Survey research paper Internet effects in China

INTERNET USE, SOCIAL INTERACTION AND CITIZEN ENGAGEMENT IN CHINA 1

Socially Embedded Pro-Civic Effect of the Internet: Differential Civic Influences of Internet Use and Citizens’ Everyday Interactions in China

Abstract This study seeks to investigate the civic effect of the Internet as integrated in the context of citizens’ offline everyday interactions. The data analyzed came from a nationwide sample survey conducted in 2010 in China (n = 37,279). Utilizing the methods of propensity score weighting and matching, it was able to adjust for the self-selection bias in Internet use, and estimate the causal effect of Internet use on citizen engagement with robustness. This forms the basis for 1) examining how citizen engagement is fostered by Internet use in conjunction with the heterogeneity and intensity of one’s offline everyday interactions, and 2) exploring how the Internet effect is moderated by these interactional characteristics. The findings showed the parallel influences of Internet use, traditional media use, and the interactional characteristics in boosting citizen engagement in China, and the important roles of one’s interactional heterogeneity and intensity for aiding the Internet effects on civic expression and participation.


Citizens’ engagement in politics by various means keeps a polity lively at its bedrock. Even under the Communist regime in China, citizen engagement practices are no longer rare today. Digital technologies enable the Chinese people to carve out a space for expression and association despite state control (Meng, 2011; Yang, 2009). Its increasingly rights-conscious citizens mobilize their offline bonds and online resources to fight encroachment by political and market power (Jacobs, 2012; Read, 2003). According to Yang (2003), the Internet and society make a co-evolutionary process in which the growth of the Internet furnishes new civic possibilities, which in turn find favorable conditions in its society. In this study, we investigate a main crux of the process as manifested on the individual level, that is, how the effect of Internet use on citizen engagement is embedded in the patterns of its citizens’ offline social life.

The research on media effect and sociality harks back to Columbia School sociologists’ findings that mass media’s effect on political decisions is socially transmitted (Lazarsfeld, Gaudet & Berelson, 1965). More recently, scholars found similar dynamics working in the Internet effect on citizen engagement (Hardy & Scheufele, 2005; Nisbet & Scheufele, 2004; Shah, Cho, Eveland & Kwak, 2005; Shah et al., 2007). The civic influences of Internet use are shown to be moderated or mediated by people’s political discussion that helps them understand online information better and pay attention to politics. In Lake and Huckfeldt (1998)’s term, the regular exchange of political expertise and information among citizens yields the “politically relevant social capital” (p. 570), benefiting the learning of online information for civic purposes. However, this focus on political discussion may not be highly applicable to China, where as the government still keeps a tight grip on politics and information (Zhao, 2004), the most salient, officially sanctioned political topics are often not real concerns in people’s life and spontaneous political talks are still subject to pressure (Audin, 2016; McDonald, 2012).


Drawing upon prior research on social capital, social network and politics (Coleman, 1988; Katz, 2006; McAdam, 2003), we suggest moving the focus to people’s everyday interactions to consider how the civic influences of Internet use are socially embedded in China. Here, we argue that social interactions do not have to be politically relevant so as to be politically meaningful, and everyday interactions may also matter for channeling the civic influences of Internet use. It is based on the recognition of various pro-civic resources in social network – besides opportunities for political discussion, there are also social support, trust, and opportunities for mundane talks on private matters with potential public import. People’s everyday interactions, even when conducted for purely sociable pleasures, help maintain their web of ties and cultivate all these resources that reside in it, therefore likely to aid in the civic influences of online information and communication.

Our study explores how the civic effect of Internet use is embedded in one’s everyday offline interactions in China. Our data came from a national sample survey conducted in China in 2010 (n=37,279). First, we examined the causal influences of Internet use on civic engagement outcomes, evaluating the pro-civic implication of the Internet in China that some scholars argue for (Zheng & Wu, 2005). Then, based on an extension of Scheufele’s (2002) model on media’s differential civic benefits as moderated by political discussion, we zoomed in on whether one’s everyday interactions (interactional intensity and heterogeneity) may exert some similar “value-added” moderation effects on the Internet impacts. A solid answer to this question requires a robust estimation of the Internet effects first of all. Thus, we applied the propensity score (PS) methods (Rosenbaum & Rubin, 1983), using the rich background information of respondents in our survey to achieve their propensity scores of Internet use, and conducting PS weighting and matching to adjust for the self-selection bias in our Internet effects estimation,1 which facilitate a precise understanding of the Internet effects and their interplay with sociality.


Our study is meaningful for understanding the linkages of the Internet, sociality and civic engagement in transitional societies like China. But its perspective on the intersection of Internet effect and everyday interactions is also relevant to explaining the dynamics of citizenship in democratic societies, where citizens, especially the younger generations, go toward communities and peer groups to define politics by identities and life styles (Bennett, 2008). For these citizens, the context of everyday interactions may also be a stage for developing their public connection that enables citizenship as exercised by them (Couldry & Langer, 2005; Eliasoph, 2000).

Citizen engagement, Internet use and social interaction Citizen engagement is a multi-layered phenomenon, including the involvement in formal political procedures, as well as various other efforts of participation, expression, and information learning around public or community affairs (Johnson, Hays & Hays, 1999; Verba & Nie, 1972). In many democratic countries, symptoms of disengagement have been noted in the decline of voter turnout, political trust, and interest in public affairs (Norris, 1999; Newspaper Association of America, 2007). Yet on the other hand, digital technologies serve to foster the loose networks of expression and community action outside of formal organizations (Bennett, 2008; Bimber, 2011). Propelled by individuals’ awareness of rights, identity and lifestyle, politics has extended to involve many activities in private social life (Schudson, 1998).

Meanwhile, in a transitional society like China, the authoritarian rule allows little space for people’s involvement in policy making. Some scholars noted a wide spread of disinterest in government affairs, even its wrongdoings (Hu & Liu, 2004; Liang, 2008). But for other scholars, an incipient civil society has actually started to take shape, amidst all the outcries and activism efforts by common people in their neighborhoods, villages and online communities (Jacobs, 2012; Read, 2003; Yang, 2003). Its netizens in particular show profound creativity in negotiating


propaganda and censorship by alternative frames and sarcasm in online discourse (Meng, 2011; Zhou & Moy, 2007). If we say citizens in many democratic countries are turning away from formal politics due to dissatisfaction and cynicism about the systems (Norris, 1999), then citizens in China have to create civic opportunities outside of formal politics under systemic constraints (Pei, 1998, 2000). But despite the difference, the dynamics of citizenship in both socio-political contexts show much vitality in the realm of citizens’ personal relationships, where they make efforts to build or restore the sense that their engagement can make a difference.

Lying behind the dynamics of citizenship, the influence of the Internet is undeniable. In Polat’s (2005) apt summary, the Internet is often considered able to benefit citizen engagement through the channels of information and communication. It brings cheaper and more convenient access to online information that is pluralistic in content producers and their perspectives, and it induces the rise of highly interactive decentralized communication network, which is relatively free from power constraints (Castells, 2007). Such affordances may allow the users to benefit in political understanding, especially from ordinary citizens’ points of view, and facilitate the civic expression and participation online and offline. Research in North America, Europe and China has found the evidence that Internet use indeed has positive impacts on civic engagement, even under the stringent political control in China (Boulianne, 2009; Kroh & Neiss, 2009; Lei, 2011).

Yet to recognize the pro-civic effects is not to take a technologically deterministic view that the Internet alone drives the dynamics of citizen engagement. When Katz (1991) reflected on the “two-step flow of communication” model, he stressed that mass media effects are “limited except insofar as they are subjects of conversations”, as “citizens are sociable members of an interpretive community, critically interacting with the media and each other” (p. 81). So it is, in this day and age, for the Internet impacts on civic engagement. Both Boulianne’s (2009) meta-


analysis and Kroh et al.’s (2009) longitudinal study show that the causal effects of Internet use on civic engagement are positive but small in size. A detailed look at the particular cases where the Internet played salient mobilizing roles, from the Step It Up day of action in America (Fisher & Boekkooi, 2010) to the 2011 Egyptian Revolution (Lim, 2012), demonstrates that citizens’ connections with family, friends and church members featured importantly in mobilizing participation and channeling the Internet influences. To better account for citizen engagement, therefore, it is necessary to consider the context of citizens’ social ties and interactions and its interplay with the Internet. This can be particularly important when so much meaningful civic endeavor occurs, as aforementioned, in the realm of personal relationships nowadays.

Social capital literature highlights various civic resources embedded in social networks, such as the norms of reciprocity and trust embedded in dense communal networks (Putnam, 2000), social support (Coleman, 1988), and the flow of information and ideas via weak ties (Granovetter, 1973). Lake and Huckfeldt (1998) concentrated on “politically relevant social capital” as generated in political discussion, which fosters citizen engagement by contributing to the social transmission of political information. To establish the connection between sociality and the Internet effects on civic engagement, political communication scholars have fruitfully focused on the interplay of online information and political discussion. Nisbet et al. (2004) and Hardy et al. (2005) revealed the differential civic influences of online information usage as moderated by the frequency of political talks. Their model maintains that political talks help people clarify what they already know, and the anticipation of having political talks with others can also cognitively motivate them to make sense of information. In both ways, political talks compensate for the limitations of online information usage, such as the lack of attention or the ambiguity of information itself, thus reinforcing the civic benefits of online information (Scheufele, 2002). Meanwhile, Shah, Cho,


Eveland and Kwak (2005) and Shah et al. (2007) took the communication mediation perspective. Their model stresses that the civic benefits of online information are partly mediated by political discussion. Exposure to online information, especially Internet news, encourages political talks with family and friends, which in turn mobilize citizen engagement practices.

Given the complex links among media, sociality and politics, it is reasonable that most studies can only show a part of it. In this sense, both the above two models are plausible accounts of the mechanism involving Internet use, political discussion and civic engagement. Yet, some further consideration is still needed to better explain the dynamics of citizenship today. When, in both democratic countries and transitional societies like China, a big part of citizenship is being exercised in the realm of personal relations, sometimes concerning issues to be recognized for political significance, can the activity of political discussion capture all politically meaningful talks in one’s dynamic social life? Besides the “politically relevant social capital” generated in political talks, are the other network resources, such as social support and trust, also conducive to civic engagement and to Internet use for civic purposes? When many citizens strive for the issues within their immediate social milieu and thus rely less on being informed by media, is it enough to focus on the interplay of social interaction and online information? To answer these questions requires a further dialogue between the above-mentioned research on Internet use, sociality and citizen engagement, and the literature on social network and social capital. It is our hope to make a further exploration and, as explicated below, we take an interest in citizens’ everyday offline interactions as a meaningful context for civic engagement and the Internet’s civic influences. Everyday interactions and the significance for citizen engagement

What we call “everyday interactions” refers to one’s day-to-day offline interactions with various ties, from family and friends to acquaintances in communities or business occasions.


Compared with political talks, it is not emphatic on the substance of interactions about politics, but rather on people’s routine exchange of verbal and non-verbal acts and its indispensability to the formulation and maintenance of social ties. It contains the opportunities for political talks, but oftentimes it is unrelated or not explicitly related to politics. We argue that the significance of everyday interactions to civic engagement must not be ignored.

“Conversation is the crucible of public opinion” (Katz, 1991, p. 84). Even the mundane talks that are not explicitly related to politics can still be politically meaningful inquiries. Katz and his colleagues found political talks and personal conversations are not absolutely separated, but form a continuum where many political issues are talked about in their personal dimensions (Waytt, Katz & Kim, 2000). Such personal talks can help people associate political issues with their everyday world. Through such talks, even those less concerned about politics may monitor some piecemeal information and obtain an awareness of political climate (Schudson, 2000). Moreover, in mundane talks, people can discuss private matters that have potential public import, but are not yet recognized on public agenda. It is often through such private, seemingly trivial everyday talks that people who keep their distance from politics due to exclusion or inefficacy can build capacity, construct identity, and finally articulate agenda. In Papacharissi’s (2010) apt words, such private exchanges help prepare private citizens for the public.

Then, what about the sociable interactions that are not conducted for inquiry of any kind? Schudson (1997) once defined an ideal-type of “sociable conversation” that is spontaneous, non-utilitarian, and only serve the purpose of social pleasure. We maintain that even such interactions can have civic implications by maintaining and expanding social ties where various pro-civic resources reside. Let’s first look at the opportunities for meaningful political communication. Huckfeldt and Sprague (1995) stressed that political talks often occur among people whose


contact is regular and recurring. They found that, among those having close interactions, such as spouses or relatives, it is more likely for political discussion to be a two-way street and have larger influences on political decisions, and that the differences in education, religion and gender between two persons are positively related to the diversity of opinions they are exposed to in their talks. These findings tell us that, while we recognize the civic benefits of the frequency and heterogeneity of political talks by transmitting information and cross-influences (McClurg, 2003; Mutz, 2002), such characteristics of political talks are not to be taken for granted. It is due to the recurrence and diversity of interactions in day-to-day occasions that our network of ties actually gets cultivated, broadened, and thus configured to enable meaningful political communication.

Sociable interactions are also indispensable for sustaining our social ties to enable other pro-civic resources such as social support and trust (Coleman, 1988). Katz (1957) stressed that personal influence matters, because opinion leaders are “trustworthy” members of our everyday associates (p. 72). Such trust must be established through at least regular social contacts. Also, research shows that the trust and social support from strong ties maintained by long-term close interactions are particularly important for incentivizing engagement. For potential participants in social movements (McAdam, 2003) or voluntary associations (Bekkers, 2004), the mutual trust with some strong ties who are already involved can help establish the linkage between people’s identity and the concerned activity, therefore encouraging successful recruitment, and the social support from such ties can help sustain their participation. The resources of trust and support, again, must be nurtured through day-to-day interactions, including those for sociable pleasures.

In sum, everyday interactions also have the civic significance. To recognize this is not to deny the crucial contribution of explicit political discussion to engaged citizenship, but rather to stress that like Antaeus, citizens must touch the “earth” of day-to-day interactions to gain civic


strength. When many vibrant civic engagement practices occur in the realm of personal relations today, this perspective is meaningful for capturing various civic resources in the setting of people’s sociality, which are not limited to political discussion. Based on the above explanation, we may focus on two aspects of everyday interactions: interactional intensity that helps sustain communication channels and furnish social support and trust, and interactional heterogeneity that helps broaden the range of ties, facilitating the flow of information and ideas. The civic influences of Internet use as moderated by general social interactions

As an extension of Scheufele’s (2002) Differential Influences Model, we further postulate that depending on the levels of interactional intensity and heterogeneity, everyday interactions may exert “value-added” moderation effects on the Internet’s civic influences, and such effects may apply for civic benefits of online information as well as online communication.

Lim (2012) noted that in the 2011 Egyptian revolution, online mobilization information was also circulated by word-of-mouth in families, friendship circles and mosques. Coleman and Blumler’s (2009) analysis of iCAN, an online database on civic issues in U.K., shows that some users process online information by making contacts with old friends and work colleagues with similar interests. In these cases, online information could have been less meaningful for informing and mobilizing citizens, if it were not for the recurring day-to-day contacts that keep their communication channels effective, and trust in social ties that facilitates interpersonal interpretation. Thus, we first postulate that the intensity of everyday interactions may reinforce the civic benefits of online information usage. Online information, which is often ambiguous or incomplete in itself, can be reiterated and interpreted through communication channels sustained by everyday interactions of at least reasonable intensity, and such socially transmitted messages can enjoy better credibility with the mutual trust developed in close interactions.


Secondly, a similar effect of reinforcing the civic benefits of online information might be exerted by the heterogeneity of everyday interactions. Katz and Lazarseld (1955) stressed that opinion leaders are often knowledgeable in a specific sphere and unlikely to be opinion leaders in other unrelated spheres. Given the enormous scope of online information, it is more important to emphasize the personal influences from people with different backgrounds and in various spheres. The heterogeneity of interactions across social strata allows for one’s exposure to a wide range of personal influences in political and mundane talks. The exposure to cross-influences may motivate people’s dynamic reflection (McLeod, et al., 1999), which help them make sense of online information and thus reinforce the civic influences of online information usage. Thirdly, civic benefits of online communication may also be moderated by heterogeneity and intensity of everyday interactions. When we recognize that decentralized online network affords the possibilities of civic expression and association relatively free from power constraints (Castells, 2007), it is reasonable to expect that when people are exposed to a wide range of social influences, the broader vision of social reality it cultivates may encourage them to explore and utilize civic engagement opportunities online. Another important pro-civic implication of online communication is the training of people’s capacity needed for civic engagement through joining online discussion (Wilhelm, 1998). With social support cultivated through close interactions, especially that from one’s strong ties, people can be further empowered to use the civic capacity for the more substantive and higher-stake participation activities online and offline. The Internet, citizenry and society in China Despite its rising economic clout, China’s prospect of democratization is unclear, which is partly related to the dualistic image of its citizenry. On the one hand, we see many cases of ordinary people connecting and fighting around various issues of citizen rights (Jacobs, 2012;


Read, 2003; Yang, 2003). On the other hand, a sense of political disinterest and inefficacy also prevails, due to political control and the cultural belief in the guardian role of government (Shi & Lu, 2010). Compared to people in other countries, the Chinese overall show a surprisingly low level of perception of government impact on their lives and weaker confidence in their ability to affect politics (Nathan & Shi, 1993). In Wang’s (2005) observation, they are still a before critical citizenry. In this context, to consider how the Internet and sociality may interrelate in their civic influences will help us better understand technological and sociological conditions for the further cultivation of active citizenship in China and the democratization potential it holds for its future.

China first connected to the Internet in 1987 by a computer scientist’s email, which read: “Cross the Great Wall. Reach the world.” Today, the Internet is considered to constitute a rising pluralistic information realm in the country (Zheng & Wu, 2005), and expected to enact a social revolution that reveals critical impulses of Chinese people (Yang, 2009). Using national survey data, Lei (2011) showed that Internet use indeed makes people more likely to participate in collection actions and more supportive of democratic norms. Yet there are also pessimistic views that the Internet becomes a hotbed of nationalism discouraging pluralistic expression, or turns into an online playground that induces political disengagement (Peters, 2002; Morozov, 2010).

Meanwhile, decades of urbanization and the implementation of one-child policy have brought forth a transformation of social network that traditional kinship networks are replaced increasingly with looser networks as characterized by less intimate, yet more heterogeneous interactions (Yang, 1996; Ren, 2013). Such changes in sociality also draw attention for their civic implications. Chen (2013) showed that controlling for demographics, media use, grievances and political interest, Chinese citizens with larger networks are more likely to participate in appeals or petitions. Shen, Wang, Guo and Guo (2010) made further connections between


Internet use and sociality, finding that heavy Internet users in China have larger social networks online, which serve as a key mediator for Internet use to benefit their civic expression.

Our study focuses on the interplay of the Internet and one’s offline everyday interactions in their civic influences. In China, explicit political talks, especially concerning the Communist leadership and regime, are still subject to political pressure; so people often have to recede to private occasions and express spontaneous political sentiments in small, even coded talks (Audin, 2016; McDonald, 2012). Also, given the low tolerance for people’s political involvement under the authoritarian rule, social support and trust from one’s personal ties can be especially important for mobilizing engagement (Palmer, Perkins & Xu, 2011; Xu, Perkins & Chow, 2011). These conditions highlight the importance of considering people’s everyday interactions with social ties as a meaningful context for civic engagement and Internet use for civic purposes.

Specifically, our study concerns the following hypotheses and research questions. First, in light of the optimism vs. pessimism debate on the Internet’s civic influences in China, we state

H1: Internet use (whether one uses the Internet or not) has positive influences on the levels of individual citizen engagement (i.e., knowledge on current affairs, opinion expression, and civic participation). Secondly, recognizing various pro-civic resources cultivated in people’s everyday

interactions and focusing on international intensity and heterogeneity, we state H2a: The intensity of one’s everyday interactions has positive influences on the levels of individual citizen engagement. H2b: The heterogeneity of one’s interacting others in everyday interactions has positive influences on the levels of individual citizen engagement.


Thirdly, considering the exploratory nature of our proposed model on the differential civic influences of Internet use as moderated by the characteristics of everyday interactions, we state the following research questions to zoom in on the interplay of the Internet and sociality.

RQ1a: Are the civic influences of Internet use moderated by the intensity and heterogeneity of one’s everyday interactions? RQ1b: How do the intensity and heterogeneity of one’s everyday interactions moderate the civic influences of Internet use? RQ2a: Among Internet users, are the civic influences of specific Internet usages moderated by the intensity and heterogeneity of their everyday interactions? RQ2b: Among Internet users, how do the intensity and heterogeneity of their everyday interactions moderate the civic influences of specific Internet usages?

Methods Our data come from a nationwide sample survey (n=37,279) conducted during 2010 in all

31 provincial-level administrative divisions of mainland China (4 municipalities, 22 provinces, and 5 autonomous regions), using multi-stage cluster sampling.2 Interviewers were recruited and trained in local areas to conduct face-to-face interviews in the needed dialects. The overall response rates were 62% for capital cities of all the divisions and 69% for other locales. Measures

Citizen engagement variables. Three sets of variables were constructed to represent the main aspects of citizen engagement in China: knowledge on current affairs, opinion expression, and civic participation. 3Knowledge on current affairs was measured by 12 questions, ranging from “Who is China’s President currently in office (Answer: Hu Jintao)” to “Which foreign


Internet company announced withdrawal from China in 2010 (Answer: Google)”. The correct answers were coded as 1; incorrect and “Don’t know” responses, 0. The sum was taken as an index for knowledge on current affairs (M=4.89, SD=3.10, α = .869).

Opinion expression was measured by asking how often (0=never, 5=very often) one expresses views on social issues by means of 1) filing complaints to government agencies, 2) filing petitions to businesses, 3) contacting a newspaper, radio or TV station, 4) talking with friends, 5) sending text messages, 6) blogging, and 7) leaving comments in online forums. The Cronbach’s alpha across them is α =.870. Due to low frequencies for most items, responses were recoded to 0 (never) and 1 (at least occasionally). Three variables of opinion expression were constructed. For all respondents, a sum across items 1-5 was used as the index of offline expression (M=1.47, SD=1.63, α =.833). For Internet users, a sum of items 6-7 was recoded to 0 (never) and 1 (at least occasionally) as the index of online expression (M=.57, SD=.50, r =.801, p<.001). For all respondents, the proportion of their expressive activities among all applicable activities (7 for Internet users; 5 for non-users) was used for the index of opinion expression in general, and the 0-1 scale was multiplied by 10 (M=2.98, SD=3.28) for the ease of interpretation.

Civic participation was measured by asking whether one has in the past 12 months joined in 15 categories of activities in voluntary associations, ranging from activities in homeowners’ committees to those in public interest advocacy groups. Among them, items 12-15 apply to Internet users (online fans’ clubs, discussion groups, and homeowners’ forums; and offline gatherings with members in such online groups). The Cronbach’s alpha across them is α =.592. Three variables of civic participation were constructed. For all respondents, a sum across items 1-11 was used as the index of offline participation (M=1.47, SD=1.67, α =.543). For Internet users, a sum of items 12-15 was recoded to 0 (never) and 1 (at least one activity) as the index of


online participation (M=.25, SD=.43, α =.715). For all respondents, the proportion of their civic participation activities among all applicable activities (15 for Internet users; 11 for non-users) multiplied by 10 was taken as an index of civic participation in general (M=1.25, SD=1.47).

Everyday interaction variables. People’s everyday interactions were measured by asking how often (0=never, 5=very often) they come into contact in offline everyday social life with people in eight categories: 1) private business owners, 2) ling’dao (a Chinese term for “leaders” that refers to government officials and corporate managers), 3) professionals, such as physicians, engineers and teachers, 4) clerks, 5) workers, 6) service staff, 7) peasants and migrant workers, and 8) the unemployed. The Cronbach’s alpha across them is α =.759. First, the responses were recoded to 0 (no contact) and 1 (at least some contact), and the sum across all items was used as the index of heterogeneity of everyday interactions (M=6.27, SD=2.11). Then, the sum of interaction frequencies across all items was divided by the heterogeneity index, and the quotient was taken as the index of intensity of everyday interactions (M=2.56, SD=.85).

Internet use variables. Internet use was measured by a series of questions on the number of days spent online in a typical week, the time spent online per day as an estimate in hours and minutes, and for Internet users, the year when they started using the Internet, and how often they conduct 11 different activities relating to specific Internet usages (0=never, 5=very often).

Based on the responses, we created the variable of Internet use (0=non-use, 1=use; M=.35, SD=.48). For Internet users, we created the variables of years of Internet use (M=6.82, SD=3.64). The product of days per week and minutes per day online was calculated, and for normalization, its square root was taken as the index of time spent online per week (M=32.44, SD=15.45). Internet use activities were grouped into 3 categories: online information usage, social communication, and entertainment. A factor analysis (principle components, OBLIMIN)


yielded a 3-factor solution in support of this conception. Online information usage is the average frequency of 3 items: reading online news, using search engines, and emailing (M=3.04, SD=1.40, α=.675). Online social communication is a 5-item index of using social network sites, writing blogs/forum posts/comments, reading blogs/forum posts/comments, producing and uploading audios/videos, and using VPN to jump China’s censorship system of the “Great Firewall” (M=1.20, SD=1.05, α=.737). 4Online entertainment is a 3-item index of online gaming, chatting, and viewing and downloading online audios/videos (M=3.21, SD=1.34, α=.546).

Traditional media use. The survey contained questions on the frequency of media use for newspapers, TV, radio and magazines. The responses were recoded to 0 (non-use) and 1 (use). The sum of the items was used for the number of traditional media used (M=1.89, SD=1.04).

Urbanization variables. Respondents’ actual residential locations were coded to 1=non-city locales, 2=county-level cities, 3=urban districts in regional cities, and 4=urban districts in provincial capitals/municipalities. It was taken as the variable of city level (M=2.17, SD=1.26). Yet, city levels cannot reflect the dynamics of rural population flowing into cities and rural areas incorporated by cities. We used the respondents’ information relating to hukou (household registration record) and residence units to create the variable of “rural residence and hukou” (0=with urban hukou or living in an urban residence unit and 1=with rural hukou and living in a rural residence unit; M=.47, SD=.50). People with urban hukou or living in an urban residence unit have more accessibility to the infrastructure and other conveniences in urban environment. Regional economic development & Internet penetration variables. These variables were used to capture the wide regional gaps of economy and Internet diffusion in China. Three binary variables of economic region, East China, Northeast China and Central China, were created based on the government’s classification (the West, the central, the Northeast and the East in the


order from the least to the most developed). Provincial GDP per capita (RMB yuan) was created (M=33264, SD=16732) based on the 2010 data from National Bureau of Statistics of China (www.stats.gov.cn, 2011). Based on our respondents’ information on individual Internet use, we computed the percentages of Internet users among all respondents for each surveyed locale as regional Internet penetration rate (M=35%, SD=19%).

Demographics. We used 13 variables to profile one’s demographic information. First, we included age, sex, years of education, income (personal and household income combined), the number of household appliances owned as an alternate measure of household wealth, 5 and Communist Party membership due to the centralization of institutional means in the Communist Party in China. Secondly, we included 2 variables concerning marital status to consider people’s relevant life stages in a detailed way: single (0=in a marital/cohabitation relationship or with such relationships before, 1=never in a marital/cohabitation relationship), and married (0=divorced, widowed, separated or never married, 1=in a marital/cohabitation relationship). Lastly, a set of binary variables on employment and occupation were used: having worked ever, with a managerial job, with a professional job, with a clerical job, and with a peasantry job. Statistical analysis

PS weighting and matching. First, a logistic regression analysis was conducted to predict Internet use with demographics, urbanization, regional economic development and Internet penetration, and survey weights variables.6 It helped us attain the propensity scores of Internet use for all respondents. Then, we conducted PS weighting and matching to estimate the treatment effects of Internet use on citizen engagement outcomes.

One-to-one nearest neighbor PS matching without replacement and with the caliper of .001 was conducted using the “MatchIt” package in R (Ho, Imai, King & Stuart, 2011),


generating the matched sample (n=9,194). Using this sample, average treatment effects (ATEs) of Internet use on engagement outcomes were estimated through both T-tests and ATE estimation by imputations with the “Zelig” package in R (Imai, King & Lau, 2011).

To address the potential compromise in external validity with the matched sample, we also drew a random subsample of comparable size (n=9000) from the dataset of original sample to conduct PS weighting. The IPTW (inverted probability of treatment weighted estimator) was calculated from propensity scores for this subsample. The product of IPTW and survey weights was taken as the “combined weights”. According to DuGoff, Schuler and Start (2014), regression analysis weighted by the combined weights can allow us to adjust for self-selection bias and simultaneously take the survey design into account, thereby attaining better generalizability to population. A series of weighted regression analyses were conducted to estimate ATEs of Internet use on engagement outcomes, with the covariates of demographics, urbanization, regional economic development, and Internet penetration. The estimates of Internet effects from PS weighting, after a comparison with matching estimates for triangulation,7 were adopted as our reported ATEs of Internet use on citizen engagement due to their better generalizability.

Analysis on socially-embedded Internet effects. Analyzing the data of the weighted random subsample (n=9000), we continued to examine the effects of Internet use and everyday interactions on engagement outcomes and explore their possible interactions. With the combined weights applied, a series of weighted regressions were conducted with the same covariates as in the PS weighting procedures, plus the predictors of Internet use, traditional media use, everyday interaction variables, and the interaction terms of Internet use and the interaction variables. Traditional media use was included to account for a wide range of media usages rather than


focusing only on Internet use, because according to Shah et al. (2005), traditional media are often found to complement the civic influences of Internet use.

Then we zoomed in on the Internet users in the random subsample (n=3,208). A series of linear regressions and logistic regressions were conducted to estimate the effects of specific Internet usages and everyday interaction variables on engagement outcomes, and explore the possible interactions between different Internet usages and the interaction variables.

Results Preliminary analyses on ATEs of Internet use on citizen engagement

Starting from a brief look at sample characteristics, the national sample (n=37,279) by average is 43.38 years old and receives 7.51 years of education; 43.77% of the people hold a peasantry job, and 35.48% of them are Internet users (Table A1, Appendix). The proportion of Internet users in the sample is very close to China Internet Network Information Center’s (2011) reported national Internet penetration rate of 34.3% in 2010. This level of Internet penetration is much lower compared with that in developed countries in the same year (World Bank, 2011), which was at 71.7% in the U.S. and 85.0% in the U.K.

The propensity score model, which predicts Internet use by a logistic regression analysis (Table A2, Appendix), reveals that the individuals who are younger, richer, better educated, with Communist Party membership, and with non-peasantry jobs are more likely to use the Internet. Urbanization and high regional Internet penetration also encourage Internet use. The overall percentage of Internet use predicted by this model is 87.11%.

PS matching, which matches Internet users and non-users by their propensity scores of Internet use, helps adjust the self-selection bias in estimating the civic effects of Internet use. As


shown in Table A3 of Appendix, the matched sample (n=9,194) generated from our matching procedure has a good balance in almost all the predictors of Internet use and propensity scores. The estimation of ATEs of Internet use on citizen engagement by imputations (Table 1) shows that using the Internet positively contributes to offline civic participation (ATE=.325), opinion expression in general (ATE=1.221), offline opinion expression (ATE=.658), and knowledge on current affairs (ATE=.859). But it has no effect on civic participation in general, measured as the proportion of one’s participatory activities among all applicable activities. It is likely due to the reason that Internet use enlarges the range of possible participatory activities to include online options, and the Internet’s benefit to civic participation is cancelled out by the larger range.

[Table 1 here] Although PS matching generates the estimates of causal effects of Internet use on citizen

engagement with self-selection adjusted for, it is weak in external validity because the matching procedure changes the original sample characteristics (Table A1, Appendix). To overcome this problem, we conducted PS weighting on a random subsample of comparable size (n=9,000) drawn from original dataset, using DuGoff et al.’s (2014) strategy of running regression analyses weighted by combined weights. The estimation by PS weighting (Table 1) shows that using the Internet positively contributes to offline civic participation (ATE=.439), opinion expression in general (ATE=1.140), offline opinion expression (ATE=.659), and knowledge on current affairs (ATE=.470), while it has no effect on civic participation in general. The PS weighting estimates are roughly consistent with the PS matching estimates in their direction and effect sizes. According to Stuart (2010), the triangulation between different estimation strategies is important for demonstrating the credibility of effect estimates by PS methods. The only place where the two procedures show main inconsistency is the Internet effect on knowledge about current


affairs. It is likely due to the changes of sample characteristics in matching. Compared with the whole dataset and the random subsample, the matched sample are by average richer, better educated and with a smaller portion of people holding a peasantry job (Table A1, Appendix). People with such features may be better able to achieve knowledge gain from the Internet.

Due to the importance of generalization for survey-based studies, we adopt the estimates from PS weighting. It shows that when the self-selection bias is adjusted for, Internet use turns out to contribute positively to citizen engagement in terms of opinion expression, knowledge on current affairs, and also offline civic participation, thus lending support to H1. The pro-civic effects of Internet use & everyday interactions

While the PS methods help estimate the effect of Internet use without the interference of endogeneity, the above estimation procedures treat Internet use in isolation from the actual social life where citizens interact dynamically with each other and with a variety of media. In Table 2, with additional predictors relating to everyday interactions and traditional media included, the regression analyses weighted by combined weights allow us to further understand the joint civic influences of Internet use and everyday interactions, and assess the magnitude of the Internet’s civic influences vis-à-vis those of the other antecedents. Table 2 focuses on the results for offline civic participation, offline opinion expression and knowledge on current affairs, but those for the other engagement outcomes are also reported in the description of results below.

[Table 2 here] When additional predictors are added, the civic effects of Internet use remain positive,

but slightly weaker compared with the ATE estimates in Table 1. Internet use has positive influences on offline civic participation (b=.309, p<.001), opinion expression in general (b=.866,


p<.001), offline opinion expression (b=.526, p<.001), and knowledge on current affairs (b=.405, p<.001). These results lend further support to the civic benefits of Internet use as indicated in H1.

Concerning H2a, intensity of everyday interactions contributes positively to offline civic participation (b=.230, p<.001), civic participation in general (b=.227, p<.001), and knowledge on current affairs (b=.213, p<.01). But the interactional intensity has no effect on opinion expression. A possible explanation is that frequent everyday interactions not only furnish social support for civic expression, but also highlights the collectivistic norm that discourages criticisms on social issues and government wrongdoings.

Concerning H2b, heterogeneity of everyday interactions turns out to benefit offline civic participation (b=.158, p<.001), civic participation in general ((b=.132, p<.001), offline opinion expression (b=.167, p<.001), and opinion expression in general (b=.331, p<.001). But it has no effect on knowledge on current affairs. It is possible that in the Chinese context, social and political pressures still make people hesitant to conduct explicit political discussion outside of strong ties. So, in everyday interactions with people from diverse backgrounds, people may often avoid exchanging information on the salient public issues as included in our knowledge measure; or they may talk mainly about mundane topics, some of which have potential public significance, but the information gain from such talks cannot be captured by our knowledge measure.

The number of traditional media used exerts positive effects on all engagement outcomes. It suggests that despite strict government control in China, traditional media still contribute to broadening the civic space of information, expression and participation. Such a role of traditional media in China is probably related to its increasingly commercialized media system’s market pursuits in terms of meeting public demands and dealing with competition from online media.


A comparison of standardized regression coefficients for these predictors shows two patterns. First, the civic effects of Internet use turn out to be relatively moderate. Its effect on offline participation (b*=.097) is weaker than that of traditional media (b*=.109), interactional intensity (b*=.122) and interactional heterogeneity (b*=.204). Its effect on offline expression (b*=.167) is weaker than that of interactional heterogeneity (b*=.218). Even for knowledge on current affairs, its contribution (b*=.072) is smaller than that of traditional media use (b*=.104). Secondly, interactional heterogeneity contributes more to participation and expression outcomes than do Internet use, traditional media use and interactional intensity. It suggests the particular importance of coming into contact with people across social strata for enabling active citizenry.

Concerning RQ1a and RQ1b, our exploration highlights the interaction effects of Internet use and interactional heterogeneity on both offline opinion expression (b=.084, p<.05), and opinion expression in general (b=.177, p<.05). Figure 1 shows that being an Internet user could have a differential impact on one’s level of offline expression. For those who are situated in heterogeneous everyday interactions, using the Internet can boost their offline expressions significantly more than those with less heterogeneous interactions. While we expect the Internet to enact a social revolution revealing the critical impulses of Chinese people (Yang, 2009), the results show that this potential influence may not play out to the full, if separated from everyday interactions with sufficient social heterogeneity.

[Figure 1 here] Socially embedded civic effects of online information and communication

With the measures of Internet use including specific usages, history and frequency, we explore more fully the civic effect of Internet use and the ways in which it may work together with everyday offline social interactions for Internet users in China.


Among Internet use predictors, specific usages, particularly online social communication, turn out to play a more important role in influencing citizen engagement compared with years of Internet use and its weekly frequency (Tables 3 & 4). Online information usage benefits people’s knowledge on current affairs (b*=.199, p<.001). Online social communication benefits offline civic participation (b*=.115, p<.01), online civic participation (Standardized OR=1.791, p<.001), and participation in general (b*=.163, p<.001). To a greater extent, it also encourages offline opinion expression (b*=.194, p<.001), online opinion expression (Standardized OR=2.810, p<.001) and expression in general (b*=.276, p<.001). Overall, the civic benefit of the Internet mainly lies in people’s utilization of it for informational and communicative purposes.

[Tables 3 & 4 here] Everyday interaction variables, especially interactional heterogeneity, are found to exert

positive effects on most participation and expression outcomes both offline and online. Yet neither interaction variables are found to exert effects on knowledge on current affairs. As mentioned above, our knowledge measure focuses on some most salient public topics. It is likely that for Internet users, with their quick convenient access to online information, the additional knowledge gain on such issues from everyday offline interactions is limited. Also, while everyday interactions may increase one’s knowledge on politically meaningful issues in one’s immediate social milieu, this benefit may not be captured by our knowledge measure.

Concerning RQ2a and RQ2b, our analysis reveals some interesting interaction effects of online information usage and interactional heterogeneity, and of online social communication and interactional intensity. Most of the interaction effects are on opinion expression outcomes.

Interactional heterogeneity positively moderates the benefits of online information usage to offline opinion expression (b*=.095, p<.01) and expression in general (b*=.074, p<.01).


Figure 2 illustrates the differential effects of online information usage on offline expression, depending upon the level of heterogeneity of everyday interactions. It fosters offline expression when one has high interactional heterogeneity, and discourages it when one has low interactional heterogeneity, highlighting the importance of information exchange and interpretation with diverse social members for enabling the mobilizing role of online information for expression.

[Figure 2] Interactional intensity reinforces the benefits of online social communication to offline

opinion expression (b*=.054, p<.05) and expression in general (b*=.044, p<.05). While online communication resources – blogs, social network sites and the services of jumping the Great Firewall to communicate with people outside of China – can help mobilize offline expression, this benefit is more pronounced for those with high interactional intensity, as shown in Figure 3. Through frequent everyday interactions, Internet users maintain the connections to their local ties, groups and communities, which might be an important condition for them to apply the communicative efficacy developed in online platforms to offline opinion expression. The social support and trust as cultivated via close interactions may further empower them to voice critical opinions on social issues through such offline channels of government, businesses and media.

[Figure 3] Interactional intensity also has a positive moderation effect on the benefit of online social

communication to offline civic participation (b*=.061, p<.05), but no such effects for the other participation variables. As Figure 4 shows, online social communication has little benefit to offline participation for those with low interactional intensity, but this benefit becomes obvious for those with high interactional intensity.


[Figure 4] Interestingly, interactional heterogeneity is also shown to slightly weaken the benefit of

online information usage to knowledge on current affairs (b*=-.048, p<.05), as Figure 5 shows. A possible explanation is that many of those Internet users who both use online information frequently and have high interactional heterogeneity are likely to be young people, who are technologically savvy, socially active and unsettled in the routines of marriage and family yet. These people may not be especially keen on using the Internet for the flat knowledge on politics and economy, but more interested in other forms of knowledge to their individualized tastes.

[Figure 5] Conclusion and discussions

This study examines the civic effects of Internet use as embedded in the offline context of everyday interactions, using national survey data from China. It offers robust estimates of the civic effects of Internet use, using PS methods to adjust for the self-selection bias. Internet use is found to benefit opinion expression, participation in voluntary associations, and knowledge on current affairs, but these effects are moderate compared with those of interactional heterogeneity and traditional media use. This resonates with Boulianne’s (2009) summary of the civic effects of Internet use in North America and Kroh et al.’s (2009) estimate in Germany.

Moreover, the intensity and heterogeneity of offline everyday interactions are found to foster civic expression and participation, and reinforce the civic effects of Internet use. High interactional heterogeneity strengthens the benefit of Internet use to expression. Among Internet users, high interactional heterogeneity reinforces the benefit of online information to offline expression; high interactional intensity increases the benefit of online social communication to


offline expression and participation. It is fair to conclude that offline everyday interactions, particularly its heterogeneity, exert meaningful “value-added” influences on the Internet effects.

This study is relevant to the debate on the Internet’s political implications in China. Our estimates of civic effects of Internet use indeed lend support to the beneficial influence of the Internet in enlarging the civic space of information, expression and participation for its people (Yang, 2009; Zheng & Wu, 2005). Yet this does not warrant a sense of optimism for bottom-up political transformation in the country. The results on the interplay of Internet use and sociality highlight the following elements of hopes and risks in its social reality.

First, both the direct civic effects of Internet use and the moderation effects of interaction variables are most pronounced for expression outcomes. When it comes to civic participation outcomes, using the Internet has a positive effect on offline participation, but none on the level of participation in general; interactional heterogeneity or intensity does not help add to its effects here. That is to say, even when we consider the combined influences of the Internet and sociality, the benefit of Internet use to participation is limited compared with that to opinion expression, perhaps because joining and organizing voluntary associations are relatively high stake activities, especially in China. This suggests that a bigger challenge for the Internet to nurture China’s democratizing potential is enabling citizens to use it for civic participation more effectively.

Secondly, aggressive urbanization in China has brought about a half of its population to live in cities (Ren, 2013). For urban residents, their expanded range of social life necessitates interactions with people across social strata. According to our findings, this is a driving force of civic engagement and a factor that amplifies civic benefits of Internet use. Yet the expansion of network can also entail lower interactional intensity, which may exert opposite civic influences. This risk of loose connections can be even greater for rural residents who have flowed into cities,


due to the “non-integration” tendency (Li, 2011; Wang, 2006) – living in cities, they have limited interaction with the locals, even facing social isolation, and in light of our findings, the Internet’s civic benefits can be profoundly compromised for them. Furthermore, as Qin (2014) emphasized, continuous movement of working force into cities has eroded the basis of social interactions in many rural areas. This tendency may aggravate the problems of disengagement for rural China. Besides losing social capital for civic engagement, rural people may also be situated in a social context that hinders their mobilization of Internet civic resources, even if accessible at all.

Theoretically, our study extends insights from prior research on the interplay of online information usage and political talks in their pro-civic effects (e.g., Hardy et al., 2005; Shah et al., 2007), lending support to the implications of offline everyday interactions in furnishing civic resources and strengthening the civic benefits of online information as well as communication.

Of course, everyday interactions can contain the opportunities for political talks, which may contribute to part of the direct and moderation effects of interaction variables here. Yet a detailed look at the results shows that Chinese citizens’ interactional heterogeneity has no effect on their knowledge about current affairs, and for Internet users, neither interactional intensity nor heterogeneity causes knowledge gain. This pattern suggests that political talks, which touch upon salient public issues explicitly, may not be a main factor enabling the effects of interactions here. The uniqueness of Chinese political system that discourages political talks is an important reason for such a pattern. But besides that, it also indicates that political talks are not the only politically meaningful factor in dynamic day-to-day interactions. Mundane interactions unrelated or not explicitly related to politics can also serve to drive civic engagement and channel the Internet effects on it. Although our study is about China, this theoretical point corresponds with Marques and Maia’s (2010) qualitative observation of interrelated influences of media and everyday


conversations in facilitating political deliberation, and Eliasoph (2000)’s argument that everyday interactions can also help shape engaged citizenship, based on her ethnographic evidences.

Also, the moderation effects of interaction variables apply to the civic benefits of online communication as well. In our results, the benefits of online social communication to offline expression and participation are greater for Internet users with high interactional intensity. It suggests that frequent offline interactions, which is key to cultivating social support and trust, are helpful for the conversion of online communication resources to the ends of active engagement offline. This pattern has to do with the Chinese context, where offline civic efforts may be in great need of interpersonal incentives under the intolerant political system. But the theoretical relationship manifested here may not be unique to China, because no matter in which society, civic engagement is never cost-free, but demands time, cognitive efforts and even some civic enterprise. Thus it is reasonable to expect that interpersonal trust and support, as cultivated via frequent interactions, can incentivize offline engagement in other social contexts, and this can be particularly meaningful for those holding minority views (Finifter, 1974). This point is meaningful for considering a main concern held by researchers of Internet and politics – while decentralized online communication network allows for pluralistic expression and even revolutionary ferment, how to take online civic opportunities outside of the virtual world (Altman, 2011; Lim, 2012)? A partial answer is likely related to Internet users’ close interactions with offline ties that furnish social incentives. Some examination into what encourages their offline interactions and how they mobilize social support in using online communication resources for offline civic endeavors may help further address the above concern.

Based on the above discussion, we argue that the effects of everyday interactions in our results also deserve attention in social contexts other than transitional societies like China. In


democratic countries, given the tendency of privatization of politics, scholars call for “bringing a public-spirited imagination to bear on seemingly private experiences” (Eliasoph, 2000, p.67). Couldry et al. (2005) stress that rather than limiting our imagination to citizens’ involvement in formal politics or to the ideal of informed citizenry, we should take a broader perspective to consider how citizens attain their “public connection” as “a general sense of orientation to some shared world of collective action” that enable various forms of citizenship (p. 4). Reflecting on our findings in light of the dynamics of citizenship in democratic countries, we suggest that it is worthwhile to observe the cultivation of “public connection” in the interplay of Internet use and people’s everyday interactions, the latter of which can contain meaningful public inquiries mantled in day-to-day conversations, sustain communication channels, and furnish social support and trust. Citizenship must also be rooted in such mundane interactions to gain its momentum.

Methodologically, our study illustrates how PS methods can be fruitfully used for the estimation of civic effects of Internet use. Some points should be noted for their application in survey research on relevant topics. First, large-scale surveys with rich background information for respondents facilitate the power for predicting propensity scores of Internet use, making the adjustment of self-selection bias effective. Secondly, PS weighting with the combined weights, IPTW*survey weights, is crucial for attaining generalizable estimates of the Internet effects, as it allows us to adjust for selection bias and simultaneously consider survey design. Thirdly, the triangulation of estimations by PS weighting and matching can add to the reliability of estimates.

Given the complexity of relationships of the Internet, sociality and citizen engagement, our study only covers a small part of the overall mechanism that has yet to be revealed. Our model on differential civic effects of Internet use as moderated by everyday interactions is tested using the data from China. Its manifestation in other societies deserve further examination. Also,


our model does not attend to reciprocal causality between Internet use and everyday interaction variables. Future research might consider the influences of interactions on Internet use, or the mediation of the Internet’s civic effects by interaction variables. Moreover, the civic influences of everyday interactions are posited on the recognition of various pro-civic resources cultivated in such interactions. It will be meaningful to include the measures on these specific network resources in later investigations to contribute to a detailed understanding of the mechanism.

Endnotes 1 When we use Internet use vs. non-use as the exogenous variable in the prediction of citizen engagement, it is hard to differentiate the treatment effect, and the self-selection effect that those individuals who are actively engaged are more likely to be Internet users (Kroh & Neiss, 2009). Theoretically, many characteristics (i.e., education, income, occupation, regional economy, etc.) are strongly associated with being Internet users, and simultaneously, have to do with levels of civic engagement (Verba, Schlozman & Brady 2002). This makes it particularly important to deal with self-selection bias in estimating the causal effects of Internet use on engagement outcomes. 2 More specifically, parallel random sample surveys were conducted in all the 31 provincial level administrative divisions of Mainland China with a pooled sample size of 37,279 complete interviews. In each provincial level division, the survey adopted the same multi-stage cluster sampling procedure and the same questionnaire. 3 Voting is not included here, since elections in China are characterized by the Communist Party’s constraints on candidate nomination and voters’ choices, thus limiting voting as a spontaneous expression of citizen interest and democratic pursuits (Chen & Zhong, 2002). 4 The factor structure that emailing loaded on online information usage rather than online social communication is consistent with previous research (Shah, Kwak & Holbert, 2001; Swickert, Hittner, Harris & Herring, 2002). The activity of using VPN to jump the Great Firewall loaded on the factor of online social communication, rather than online information usage. It is probably due to the reason that many young, tech-savvy users jumped the Great Firewall to use social network sites blocked in China, such as Facebook, Twitter and Youtube. 5 It is difficult to measure household income reliably in a large-scale survey in China due to the various, oftentimes undocumentable ways in which households might earn income. Therefore, we use the number of household appliances owned as an alternate and more reliable measure of household wealth. This measure was correlated to household income at .43 and personal income at .38 respectively. 6 In our data, survey weights are the inverse of the probability of being selected into sample, constructed based on census data and sampling design features. Using survey weights as a predictor in the propensity score model is recommended by DuGoff, et al. (2014), who held that survey weights might capture some relevant factors that the dataset fails to include. 7 PS methods can be used to adjust for the potential bias in treatment assignment. Using the propensity scores, that is, the probabilities of taking treatment given a vector of observed variables, for matching or weighting, we can realize the quasi-random assignment into treatment and control (Rosenbaum & Rubin, 1983). However, PS methods also involve uncertainty, since different strategies or specifications of matching standards often yield inconsistent estimates. Thus it is important to apply multiple strategies to estimate the same effect. If multiple procedures arrive at similar estimates, then the estimates can be considered reliable (DuGoff, et al., 2014; Stuart, 2010).


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Table 1 Estimation of average treatment effects (ATEs) of Internet use on citizen engagement variables by different methods

Civic Engagement

Matched sample (n = 9,194)

Random subsample (n = 9,000)

ATE 95% CI ATE 95% CI Civic participation .005 [- .035, .046] .168 [- .000, .337] Offline civic participation .325 [.227, .370] .439 [.247, .631] Expression 1.221 [1.127, 1.311] 1.140 [.655, 1.624] Offline expression .658 [.612, .705] .659 [.421, .897] Knowledge of current affairs .859 [.795, .928] .470 [.165, .774] Estimation methods Imputation Weighted regression

Note. Weighted regressions were conducted with the random subsample (n=9,000), using the combined weights (IPTW*survey weights), to estimate the ATEs of Internet use on civic engagement outcomes. 21 covariates were controlled for, including demographics, economic regions, and the aggregate variables of regional Internet penetration rate and provincial GDP per capita. Meanwhile, the same set of ATEs were estimated with the matched sample (n=9,194) through conducting imputations by the “Zelig” package in R. In addition to what is reported in this table, we also conducted a series of t-tests on the matched sample to estimate the ATEs of Internet use on engagement outcomes, which were highly consistent with the imputation results.


Table 2 Estimated effects of Internet use, traditional media use, and social interaction in the random subsample (n = 9,000)

Variable

Offline civic participation

Offline expression

Knowledge of current affairs

Media use Number of traditional media used .187 *** .109 .179 ** .106 .314 *** .104 Internet use .309 ** .097 .526 *** .167 .405 ** .072 Social interaction

Intensity of everyday interaction

.230 ***

.122

.080

.043

.213 **

.064

Heterogeneity of everyday interaction

.158 ***

.204

.167 ***

.218

.044

.032

Interaction term Intensity of everyday interaction*Internet use

.098

.025 .103

.027

-.015

-.002

Heterogeneity of everyday interaction*Internet use

.078

.047

.084 *

.051

-.088

-.030

ΔR²

.076

.093

.019 .461 *** Total R² .259 *** .221 ***

Note. Cell entries are unstandardized regression coefficients followed by their corresponding standardized coefficients. The regressions were weighted by the combined weights (IPTW*survey weights). In all the regressions, 21 covariates were controlled for, including demographics, economic regions, and the aggregate variables of regional Internet penetration rate and provincial GDP per capita. * p < .05, ** p < .01, *** p < .001

Table 3


Prediction of civic engagement with Internet usage, traditional media use, and social interaction among Internet users in the random subsample (n = 3,208)

Variable Offline

civic participation Offline

expression Knowledge of current affairs

Media use Number of traditional media used .295 *** .175 .156 ** .092 .255 *** .094 Years of Internet use -.023 -.044 .009 .017 .027 .033 Time spent using Internet per week -.001 -.005 -.004 -.034 .001 .006 Online information usage .017 .014 .052 .042 .400 *** .199 Online social communication .200 ** .115 .340 *** .194 .013 .005 Online entertainment -.012 -.009 -.025 -.018 -.154 ** -.069 Social interaction Intensity of everyday interaction .372 *** .157 .233 *** .098 -.032 -.008

Heterogeneity of everyday interaction .179 *** .166 .284 *** .262 -.040 -.023 Interaction term Heterogeneity of everyday interaction

*Online information usage .003

.005

.061 **

.095

-.050 *

-.048

Intensity of everyday interaction *Online social communication

.136 *

.061

.121 *

.054

-.122

-.034

ΔR²

.110 .228 ***

.127 .232 ***

.043 .381 *** Total R²

Note. Cell entries are unstandardized regression coefficients followed by the corresponding standardized coefficients. 21 covariates are controlled for, including demographics, economic regions, and aggregate variables of regional Internet penetration rate and provincial GDP per capita. * p < .05, ** p < .01, *** p < .001


Table 4 Logistic regression of online civic participation and expression on Internet usage, traditional media use, and social interactions among Internet users in the random subsample (n = 3,208) Online civic participation Online expression

Variable OR Standardized OR OR Standardized OR Media use Number of traditional media used 1.218 * 1.232 1.100 1.106 Years of Internet use 1.020 1.072 1.032 1.118 Time spent using Internet per week 1.016 ** 1.277 1.009 1.152 Online information usage .994 .992 1.088 1.128 Online social communication 1.791 *** 1.811 2.757 *** 2.810 Online entertainment 1.058 1.076 .897 .868 Social interaction Intensity of everyday interaction 1.465 ** 1.331 1.195 1.142 Heterogeneity of everyday interaction 1.232 ** 1.409 1.233 *** 1.411 Interaction term Heterogeneity of everyday interaction *Online information usage

1.006

1.017

1.005

1.013

Intensity of everyday interaction *Online social communication

.805 *

.842

1.222

1.173

X2 (df=10)

88.90 ***

175.64 ***

Wald X2 (df=31) 186.40 *** 337.30 *** McFadden R² .168 *** .254 ***

Note. In all the regressions, 21 covariates are controlled for, including demographics, economic regions, and the aggregate variables of regional Internet penetration rate and provincial GDP per capita. * p < .05, ** p < .01, *** p < .00


Figure 1. Confidence interval graph for the interaction of Internet use and heterogeneity of everyday interaction on offline opinion expression (n=9,000)


Figure 2. Confidence interval graph for the interaction of online information usage and heterogeneity of everyday interaction on offline opinion expression among Internet users (n=3,208)


Figure 3. Confidence interval graph for the interaction of online social communication and intensity of everyday interactions on offline expression among Internet users (n=3,208)


Figure 4. Confidence interval graph for the interaction of online social communication and intensity of everyday interaction on offline participation among Internet users (n=3,208)


Figure 5. Confidence interval graph for the interaction of online information usage and heterogeneity of everyday interaction on knowledge of current affairs among Internet users (n=3,208)


Appendix Table A1 Demographics, economic regions, and Internet penetration of the whole sample, the random subsample and the matched sample

Characteristics Whole sample (n = 37,279)

Random subsample (n = 9,000)

Matched sample (n = 9,194)

Demographics Sex (% male) 49.82 50.03 51.48 Age 43.38 43.33 38.96 Education (years) 7.51 7.54 10.17 Personal and household income combined (RMB)1 1530.17 1524.05 2419.45 Number of household appliances owned (0-16) 3.45 3.46 4.78 Single (%) 11.51 11.92 10.75 Married (%) 82.20 81.70 84.99 Communist Party membership (%) 8.41 8.53 11.36 Managerial (%) 1.30 1.38 2.72 Professional (%) 4.84 4.91 8.56 Clerical (%) 5.35 5.54 10.43 Peasant (%) 43.77 43.96 10.82 Having worked ever (%) 94.32 94.09 94.74 Economic regions East China (%) 38.29 38.89 33.97 Northeast China (%) 8.78 8.69 10.94 Central China (%) 26.95 26.03 19.18 Internet use (%) 35.48 35.64 50.00

1 The combined income is the average of each respondent’s personal income and household income per month.


Table A2 Logistic model prediction of Internet use in the propensity score model (n = 37,279) Internet use

Variable b OR Constant - 2.129 *** .119 Demographics Sex (male) .385 *** 1.469 Age - .108 *** .897 Age squared .000 1.000 Education (years) .266 *** 1.304 Personal and household income combined .045 *** 1.046 Number of household appliances owned .214 *** 1.239 Single (vs. sample mean) .503 *** 1.654 Married (vs. sample mean) - .316 *** .729 Communist Party membership .242 *** 1.274 Managerial (vs. sample mean) .389 *** 1.476 Professional (vs. sample mean) .252 *** 1.287 Clerical (vs. sample mean) .352 *** 1.422 Peasant (vs. sample mean) - .841 *** .431 Having worked ever .209 * 1.233 Economic regions East China (vs. sample mean) .005 1.005 Northeast China (vs. sample mean) .133 ** 1.142 Central China (vs. sample mean) - .005 .995 Urbanization City level (1-4) .237 *** 1.268 Rural residence and Hukou - .475 *** .622 Aggregate variables Regional Internet penetration rate 4.164 *** 64.359 Provincial GDP per capita - .010 *** .990 Survey weight2 .076 *** 1.079 Overall percentage predicted (%)

87.11

McFadden R² .552 *** * p < .05, ** p < .01, *** p < .001 2 Using survey weight as a predictor in the propensity score model is a PS matching strategy recommended by DuGoff et al. (2014), who held that survey weight can capture some relevant factors the dataset fails to include. With this strategy, our following matching procedure achieved an almost perfect balance between treatment and comparison as shown by Table A3 and Figure A4 in this appendix.


Table A3 Mean comparison between Internet users and non-users in the matched data (n = 9,194)

Variable Net users Non-users p-value

n = 4,597 n = 4,597 Propensity score for Internet use .460 .460 .820 Sex .513 .516 .786 Age 38.759 39.162 .117 Education (years) 10.158 10.190 .561 Number of household appliances owned 4.769 4.790 .672 Single (%) .109 .106 .590 Married (%) .847 .852 .559 Communist Party membership (%) .111 .116 .233 Managerial (%) .029 .025 .200 Professional (%) .084 .087 .628 Clerical (%) .106 .102 .562 Peasant (%) .115 .100 .021 Having worked ever (%) .947 .948 .852 East China (%) .339 .341 .843 Northeast China (%) .111 .108 .593 Central China (%) .189 .194 .542 City level (1-4) 1.934 1.901 .173 Rural residence and Hukou .349 .337 .219 Regional Internet penetration rate .399 .405 .108 Survey weight .877 .863 .560 Personal and household income combined (RMB)

2416.15

2422.75

.738

Provincial GDP per capita (RMB) 34,311 34,440 .720


Figure A4. Propensity score distribution for Internet users (treated) and non-users (control). The histogram on the left denotes the distribution in the whole sample (before matching); that on the right, the distribution in the matched sample (after matching).

Raw Treated

Propensity Score

Densi

ty

0.0 0.2 0.4 0.6 0.8 1.0

01

23

45

6Matched Treated

Propensity Score

Densi

ty

0.0 0.2 0.4 0.6 0.8 1.0

0.00.4

0.81.2

Raw Control

Propensity Score

Densi

ty

0.0 0.2 0.4 0.6 0.8 1.0

02

46

810

Matched Control

Propensity Score

Densi

ty

0.0 0.2 0.4 0.6 0.8 1.0

0.00.4

0.81.2

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