (see Image 1). Input data includes information on company
representatives, annual reports of corporations, and compliance with government
regulations (for example internet regulations, safe work environments, tax payment,
environmental impact, and intellectual property). The scores have an effect on subsidies
and investment opportunities of the companies, and even travel possibilities and career
opportunities of company representatives, among other things. Ultimately, the system will
also incorporate real-time and remote monitoring and automated score computation
(Meissner 4). In e-commerce, for example, real-time data could provide information on
customer satisfaction, delivery, product quality, etc. In the transportation sector, vehicles
will be tracked remotely, and in polluting industries emissions will be monitored in real-
time. Meissner notes that, instead of a centralised rating organisation that compiles a single
score per company, “the government plans suggest a rather diversified and decentralized
market for social credit ratings” with multiple (commercial and governmental) score
providers (5).
15Image 1: Dataflows of the Social Credit System in the commercial sector (source: Meissner 4)
For the improvement of ‘social sincerity,’ the Social Credit System is implemented in areas
of healthcare, social security, and education. The "Planning Outline" also dedicates one
paragraph to the construction of a credit system that evaluates ‘natural persons’. It states
that the Social Credit System will collect credit records on the economic and social lives of
individuals, alongside the ratings that relate to their professional function. Moreover, the
document also mentions that a system will be constructed to evaluate the online behaviour
of so-called ‘netizens’ (网民 / wǎngmín). Interestingly enough, the document does not
mention quantitative scoring as a method of evaluation (Creemers 13). Although the
document implies that the SCS will increase surveillance on citizens and evaluate their
behaviour, it is unclear if it will rate citizens with an actual ‘citizen score’.
Although the mention of a credit system for natural persons and netizens is brief in
this first document, further publications specify some of the consequences for individual
citizens. Blacklisted people are for example restricted to hold high positions in companies or
government bodies. Other imposed restrictions are on train or air travel, hotels and
restaurants, conspicuous consumption travel (such as organised holidays to foreign
countries and other holiday areas), high-fee schools for children of the subject, and building
and renovating housing (China, State Council, Opinions).
16In short, while the Chinese government presents the Social Credits System as a
"cure-all" for a whole range of societal problems (Ohlberg, Ahmed and Lang 5), it will also be
a massive technology-based system for economic and social control (Creemers 3). The SCS
has two main functions: to promote financial credit and to track and manipulate citizens’
social and moral behaviour. The two are, however, not completely separated. The core of
the system will be the collection of data on individuals and organisations from a multiplicity
of sources, possibly also commercial ones, that will be shared among local and central
government bodies. On the basis of these data and evaluations, citizens and institutions will
be evaluated and penalised or rewarded accordingly.
Current status of the implementation
Although the construction of the Social Credit System is still at an early stage, certain
fundamental components have already been set in place (Ohlberg, Ahmed and Lang 2). So
far, the primary concern has been the construction of a data sharing infrastructure and the
establishment of the Joint Punishment System. Besides this, local governments and
commercial actors have established pilots to experiment with the practical application of
social credit systems.
One significant factor of the digitisation of social management has been the
introduction of the 2003 Identity Card Law (Creemers 20). The 18-digit identity cards render
citizens digitally identifiable and allow for efficient data collection. It has become a universal
system for digital identification and is used by different government bodies and private
enterprises. Together with the regulations on real-name authentication requirements in
online environments, such as social media and other account-based systems, and in mobile
phone registration, the introduction of the ID code made it possible to connect multiple
data points and store this information efficiently (Creemers 21).
For the collection and sharing of data for the Social Credit System, the government
established the ‘National Credit Information Sharing Platform’ (全国信用信息共享平台 /
quánguó xìnyòng xìnxī gòngxiǎng píngtái) in October 2015 (Meissner 6). This has been the
central platform that receives data from multiple ministries and other government bodies,
such as the Peoples Bank of China and the National Development and Reform Commission
(NDRC) that both lead the implication of the SCS. The Information Sharing Platform is the
17back-end data provider for the information platform ‘Credit China’ (信用中国网 / xìnyòng
zhōngguó wǎng). This website, built in collaboration with Baidu, provides information about
the Social Credit System itself and makes public credit-related information about companies
and individuals (Creemers 21). The National Enterprise Credit Information Publicity System
(国家企业信用信息公示系统 / guójiā qì yè xìnyòng xìnxī gōngshì xìtǒng) is another
platform that publicises information – particularly on companies – that is backed by the
Information Sharing Platform. These practices of publicly ‘naming and shaming’ are part of a
punishment system that is connected to the Social Credit System. Although these databases
are not yet up to their full potential, they form the basis of a data sharing infrastructure that
could in the future be the core of the SCS (Ohlberg, Ahmed and Lang 11).
The Joint Punishment System (联合惩治体系 / liánhé chéngzhì tǐxì) is another
element that has already been set in place. Jointly established by 45 collaborating state
bodies, it is a blacklisting system that currently primarily lists citizens that resisted court
orders and companies that do not conform to the law or regulations (Ohlberg, Ahmed and
Lang 10). Punishments that follow for blacklisted individuals and companies include
restrictions in economic opportunities, constraints in holding high-positions in certain
organisations, and limits in conspicuous consumption (Creemers 15). The latter
encompasses restricted access to first-class air travel and high-speed trains, luxury hotels
and restaurants, holidays to foreign countries and fee-paying schools for the entrant’s
children. As these blacklists are shared with local governments and even private enterprises,
the restrictions are imposed everywhere. Alibaba’s Taobao and Tmall, for example, deny
blacklisted entrants to make luxury purchases (. Some local governments have already
incorporated the blacklist system as a punishment mechanism for minor offences. The city
of Ningbo, for example, has blacklisted fare-dodging individuals and Shenzhen installed
facial-recognition technologies at zebras and reports repeatedly caught individuals
(Creemers 17-18).
In several cities and districts, local governments are experimenting with social credit
information systems. The NDRC and PBoC have appointed 43 municipalities to construct
pilots for the national SCS. Many of these trials have developed their own rating system and
are testing punishment mechanisms for blacklisted citizens and companies. Rongsheng, one
of 12 selected ‘model cities’, has introduced a scoring-based system in which individuals are
18rated on a scale that goes up to 1000 points (Ohlberg, Ahmed and Lang 12; Creemers 19).
The scores are then categorised into six categories (AAA to D) with appurtenant
consequences. Shanghai has released a smartphone application called ‘Honest Shanghai’ (诚
信上海 / chéngxìn shànghǎi), in which users can register with their national ID number and
facial recognition (Ohlberg, Ahmed and Lang 12; Creemers 18). The application then
computes a score based on government documents and rates the citizen with one of three
categories: very good, good, and bad. The app also shows the ratings of local companies on
a map marked with green, yellow, and red smileys.
Image 2: In-store certificate of Honest Shanghai quality test.
The People’s Bank of China also encouraged eight tech companies in 2015 to develop
commercial pilots for the national SCS and to stimulate the credit economy and increase
financial inclusion. Citizens that traditionally lack credit records could benefit from such
credit systems as they gain financial opportunities from these commercial credit systems.
Among these pilots are Tencent, WeChat’s parent company, Baidu, and Alibaba’s subsidiary
Ant Financial that developed Sesame Credit. The central bank initially gave the companies
six months to develop a commercial credit-reporting system (Creemers 22).
According to the PBoC, however, these commercial trials were not a success; in 2017
the central bank refrained from granting official Social Credit System pilot licences to any of
the commercial enterprises. The main reason was a conflict of interests: the priorities of the
companies lay at developing a credit system that supported their own core business, such as
19e-commerce or insurance (Creemers 24). The vision of the People’s Bank, however, was to
establish a centralised credit reporting system. Due to the direct market competition of the
companies, they would not share their proprietary data, creating a fragmented credit
economy (Ohlberg, Ahmed and Lang 12). This also led to complains on behalf of the central
bank about the ability to indicate accurate financial credibility of the systems. Although the
eight companies included some of the largest data-owners in China, their data generation is
restricted to their relative userbases and business areas (Creemers 24-25). Other criticisms
were that the companies did not protect user privacy (Ohlberg, Ahmed and Lang 12) and
that due to their commercial interests, the companies could not inhabit an independent
third party-position (Reuters Staff).
In an effort to solve these problems, the National Internet Finance Association (NIFA)
founded a credit scoring bureau called Baihang together with the eight companies. The NIFA
is a government organisation originally initiated by, and still under the administrative
control of, the People’s Bank of China (Creemers 25). The government body owns the
majority of equity stakes with a percentage of 36%, while the rest of the shares are equally
distributed under the eight companies. As a semi-commercial credit scoring bureau, Baihang
complements the central bank’s Credit Reference Centre, which is its main governmental
credit authority. The credit union received a three-year licence from the People’s Bank of
China, which allows the central bank to keep a close watch on the commercial credit system
pilots (Creemers 25). It is however uncertain how this partnership is going to unfold in the
coming years. If the cooperation lasts, the government might be able to unify the data from
the eight companies to support the national SCS. The cooperation might however also break
due to the competing commercial interests of the private companies.
The future relationship between the national Social Credit System and Sesame Credit
thus remains ambiguous, but it is certainly an important development to follow as the
implementation of the SCS advances. One major factor in this relationship will be the
commercial interests of Ant Financial. These are currently for the most part in line with the
goals of the government, but this could change if the Baihang collaboration fails. The next
section elaborates on these interests of Ant Financial and its superior Alibaba.
201.3 Alibaba’s business ecosystem
As the previous section has shown, Sesame Credit is connected to the political context of
the Social Credit System. But although the Chinese government had originally initiated the
development of commercial social credit pilots, Sesame Credit remains a consumer credit
system owned by the private enterprises Alibaba and Ant.
This section analyses the commercial and organisational (infra)structures of Sesame
Credit within Alibaba’s ‘business ecosystem’ (Moore), and in the larger ‘ecosystem’ of
connective media in China (Van Dijck, Culture). To do so, I use three concepts from Manuel
Castells’ theory of political economy: ownership, business model, and governance.
“Proponents of political economy”, remarks José van Dijck, “[…] regard platforms and digital
networks as manifestations of power relationships between institutional producers and
individual consumers.” (27) It is precisely the commercial and institutional power relations
that this section aims to address.
Ownership: Alibaba’s e-business ecosystem
Ownership is perhaps the most crucial aspect in defining institutional positions of power. In
this light, it no surprise that the government is cautious in granting credit scoring licences to
any one of the eight companies. In the Baihang joint credit bureau, the PBoC, via the NIFA,
retains its position of power as it has the highest equity stakes in the enterprise. In this way,
the central bank also maintains its control over the commercial initiatives.
In Sesame Credit, the most significant stakeholders are Ant Financial and, by
extension, the Alibaba Group. Ant Financial started as the payment platform Alipay to
manage online payment on the Alibaba.com e-commerce website. In 2015 Alipay changed
its name to Ant Financial as it began offering multiple financial services besides the payment
platform. Ant Financial now also incorporates Ant Fortune, a wealth managing service;
MYbank, a private online bank for micro-enterprises; and the credit scoring system Zhima
Credit (Ant Financial). As such, Ant Financial grew out to be the financial arm of the Alibaba
Group. Although Ant Financial largely operates as an autonomous enterprise, Alibaba has a
33% equity stake in the company (Alibaba Group and Ant Financial).
In fact, Alibaba itself, which started as a business-to-business (B2B) trading website
in 1999, has grown into one of the largest Internet-based conglomerates in China. Over the
last two decades, the corporation has expanded its commercial imperium through takeovers
21and partnerships. It founded the popular business-to-consumer (B2C) e-commerce website
Taobao.com, partnered up with Yahoo! and acquired the Yahoo! China web portal, and
founded the cloud computing company Alibaba Cloud (Huang, Hu and Lu 29). Today, the
Alibaba Group leads an expanding interconnected network of companies that operate
interdependently for mutual benefit. Jia and Winseck also note, that the Chinese
government also has a direct influence in Alibaba as Alibaba has one government official in
its board of directors (46). As the board decides on the management of the company,
government goals can be implemented in the business strategies of Alibaba.
Based on James F. Moore’s conception of the ‘business ecosystem’, Huang et al.
argue that Alibaba has evolved into an ‘e-business ecosystem’. They define the term as “an
organic ecosystem that is made of enterprises and organizations with close relations, using
the internet as a platform to make competition and communication through virtual alliance,
sharing resources, and making full use of their advantages beyond geographic limits” (27).
By clustering multiple companies together and covering multiple sections of the market,
Alibaba manages to compete with other e-business ecosystems such as eBay, which
withdrew from the Chinese market in 2006, and Tencent.
Much of the internal structure of Sesame Credit depends on the relationship with
other platform companies. Ownership, bonds, and competition play important roles in how
it works as a system of production (Van Dijck, Culture 36). On the back-end of the credit
scoring system, this means that user data that Sesame Credit uses to compute scores are
derived from Alibaba-owned or related enterprises. The score that is produced is thus based
on behaviour on e-commerce websites, interaction on social media, and credit history (etc.).
On the other side, it also means that Sesame Credit is applicable to many other
companies and their services. Hight Sesame scores can for example waiver deposits on the
bike-rental platform Ofo (Yu). There are even other platforms that incorporate the Sesame
score into their service. The P2P services platform Daowei (到位 / dàowèi) allows only users
with a score higher than 650 to offer services on the platform while requesting a service
requires a minimum score of 600 (Schoenmakers). Baihe (百合 / bǎihé), an online dating
platform, also encouraged users to show their Sesame score (Creemers 23).
In short, looking at ownership shows that the Alibaba Group, as a ‘leading species’
(Moore) in the e-business ecosystem occupies a central position of power. Still, the
22companies coevolve with each other in a complex, relatively decentralised, manner driven
by mutual interests. The distribution of data and the applicability of Sesame Credit is built
on this institutional infrastructure. Sesame Credit, as a system of production, relies on, and
supports, other companies in the Alibaba e-business ecosystem.
Business model: ‘Meet, work, and live at Alibaba’
Alibaba’s vision is to create an online space for consumers and businesses to ‘meet, work,
and live at Alibaba’ (Alibaba Group). It provides a platform for users, consumers, merchants,
and businesses to interact socially and commercially. Small businesses can use the digital
infrastructure of the Alibaba Group to connect with other companies in the ecosystem and
gain a larger userbase. One example of this is Alibaba’s digitisation of traditional retail under
the name of ‘New Retail’8. Small retail businesses can easily join the ecosystem and tap into
its infrastructure by conducting part of their business via Alibaba-connected platforms, such
as sales via Taobao and payments via Alipay. Also due to Jack Ma’s guru-like status in the e-
business economy, many companies gladly follow his ideas for future developments.
Thirdly, the Alibaba Group website explains that the company strives to make Alibaba
products and services central to the everyday lives of their customers.
It is clear from this vision that Alibaba aims to bind companies and consumers to
their ecosystem and to increase user experience. These two components, gaining ecosystem
members and enhancing user experience, go hand-in-hand as they amplify network effects.
For example, the user experience improves as more businesses are connected to the Alipay
platform. Conversely, the more people use Alipay; the more businesses join the platform. As
such, platform companies have a natural tendency towards monopolisation (Srnicek 45).
Similarly, Sesame Credit also enhances the network effects of the Alibaba
ecosystem. On the one hand, it encourages users to interact with Alibaba-affiliated
platforms such as Taobao or Tmall, as this increases Sesame scores and grants them
privileges. In fact, many of these rewards also encourage users to purchase more goods and
services that are related to Alibaba. This way, Sesame Credit creates behavioural feedback
loops that steer users towards staying within the ecosystem. This increased user traffic
8
For an explanatory video on ‘New Retail’: https://www.youtube.com/watch?v=336YkwayCD4
23attracts businesses to connect to Sesame and offer their products and services via the credit
platform and join Alibaba’s ecosystem.
Jie Guo and Harry Bouwman take an ecosystem view on the Alipay’s mobile payment
platform (of which Sesame Credit is part) and focus on the dependency of actors in the
ecosystem. They show that merchants highly depend on the m-payment platform as it
provides, among other things, an increased customer base, cheap transaction management,
and an IT infrastructure (69; table III). Many merchants and businesses do not have the
resources to develop their own mobile payment infrastructure or cannot compete with
payment platforms giants such as Alipay. As such, for many single merchants, it is better to
join the platform than to compete against it. Conversely, Alipay also depends on merchants
as they provide their products. This dependency is, however, less fundamental as Alipay’s
platform relies on a multitude of suppliers.
Within the Alipay ecosystem, Sesame Credit enhances trust in the commercial
relationship between consumers and companies in the Alibaba ecosystem and, as such,
further amplifies network effects. Indeed, Guo and Bouwman note that risk management is
another element of merchant dependency on Alipay (69; table III). As a financial credit
indicator, Sesame Credit reflects the customer’s ability to pay back borrowed money or
products/services paid on credit. And, not unimportantly, it also reflects their loyalty to the
Alibaba ecosystem.
This way, Sesame Credit is a mechanism that binds consumers and companies to the
Alibaba e-commerce ecosystem and strengthens their relationship. By providing a credit and
payment infrastructure between consumers and companies, Sesame Credit and Alipay
position themselves strategically between two interacting parties. This makes both
companies and consumers dependent on their financial credit infrastructure. Furthermore,
it allows platforms to extract data from this interaction and for the improvement of their
own services – such as a more accurate Sesame score – and other services in the ecosystem
of Alibaba.
Governance: Sesame Credit as a technology of power
José van Dijck notes that “[t]o analyze the governance structure […], one needs to
understand how, through what mechanisms, communication and data traffic are managed.”
(Culture 38; emphasis in original) She points to contractual mechanisms such as the end-
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