Results and discussion

A first takeaway of our analysis is the surprising extent of commercial SCSs coverage within our respondent population. Fully four out of five respondents (80%) are using at least one commercial SCS, while only 7% were aware of being part of a local government-run SCS pilot, as illustrated in Figure 2. A total of 43% of the respondents lived in one of the 42 localities where local governments started a government-run SCS; of these, only 11% were aware of being part of a local government pilot. This suggests that government-run SCSs are not yet as advanced in scope or progress as often portrayed by a few showcase examples. Among commercial SCSs, Sesame Credit is the most popular system with 58% of respondents reporting membership, followed by Tencent Credit (31%), respectively, while some respondents use both systems (19%).9 In all, 16% of all respondents do not take part in any SCS.10 A relatively small number of people (8%) indicated not knowing whether they were taking part in a SCS or not.

Table 2 below summarizes the variation in levels of approval among different groups within our respondent population. Interestingly, approval of SCSs is highest among respondents who are part of a local government SCS pilot, with 64% of these respondents indicating strong approval of SCSs in general (category 1). By contrast, approval of SCSs is lower among respondents who are part of a commercial SCS pilot, with 55% strongly approving of SCSs. This discrepancy could be explained by perceptions, noted in previous research that citizens deem the government as a more trustworthy handler of personal data and generally are more favorably disposed to government SCSs than commercial ones ( Ohlberg et al., 2017 ; Wang and Yu, 2015 ).

Overall, respondents report a high degree of approval of SCSs, with 80% of respondents either somewhat approving or strongly approving SCSs. Only 19% of respondents perceive the SCS in value neutral terms (neither disapprove nor approve) while just 1% reported either strong or somewhat disapproval. To some extent, the high degree of approval of SCSs and the almost non-existent disapproval we found might reflect the nature of conducting a survey in an authoritarian setting—while respondents were clearly informed that that the data were anonymized 11 and to be used for research purposes only, some more cautious respondents may have falsified their preferences to a degree due to concerns about expressions of disapproval resulting in reprisals from the state. 12 Yet, we are confident that such an effect would be marginal not least since half of respondents (49%) indicated strong approval of SCSs, suggesting that overall public support is quite robust. Lending support for this view is the fact that only 1% of respondents expressed the view that a nationwide SCS should not be implemented. Our semi-structured interviews with citizens of various ages further confirmed these high approval levels. That said, the significant number of value-neutral respondents (neither approve nor disapprove) might suggest the existence of a group of ‘doubters’—1 in 5 Chinese—who maintain a circumspect attitude about SCSs.

The characteristics of the “doubters,” that is, the 20% of respondents who either strongly or somewhat disapprove or neither disapprove nor approve can be summarized as follows: They are younger, have a very low income, are slightly more likely to be female, have less education, and are more likely to live in rural areas ( Table 3 ).

Degree of approval varies across age, income, gender, education, and region, as illustrated in the different graphics in Figure 4 . The 51–65 age group shows the strongest approval levels, with 56% of respondents strongly approving of SCSs. SCS approval is also higher among respondents with a higher income. Attitudes among male and female respondents are similar, with male respondents being slightly more positive. Approval is highest in the group of respondents with the highest education and lowest in the group with low education levels. Approval levels are higher in cities than in rural areas (82% vs 68%). There are no significant differences in approval between regions: respondents in West China have a slightly more positive attitude (81% approve or strongly approve), followed by East China (80%), and Central China (79%). Overall, some of these findings are surprising, as current research by Pan and Xu (2018) suggest that in China the young, better-educated, coastal urban residents lean toward liberal views, and there is an expectation that liberals would be more skeptical of SCSs. While we find that younger respondents are indeed less approving of SCSs than the older respondents, somewhat surprisingly the better-educated and wealthier respondents are more approving of SCSs. The discussion section will analyze this apparent tension in greater detail by arguing that urban residents in China receive a wider range of benefits from SCSs and see SCSs through particularly positive frames.

Our analysis further shows that SCS approval is higher among respondents who receive actual benefits with significant ordered log-odds estimates of 0.811 (confirming Hypothesis 17). We find a non-significant negative effect of received disadvantages (e.g. difficulties obtaining a credit, restrictions from public transport, or limited access to sharing economy services), a disconfirmation of Hypothesis 18. The ordered log-odds estimates become very high and statistically significant for all three function variables: The ordered log-odds estimates are 0.775 for the believed function that “SCSs are a useful tool to make individuals and companies more honest and accountable for their actions” (confirming Hypothesis 19), 1.732 for the believed function that “SCSs are useful to ensure that companies abide by regulations” (confirming Hypothesis 20), and 0.766 for the believed function that “SCSs improves the quality of life” (confirming Hypothesis 21).

We find a non-significant negative effect for time spent on the smartphone (a disconfirmation of Hypothesis 7) and a low but significantly positive effect for frequency of online posting in social media (confirming Hypothesis 8). The ordered log-odds for CCP membership and confidence in the Chinese government are slightly low (0.314 and 0.589, respectively) but statistically significant (confirming Hypotheses 9 and 10). Model 4 finds that no significant effect linking approval and respondents scores in an SCS (disconfirming Hypothesis 11). However, if users believe they have a slightly higher score than their friends and families, the ordered log-odds estimates are high and significant, confirming Hypothesis 12. In addition, the findings show that (believed) knowledge about how social credit scores are calculated is a significant predictor of SCS approval (confirming Hypothesis 13) while receiving information on SCSs has a non-significant effect on approval (disconfirming Hypothesis 14). Actively joining an SCS instead of being automatically integrated is negatively correlated with SCS approval, but the findings here are not significant (disconfirming Hypothesis 15). A very powerful predictor of SCS approval is fairness of personal social credit scores—here, the ordered log-odds estimate is 1.241 with very high significance (confirming Hypothesis 16).

The results of our regression on socio-demographics are somewhat surprising ( Table 4 , model 1). Age, income, gender, education (except for low education), and (urban or rural) location have a statistically significant and positive effect. In other words, respondents who approve of SCSs tend to be older, higher-income, male, more highly educated, and living in an urban area. Among these significant socio-demographic factors, the effect of education is highest, followed by income and urban/rural location. We further find no statistically significant effect of region, which implies that there are no regional effects influencing individual’s opinion of SCSs in our sample. In other words, the following hypotheses are not supported by this analysis: Hypothesis 1 (higher SCS approval among younger citizens), Hypothesis 2 (higher approval among citizens with lower income), Hypothesis 4 (higher approval among less educated citizens), and Hypothesis 6 (higher approval in Eastern China). The findings only support Hypothesis 3 (higher approval by male citizens) and Hypothesis 5 (higher approval in urban areas).

In order to measure the explanatory power of different independent factors, we undertake several logit regressions in Table 4 . First, we measure socio-demographic characteristics in addition to online habits (model 1 and 2). Second, we run a regression that includes the effects of political attitudes (model 3), the magnitude of SCS score (model 4), different characteristics of SCSs including available information, modes of joining a SCS and the perceived fairness of SCSs (model 5), effects of received (dis)advantages (model 6), and effects on perceived functions (model 7). 13

Discussion

Overall, the most interesting outcome might be the unexpected findings with regard to individual characteristics and beliefs. Following the findings of Pan and Xu (2018), we expected to find that younger, well-off, better-educated respondents would be less likely to support SCSs due to concerns of infringement on privacy rights and political freedom—after all, ensuring protection of the private sphere from government encroachment is a mainstay of liberal thought going back to John Locke. While younger respondents in our sample do seem to be relatively more circumspect about SCSs, older “elites” (better-educated and wealthier) are overwhelmingly positive about SCSs.

The findings further show that there is less approval for SCSs in rural areas. One potential explanation is that respondents in rural areas are less familiar with SCSs and hence more skeptical. About 43% of respondents in rural areas reported not knowing how their social credit score is calculated, while this was the case for 36% of respondents in urban areas. Another and perhaps more satisfying explanation could be that respondents in rural areas might not have had equal access to the benefits and services offered by SCSs.14 A total of 87% of rural citizens and 88% of urban citizens received some type of benefit or advantage as a result of joining a commercial pilot. However, Table 5 shows that urban respondents received a wider range of benefits. For example, 37% of commercial pilot users in urban cities had obtained a credit without difficulty, while this ratio was only 31% for rural citizens. The SCSs’ benefits schemes also have a strong urban bias as sharing economy services and travel-related incentives might be less relevant for rural citizens. For instance, using deposit-free rental bikes or cars as a benefit might be less applicable to rural areas with lower population density; in urban areas, 40% of users reported using this as a benefit, while in rural areas the figure dropped to 32%. Moreover, 14% of urban residents had received a fast-tracked visa, while this was the case for only 11% of rural residents, presumably because urban residents travel abroad more frequently than rural residents.

Table 5. Commercial SCSs—advantages and location (N = 1549, weighted).

About 29% of respondents in rural areas also reported to have received some type of disadvantage as a result of their participation in an SCS, while fewer urban respondents reported having received disadvantages (25%). For instance, 5% of rural respondents reported difficulties in obtaining credit because of their social credit score, whereas this was only the case for 2% of respondents in urban areas. A few interviewees also perceived benefits from commercial SCSs as biased against rural citizens as they “do not have as good use for benefits (as compared to city residents) and most importantly, because they are limited by income and other factors to increase their score” (Interview 4, June 2018).

The results are also interesting with regards to particular characteristics of SCSs (category 2). While the actual magnitude of respondents’ scores is not a significant predictor for SCS approval, what does matter is whether or not respondents believe that they have a slightly higher score than their family members and friends (60% of all respondents). Moreover, the perceived fairness of social credit scoring plays an important role in public support for SCSs. In interviews, concerns about the unfairness of scoring methods were repeatedly raised, ranging from difficulties in credit repair to scores being too homogenized (Interview 8, June 2018; Interview 9, July 2018). One interviewee, for instance, noted that “personal difficulties, debt accumulation because of sickness, and other family reasons can result in a low social credit score, and one should not judge someone based on their low score, it is simply unfair” (Interview 8, June 2018). Others raised concerns that the scoring system might not apply for all as “people in powerful positions of responsibility might escape punishments, which is unfair” (Interview 10, July 2018).

Finally, with regards to perceived functions of SCSs (category 3), the survey findings suggest that citizens perceive SCSs not as an instrument of “surveillance” but instead as an instrument to improve “quality of life” and to close “institutional and regulatory gaps” leading to more honest and law-abiding behavior in society. SCSs are viewed within the context of technological progress and are understood as a means of improving life quality. The various benefits provided via SCSs are seen as very convenient and attractive. For instance, one interviewee reports that “sometimes there is not enough money left in Alipay and Sesame Credit can be used for ordering delivery food. This is convenient and increases life quality” (Interview 5, June 2018).

Our finding that respondents associated SCSs with the functions “improve accountability and honesty” and “abide by regulations” suggests that SCSs are also perceived as useful tools that help to increase trust in society and close particular institutional and regulatory gaps. One such institutional gap is the underdeveloped financial credit rating system which has made it very difficult for households to access credit (Interview 2, March 2018; Pang, 2017). Commercial SCSs such as Sesame Credit are seen as valuable because they offer their own banking services with attractive interest rates for loans and saving accounts for their users. In addition, SCSs are seen to address regulatory enforcement issues ranging from food safety and non-compliance with environmental regulations to rising Internet scams. For instance, in a context of frequent food safety scandals, government SCS pilots such as the Honest Shanghai app offer users additional “reliable” information to check whether restaurants are “trustworthy” and abide by food safety regulations.

Overall, the perceived function of SCSs to resolve regulatory enforcement issues is tightly linked with citizens’ perceived “lack of trust within society.” In all, 76% of the respondents in our survey stated that they believe that there is an issue of mutual mistrust between citizens in China’s society. One interviewee also stressed that there was a need to “generate a guide and norm for personal social behavior with the Chinese society. It could improve the efficiency of social operations” (Interview 9, July 2018). Another interviewee explains as follows:

SCS can create trust in society through feedback mechanisms. People with bad credit will be less likely to be employed and it will not be easy for them to access more funds in the future. Such punishments provide feedback to people with bad behavior to restrain themselves. Step by step, SCSs will create trust in society. (Interview 5, June 2018)

Another interviewee notes the following:

Take, for instance, the example of using shared bikes. If someone does not lock a shared bike after using it properly, her or his own credit will be influenced. Alipay can collect such very detailed information from different aspects in life and include this in a score. Through such detailed accounting, SCSs can track individuals’ actions and create trust in society. (Interview 10, June 2018)

Our findings plausibly also reflect China’s authoritarian political context in which the survey was conducted. Interviewees were conceivably less concerned that SCSs provide data for surveillance and social control purposes since many would assume that the Chinese security apparatus is able to access to any such information already (e.g. Interview 6, June 2018; Interview 7, June 2018; and Interview 8, June 2018). One interviewee summarizes this view as follows:

All data is accessible to the CCP already. For instance, during the registration for primary school, people must provide detailed family information. So, I do not think that there is any point in worrying about the Party having access to data through the SCS, because it is inevitable that all data is accessible to the CCP. (Interview 7, June 2018)

Another interviewee notes that “the collection of private personal data depends on the consciousness and governance of the Party. If personal data is used for good reasons, I think it is acceptable” (Interview 8, June 2018.) The positive views among the respondents are no doubt also influenced by how Chinese media report on SCSs and how little room it has for critical debates on surveillance and privacy issues.

A limitation of our study is that the query about approval of SCS in general (社会信用体系shehui xinyong tixi) did not differentiate between governmental pilots or commercial systems. In the future, such research could differentiate between the two system since their aims and operation are quite distinct. Yet, other queries in our survey suggest that citizens do differentiate between government-run and privately run SCSs. Figure 5 shows that 59% of respondents believe that the central government should be responsible for management of a nationwide SCS, while just 9% believe that local governments should take the lead.15 These results echo previous finding of “hierarchical trust” meaning that Chinese citizens tend to have high degrees of trust in the central level and much less trust in local authorities (Li, 2016; Tang, 2016). Our findings also suggest a degree of skepticism about the motives of private players involved in SCS schemes. Only 17% of respondents believe that the government should work jointly with private enterprises and less than 2% believe private companies should manage a nationwide SCS. Figure 6 shows that respondents believe that personal data are used most responsibly by the central government (77%), followed by the provincial government (48%), the municipal government (42%), state-owned companies (24%), foreign enterprises (13%), and private enterprises (8%). Responses to these two questions would suggest that approval levels are likely to be higher for government pilots than commercial systems.