When system justifiers feel anxious: conservatism, institutional trust, and policy preferences in China

When system justifiers feel anxious: conservatism, institutional trust, and policy preferences in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article When system justifiers feel anxious: conservatism, institutional trust, and policy preferences in China Gong Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9443334/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract The article aims to investigate how conservative ideology in contemporary China shapes the public’s institutional trust and domestic/foreign policy preferences. To facilitate between-country comparison, this study draws on a psychological approach to political ideology and defines conservatism as a combination of two elements: traditionalism and anti-egalitarianism. Similar to their Western counterparts, Chinese conservatives, accepting of traditional values and social inequalities, are hypothesized to support authoritarian policies at home and hawkish policies abroad. Additionally, conservatism plays a system-justifying role in satisfying people’s needs for certainty and security. As such, individuals who endorse conservative ideology are expected to be more trusting of the current institutions led by the Chinese Communist Party. Given the Party’s increasingly repressive and assertive policies in the era of Xi Jinping, it is further argued that institutional trust partially mediates the link between ideology and policy opinions. More importantly, under the circumstances of (internal/external) threat-induced anxiety, Chinese citizens scoring higher on conservativism are motivated to double down on their attachment to the Party-dominated institutional status quo and authoritarian-hawkish policies, resulting in significant interaction effects. Analyses of the Netizens’ Social Awareness Survey (2019–2020) are largely supportive of the above arguments. Implications on China’s bottom-up regime change in times of diminishing economic performance are also discussed. Social science/Politics and international relations Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Despite a variety of stimulus measures, China’s post-COVID economy shows few signs of recovery due to continuing headwinds, including the deflation of a massive real estate bubble, local government off-balance-sheet debt, foreign capital outflows, excess capacity in manufacturing, high youth unemployment, and weak consumer and investor confidence (Horowitz, 2025 ; Piao and Cui, 2024 ). Because the Chinese Communist Party (CCP), to a large extent, bases its legitimacy on economic performance, it is expected that Chinese people, under increasingly harsher conditions, will become anxious and pessimistic about their economic well-being, skeptical and critical of the government, and even willing to struggle for civil liberties and political rights (Alisky et al., 2025 ; Beckley and Brands, 2023 ). However, popular reactions to system threat in general and poor regime performance in particular can also be contingent. For example, according to Neundorf et al.’s ( 2025 ) study of Turkey, during a severe economic crisis, government voters, compared to non-government voters, were less likely to defect from the ruling regime party (Justice and Development Party, AKP) and more likely to remain loyal to it. Indeed, for those strongly attached to the AKP, negative economic evaluations enhanced the probability of voting again for it in the subsequent election. Although China, unlike Turkey and other electoral autocracies/democracies, is a single-party authoritarian regime that lacks partisan competition, this article attempts to explore whether and how ideology (i.e., conservatism) shapes Chinese people’s political attitudes (i.e., institutional trust and domestic/foreign policy preferences), especially in times of societal threat and public anxiety. Extending Jost’s ( 2021 ) political psychological model from the West to China, right/conservative and left/liberal ideologies are differentiated by two fundamental tradeoffs: (1) tradition versus progress and (2) hierarchy versus equality. With a focus on the right (vs. left) in this paper, I define conservatism (vs. liberalism) in contemporary China as a combination of traditionalism (vs. anti-traditionalism) and anti-egalitarianism (vs. egalitarianism). Drawing on the psychological approach to political ideology, I argue that Chinese conservatives, because of their acceptance of traditional values and social inequalities, tend to support authoritarian policies at home and hawkish policies abroad. Additionally, conservatism is a system-justifying ideology that satisfies people’s needs for certainty and security. As such, individuals who endorse conservative ideology are hypothesized to place more trust in the current institutions led by the CCP. Given the party-state’s increasingly repressive and assertive policies in the era of Xi Jinping, I further expect institutional trust to be a mediator that partially accounts for the link between ideology and domestic/foreign policy opinions. More importantly, under the circumstances of (internal/external) threat-induced anxiety, Chinese citizens scoring higher on conservatism are motivated to double down on their attachment to the CCP-dominated institutional status quo and authoritarian-hawkish policies. In other words, threat-induced anxiety is suggested to be a moderator that reinforces the effects of conservatism. Analyses of the Netizens’ Social Awareness Survey (2019–2020) are largely supportive of the above arguments. This article contributes to the existing literature in three ways. First, the psychological approach to political ideology is not regime- or culture-specific, which facilitates between-country comparison. To date, much of the research has employed preferences over political (liberal-democratic vs. CCP-dictatorial) and economic (market-oriented vs. state-directed) policy issues to measure the mass public’s ideology in China (Beattie et al., 2022 ; Ma and Lewis, 2020 ; Pan and Xu, 2018 ; Wu and Meng, 2023 ). This regime-specific operationalization, although widely adopted in China studies, may lead to inconsistent findings and hinder comparative studies. Contrarily, as psychological dispositions, unlike political or economic policy opinions, are pre-political or non-political (Jost, 2021 ), the psychological approach to ideology (tradition vs. progress, hierarchy vs. equality) helps to explore the degree to which Chinese conservatives/liberals are (dis)similar to their Western and democratic counterparts. Second, this work engages with the studies on system justification in China. An emerging literature in social/political psychology has probed system-justifying beliefs among the Chinese public. Some revolve around the impacts of system justification on life satisfaction and corruption perception (Li, Wu, and Kou, 2020 ; Tan et al., 2016 ), while others round the antecedents of system justification like socioeconomic status and conspiracy mentality (Li, Yang, Wu, and Kou, 2020 ; Mao et al., 2024 ; Valdes et al., 2023 ). To the best of my knowledge, this is the first study examining both the causes (political ideology) and consequences (domestic/foreign policy attitudes) of system justification, embodied by Chinese citizens’ trust toward the current institutions. More specifically, institutional trust is proposed to be a mediator linking ideology to policy attitudes. Third, apart from individual-level ideological factors, this research deems public anxiety induced by internal/external threats to be a moderator and analyzes the contextual variability in ideology-political attitude relationships. In particular, the majority of previous research about ideological asymmetries in response to system threat is based on Western, educated, industrialized, rich, and democratic (WEIRD; Henrich et al., 2010 ) cases, which makes the inference, at least geographically speaking, narrowly defined. After all, collective threats, such as national economic downturns (internal) and global public health crises (external), are potential problems that all states may face. To expand the scope and boundary conditions, this study distinguishes between internal and external threats and moves beyond Euro-American-centrism with samples obtained from China, a non-WEIRD society. A psychological approach to political ideology in China In a communist regime short of electoral accountability and multiparty competition while transitioning from a centrally planned economy to an open market economy, many Chinese people find it difficult to place them on a left-right spectrum, a common measure of political ideology in democracies (JY Wu, 2023 ). Considering that the conventional labels of left and right lack a coherent programmatic meaning, the extant literature suggests that China’s ideological divide, distinct from that in the West, is organized politically around competitive democracy versus single-party dictatorship and economically around free-market capitalism versus government intervention in the economy (Beattie et al., 2022 ; Ma and Lewis, 2020 ; Pan and Xu, 2018 ; Wu and Meng, 2023 ). 1 Yet, the empirical findings are not conclusive. Studies drawing on online samples find that the political dimension and the economic dimension are highly correlated, so a combined political-economic framework can be used to explain ideological cleavage in China (Beattie et al., 2022 ; Ma and Lewis, 2020 ; Pan and Xu, 2018 ). While the “liberal right” embraces liberal democracy and market economy, the “conservative left” sticks to single-party rule and state intervention. Conversely, Wu and Meng ( 2023 ) reveal that the political dimension is largely orthogonal to the economic one in urban China. For example, individuals who are pro-market do not necessarily prefer political democracy to dictatorship. Likewise, through in-depth interviews, Mulvad ( 2018 ) points out that China’s ideological spectrum is not one-dimensional but two-dimensional, resulting in four ideal typical ideological quadrants (2 political × 2 economic). Differing from past work that utilizes preferences over political-economic policy issues to capture ideology in China, this research engages with the debate from a psychological perspective. According to Jost ( 2021 ), political ideology originates from basic, underlying psychological dispositions that are pre-political or non-political: Tradeoffs regarding (1) tradition versus progress and (2) hierarchy versus equality are the core differences between the right/conservative and the left/liberal. On average, conservatives prefer tradition and hierarchy, whereas liberals are in favor of progress and equality. Notably, the psychological approach to political ideology is not regime- or culture-specific, which facilitates between-country comparison. For this reason, following Jost ( 2021 ), I theorize that China’s ideological divide can also be characterized by the above psychological tradeoffs. With a focus on the political right, I argue that Chinese conservatism, like its Western and democratic counterpart, is composed of two elements: traditionalism and anti-egalitarianism. 2 Conservativism and domestic/foreign policy preferences As Jost ( 2021 ) points out, there exists a slippery slope from conservatism to authoritarianism. In Western societies, people on the political right (vs. left) generally support policies limiting gay rights and abortion while maintaining traditional gender roles and family structures where the husband is the provider and the wife, subordinate to the husband, is the homemaker. For conservatives, traditional and hierarchical relations between men and women, husbands and wives, and parents and children are a central concern (Marietta, 2012 ). Hence, attachment to tradition and hierarchy makes them object to same-sex marriage, gender equality, and feminism. Extending from gender and family to broader social issues, political intolerance/authoritarianism is also more endemic to conservatives than liberals. For psychological reasons, conservatives (vs. liberals) are less willing to extend civil rights to those with whom they disagree (Altemeyer, 1996 ; Davis and Silver, 2004 ; Sniderman et al., 1989 ). Across liberal and electoral democracies, conservativism, albeit to varying degrees, is connected with political intolerance/authoritarianism in general and with homophobia, misogyny, and xenophobia in particular (Norris and Inglehart, 2019 ). Empirical studies have consistently indicated such ideological asymmetries in public policy preferences. For instance, during the 2016 US presidential election, Republican voters, compared to their Democratic counterparts, scored higher on authoritarianism (Womick et al., 2019 ). Particularly, Trump supporters were more likely to believe that “what our country needs instead of more civil rights is a good stiff dose of law and order” and that only a strong, determined leader can “crush the evil and set us in our right way again.” Unlike liberals, conservatives tend to endorse censorship across messages of different political content, an anti-democratic policy that restricts individual freedoms (Fisher et al., 1999 ; Lindner and Nosek, 2009 ). Facing a tradeoff between civil liberties and security, liberals are found to favor the former while conservatives the latter (Davis and Silver, 2004 ). Conservatives are oriented not only to authoritarian policies in domestic affairs but also to hawkish policies in foreign affairs. A good deal of evidence has demonstrated that conservatives (vs. liberals) are more inclined toward militant internationalism (Brewer and Steenbergen, 2002 ), more hostile toward foreign outgroups (De Zavala et al., 2010 ), and more supportive of competitive foreign policies (Binning, 2007 ). On the basis of these findings, I argue that Chinese people who endorse conservative (traditional and anti-egalitarian) ideologies, similar to their Western counterparts, tend to favor policies on the right. That is, individuals high in conservatism are more likely to support authoritarian policies domestically (H1) and hawkish policies internationally (H2). Conservatism as a system-justifying ideology in China According to Jost et al. ( 2003 ), epistemic motives to mitigate uncertainty and existential motives to assuage insecurity are two fundamental antecedents of conservative and right-wing beliefs. In comparison to liberals, conservatives are characterized by greater needs for (epistemic) certainty and (existential) security. Meanwhile, system justification, such as generalized trust in existing political, economic, and social institutions, serves a palliative function in addressing people’s epistemic and existential concerns (Jost, 2019 ). As a consequence, those on the political right (vs. left) are prone to bolster and rationalize the institutional status quo (Jost, 2021 ). The positive correlation between conservatism and system justification or institutional trust has been observed longitudinally in the United States (Jost et al., 2017 ) and cross-sectionally in Argentina, Finland, Germany, Hungary, Latvia, Lebanon, New Zealand, Poland, Sweden, and the United Kingdom (Jost, 2019 , Table 2 ). In the case of China, I likewise posit that individuals high in conservatism are more likely to trust institutions led by the CCP (H3). On top of that, I expect institutional trust to partially account for Chinese conservatives’ policy preferences because the party-state’s domestic and foreign policies in recent years—featured by mounting authoritarianism and nationalism—are mostly favored by these right-wing citizens. Since Xi Jinping took office, China has become increasingly repressive at home and aggressive abroad (Beckley and Brands, 2023 ; Economy, 2018 ). Domestically, departing from the liberalizing reform in post-Mao China, a variety of coercive measures—jailing dissidents and human rights activists, cracking down on religious groups and civil society organizations, suppressing local identity in Xinjiang, Tibet, and Hong Kong, and deepening digital surveillance, censorship, and propaganda—have been taken to strengthen the CCP’s control of leadership and its hegemony over society (Beckley and Brands, 2023 ; Béja, 2019 ). Internationally, in lieu of the low-profile policy stance advanced by Deng Xiaoping, China under Xi Jinping has adopted the “wolf warrior diplomacy” and become more assertive in consolidating control over offshore territorial claims, making incursions into Taiwan’s air defense identification zone, and engaging in a series of diplomatic conflicts with the United States over trade, human rights, intellectual property rights, and high technology (Miura, 2023 ; X Wu, 2023 ). As such, it is plausible that Chinese conservatives’ system-justifying tendencies spill over and relate to their domestic and foreign policy opinions. In brief, I contend that institutional trust mediates the association between political ideology and policy attitudes (ideology→institutional trust→domestic/foreign policy). Chinese conservatives’ preferences for authoritarian domestic policies and their preferences for hawkish foreign policies are, in part, explained by their higher levels of institutional trust (H4, H5). Moderating effects of threat-induced anxiety Threatening events, such as economic recessions and natural disasters, are a contextual factor that arouses public anxiety (Henderson and Oden, 2024 ). Generally, societal threats are categorized into two types: Internal (or endogenous) threats (e.g., economic stagnation and social immobility) are chronic and long-standing, whereas external (or exogenous) threats (e.g., pandemic diseases and terrorist attacks) are acute and unanticipated (Pizarro et al., 2024 ; You et al., 2024 ). Both exert negative influences over the public’s well-being, with the former more cumulative while the latter more immediate. 3 As Albertson and Gadarian ( 2015 ) point out, anxiety is a negatively valenced emotional reaction to (perceived) threat. People are prompted to cope with this negative emotion by endowing trust in the government and/or by supporting protective policies that are potentially anti-democratic or militaristic (Albertson and Gadarian, 2015 ; Henderson and Oden, 2024 ). Based on this strand of literature, I argue that threat-induced anxiety moderates the impacts of political ideology: In a time of high anxiety, individuals high (vs. low) in conservativism tend to reinforce their trust in the current system as well as their support for authoritarian-hawkish policies. As noted earlier, conservatives (vs. liberals) are characterized by greater needs for certainty and security, so they are more sensitive to threatening environments and feelings of anxiety. To reduce psychological discomfort, conservatives are thus motivated to double down on their pre-existing views. For instance, during the COVID-19 pandemic, conservatives (vs. liberals) are found to become more accepting of anti-democratic forms of governance (Pizarro et al., 2024 ), more antagonistic toward outgroup members (Gordils et al., 2021 ), more patriotic and anti-immigration (Rigoli, 2024 ), and more supportive of nationalistic policies (Su and Shen, 2021 ). In recent years, China has encountered a series of internal and external threats. On the one hand, rapid economic growth, better quality of life, and optimism about upward mobility—the hallmarks of China’s reform era—are disappearing (Minzner, 2018 ). Alongside economic slowdown, there has been a sharp increase of income and wealth disparity (Xie and Zhou, 2014 ; Zhao et al., 2024 ). Nowadays, the income share of the top 1% equals that of the bottom 50%, while the middle-class share of national wealth fell from 43% in 1980-95 to 26% in 2015 (Zhao et al., 2024 ). These problems have resulted in growing public concerns about intergenerational immobility, wealth and status loss, and economic insecurity more generally (Lei, 2020 ; Li et al., 2019 ). On the other hand, the COVID-19 pandemic emerged in Wuhan, Hubei and spread nationwide between December 2019 and early 2020, which posed an unprecedented threat to Chinese people’s physical health and lives. Owing to its rapid human-to-human transmission and the potential for fatality, this pandemic caused widespread public anxiety and panic, especially following the strict quarantine of Wuhan (Qiu et al., 2020 ). By February 2020, there were 74,675 confirmed COVID-19 cases and more than 2,000 deaths in China, about 780 million citizens were under travel restrictions, and cities outside of Hubei were subject to lockdowns (Chang et al., 2022 ; Qiu et al., 2020 ). Although many studies have analyzed how threat shapes Chinese public opinion (Cai, 2023 ; Lei, 2020 ; Lu et al., 2021 ; Wu et al., 2021 ; You et al., 2024 ), the issue of ideological asymmetries remains underexplored. Particularly, this omitted heterogeneity may explain why there are divergent findings. Take, for example, the impact of COVID-19 on political attitudes in China. While some research points to a higher level of popular support for the Chinese government (Cai, 2023 ; Wu et al., 2021 ), others uncover a decline in political trust (You et al., 2024 ) or a mix of positive and negative evaluations (Lu et al., 2021 ). The odds are that political ideology (conservatism) interacts with societal threat in affecting public opinion. Consequently, I hypothesize that the effects of conservatism on institutional trust, domestic policy preferences, and foreign policy preferences will increase given (internal/external) threat-induced anxiety (H6a, H7a, H8a/H6b, H7b, H8b). For clarity, I do not have a priori expectation regarding whether or how internal threat and external threat may function differentially, and I examine the two types of threat separately in the subsequent empirical sections. For internal threat, I focus on sociopolitical and economic problems like decline in employment and income (H6a, H7a, H8a). For external threat, I look at the COVID-19 pandemic (H6b, H7b, H8b). Table 1 outlines the hypotheses to be tested. Table 1 Outline of research hypotheses. H1 conservatism → authoritarian domestic policy H2 conservatism → hawkish foreign policy H3 conservatism → institutional trust H4 conservatism → institutional trust → authoritarian domestic policy H5 conservatism → institutional trust → hawkish foreign policy H6(a/b) conservatism × (internal/external) threat-induced anxiety → institutional trust H7(a/b) conservatism × (internal/external) threat-induced anxiety → authoritarian domestic policy H8(a/b) conservatism × (internal/external) threat-induced anxiety → hawkish foreign policy Data and measurement Data are drawn from the Netizens’ Social Awareness Survey (NSAS), a Chinese national survey administered by Professor Deyong Ma and his research team at Renmin University of China. The NSAS solicits responses regarding a variety of sociopolitical and economic issues from Internet users on WeChat (a WhatsApp-like messaging app), Sina Weibo (a Twitter/X-like social networking app), and Love Research (an online crowdsourcing platform akin to Amazon’s Mechanical Turk). Although this survey is not population-based, the demographic features (e.g., age, education, and gender) of its participants are found to be representative of Chinese Internet users accounting for more than 70% of the national population (Xia, 2022 ). A growing number of research has drawn on the NSAS in exploring public opinion and political orientation in China (Ma and Lewis, 2020 ; Ma and Lu, 2024 ; Wang, 2020 ; Xia, 2022 ; You et al., 2024 ). 4 In view of the same survey items and question wordings, I use the 2019 wave and the 2020 wave for hypothesis testing. There are 4,882 respondents in the former survey (administered in October 16–29, 2019) and 2,028 respondents in the latter one (administered in January 23-February 14, 2020). The two rounds of data were collected at an interval of about three months, with the second round conducted immediately after the onset of COVID-19. This helps to test H6b, H7b, and H8b concerning the moderating effects of anxiety caused by the COVID-19 pandemic, an unanticipated external threat. Dependent variables Domestic policy preferences. In H1, H4, and H7, the dependent variable of interest is preference for authoritarian domestic policies, tapped by three items in the 2019–2020 NSAS: (1) “Teachers are not allowed to criticize the party-state in the classroom,” (2) “Government censorship of news reports is necessary,” and (3) “Foreign news and social media websites must be blocked for too much harmful information.” Respondents were asked the extent to which they agreed with each of the statements on a five-point scale from 1 “strongly disagree” to 5 “strongly agree.” The three items are averaged into an index where higher scores indicate more authoritarian policy preferences (Cronbach’s α = 0.76, M = 3.60, SD = 0.88). Foreign policy preferences. In H2, H5, and H8, the dependent variable of interest is preference for hawkish foreign policies, tapped by three items in the 2019–2020 NSAS: (1) “If feasible, Taiwan should be reunified by force,” (2) “China has been too weak in handling territorial and trade disputes and should be more assertive,” and (3) “We must fight against the United States at all costs and never compromise in this trade war.” Respondents rated the extent to which they agreed with the statements on a five-point scale (1 = strongly disagree , 5 = strongly agree ). The three items are averaged into an index where higher scores indicate more hawkish policy preferences (Cronbach’s α = 0.67, M = 3.59, SD = 0.89). Institutional trust. In H3 and H6, the dependent variable of interest is institutional trust. Respondents assessed the extent to which they trusted the following eight institutions: the central government, local governments, hospitals, schools, state-run news media, public security bureaus, the police, and the courts (1 = don’t trust at all , 5 = trust a lot ). Item responses are averaged into an index where higher scores indicate higher levels of institutional trust (Cronbach’s α = 0.88, M = 3.93, SD = 0.67). At the same time, institutional trust is the intervening variable in H4 and H5. Key explanatory variable The key explanatory variable, conservatism, is composed of two subfactors: traditionalism and anti-egalitarianism. First, I draw on traditional Confucian values—filial piety and loyalty—for the operationalization of traditionalism. This subfactor is assessed with two items on a five-point scale: “Even if parents’ demands are unreasonable, children should do what they ask” and “It is taken for granted that subordinates loyally obey those of a higher-rank” (1 = strongly disagree , 5 = strongly agree ). Item responses are averaged into an index where higher scores indicate higher levels of traditionalism ( r = 0.53, M = 2.84, SD = 1.03). Second, anti-egalitarianism is tapped by two items: “People are born unequal, and let’s not change the unequal situation” and “The poor themselves are responsible for their poverty, which has nothing to do with the government’s policy.” Analogously, both are given on a five-point scale and are averaged into an index where higher scores indicate higher levels of anti-egalitarianism ( r = 0.51, M = 2.81, SD = 1.09). As expected, traditionalism and anti-egalitarianism are positively correlated ( r = 0.60), which points to the reasonability of combining them into a single scale. An additive index of conservatism is thus formed by averaging the two subfactors ( M = 2.83, SD = 0.95). Moderating variables According to H6, H7, and H8, the moderator is threat-induced anxiety. First, for internal threat (H6a, H7a, H8a), respondents rated, on a scale from 1 “strongly disagree” to 5 “strongly agree,” the degree to which they were anxious about the following issues: economic slowdown/unemployment/reduced income, substandard food and drugs, asset devaluation/confiscation, inadequate access to healthcare services, personal information leakage, regime instability, lack of social credit, erosion of mainstream values, worsened public safety, and worsened national security. The ten items are averaged into an index where higher scores indicate higher levels of anxiety (Cronbach’s α = 0.90, M = 3.52, SD = 1.00). Since these survey questions are not included in the 2020 wave, I only use the 2019 wave to test H6a, H7a, and H8a. Second, for external threat (H6b, H7b, H8b), I look at the COVID-19 outbreak. A year dummy—2020 (vs. 2019)—is generated to indicate whether respondents were exposed to the pandemic threat. It is expected that individuals interviewed in 2020, compared to those in 2019, had higher levels of anxiety due to the COVID-19 pandemic. I will shed more light on the reasonability of this operationalization in the subsequent statistical analysis. Control variables To present a more fully specified model, I add a battery of control variables: economic beliefs (support for private ownership), interpersonal trust, egocentric perception, sociotropic perception, political interest, official media use, foreign media use, age, gender (male), CCP membership, education, income, social class (upper/middle/lower), and the year dummy (2020 vs. 2019). Table 2 summarizes the descriptive statistics. Also see Supplementary Section A, Table A1 (2019 wave) and Table A2 (2020 wave) for two separate summary statistics. Table 2 Descriptive statistics (NSAS 2019–2020). Domestic policy preferences Mean Std. Dev. Min. Max. 3.60 0.88 1 5 Foreign policy preferences 3.59 0.89 1 5 Institutional trust 3.93 0.67 1 5 Conservatism 2.83 0.95 1 5 Anxiety (induced by internal threats) 3.52 1.00 1 5 2020 (vs. 2019) 0.29 0.46 0 1 Private ownership 3.13 1.17 1 5 Interpersonal trust 2.61 1.11 1 5 Egocentric perception 3.67 1.07 1 5 Sociotropic perception 3.90 0.99 1 5 Political interest 5.22 1.35 1 7 Official media use 0.83 0.38 0 1 Foreign media use 0.26 0.44 0 1 Age 2.60 1.64 1 9 Male 0.56 0.50 0 1 CCP membership 0.25 0.43 0 1 Education 4.38 1.04 1 6 Income 3.12 1.68 1 9 Lower class 0.28 0.45 0 1 Middle class 0.60 0.49 0 1 Upper class 0.11 0.32 0 1 Model and analysis For ease of interpretation, I treat the dependent variables as continuous and rely on ordinary least square (OLS) regression modeling. Residence dummies are added to correct for within-cluster correlation. To begin with, the three models in Table 3 are concerned with H1-H3. As expected, conservatism has a statistically significant ( p < 0.05) association with institutional trust and domestic/foreign policy preferences. The estimated coefficients are all positive, so Chinese citizens scoring higher on conservative ideology are more likely to support the current party-state system ( B = 0.17, SE = 0.01), authoritarian domestic policies ( B = 0.33, SE = 0.01), and hawkish foreign policies ( B = 0.23, SE = 0.01). Relative to other variables in Model 1, conservatism, the key explanatory variable, accounts for about 23.07% of all the information in predicting the outcome (Model 2: 41.04%; Model 3: 35.35%). Considering control variables, egocentric perception, sociotropic perception, political interest, and official media use are positively correlated with the three dependent variables, whereas foreign media use is negatively correlated with them. Graphical tests of normality are presented in Supplementary Section B, Figures A10-A12. The histograms of residuals, Q-Q plots, and P-P plots indicate that the residuals in Models 1–3 are approximately normally distributed, so OLS modeling is seen as appropriate. Table 3 OLS models analyzing effects of conservatism (NSAS 2019-20). Conservatism Institutional trust (1) Domestic policy (2) Foreign policy (3) 0.17* 0.33* 0.23* (0.01) (0.01) (0.01) 2020 (vs. 2019) 0.15* 0.03 -0.08* (0.02) (0.03) (0.03) Private ownership -0.06* -0.03* 0.02* (0.01) (0.01) (0.01) Interpersonal trust 0.02* -0.05* -0.05* (0.01) (0.01) (0.01) Egocentric perception 0.09* 0.05* 0.03* (0.01) (0.01) (0.01) Sociotropic perception 0.17* 0.18* 0.12* (0.01) (0.01) (0.01) Political interest 0.08* 0.09* 0.09* (0.01) (0.01) (0.01) Official media 0.16* 0.23* 0.21* (0.02) (0.03) (0.03) Foreign media -0.06* -0.13* -0.08* (0.02) (0.02) (0.02) Age -0.00 0.03* 0.04* (0.00) (0.01) (0.01) Male -0.08* -0.02 0.01 (0.01) (0.02) (0.02) CCP membership -0.03* 0.04 -0.05 (0.02) (0.02) (0.02) Education -0.00 -0.05* -0.02* (0.01) (0.01) (0.01) Income -0.01 -0.02* -0.01 (0.00) (0.01) (0.01) Upper class 0.04 0.09* 0.09* (vs. middle class) (0.02) (0.03) (0.03) Lower class -0.03 -0.03 -0.00 (vs. middle class) (0.02) (0.02) (0.02) R 2 0.32 0.33 0.23 Observations 6,910 6,910 6,910 Note : Standard errors in parentheses. Residence dummies and constants are omitted for space constraints. * p < 0.05 (two-tailed). Next, I employ structural equation modeling (SEM) to test H4 and H5. The major findings are illustrated in Fig. 1 (H4) and Fig. 2 (H5), and the full results are reported in Supplementary Section A, Table A4. Beyond the direct pathway linking conservatism to authoritarian-hawkish policy preferences, institutional trust, as manifested in Figs. 1 – 2 , is a significant factor in the indirect pathway that mediates this linkage. In other words, the association between Chinese citizens’ ideology and policy attitudes is partially explained by their trust in the current system. As hypothesized, Chinese conservatives’ system-justifying tendencies spill over and relate to their support for authoritarian domestic policies and hawkish foreign policies. Table 4 summarizes the effect sizes where the indirect effects are estimated by the Baron and Kenny’s approach. According to the Sobel’s test, both of the mediation effects are statistically significant at the 0.05 level. In detail, the mediation effect in Fig. 1 is 0.115 with a 95% confidence interval of [0.105, 0.125]; the mediation effect in Fig. 2 is 0.087 with a 95% confidence interval of [0.078, 0.097]. On average, institutional trust accounts for 32–35% of the total (direct plus indirect) effect identified via SEM. Table 4 . Summary of direct, indirect, and total effects in SEM. Direct effect Indirect effect Total effect Percent mediated Fig. 1 0.242 0.115 0.357 32% Fig. 2 0.163 0.087 0.250 35% Then, I draw on the 2019 wave of NSAS to test H6a, H7a, and H8a where internal threat-induced anxiety is a moderator. According to Model 4 in Table 5 , there exists a statistically significant and positive association between the interaction term ( Conservatism × Anxiety ) and institutional trust ( B = 0.02, SE = 0.01). In support of H6a, the impact of conservatism on institutional trust is conditional on anxiety induced by internal threats. When Chinese conservatives feel anxious about a series of internal threats, they will double down on their system-justifying tendencies and become more trusting of the existing institutions. As displayed in Fig. 3 , if the level of anxiety changes from 1 (min) to 5 (max), for example, the system-justifying effect of conservatism will increase by about 72.25% (effect size from 0.104 to 0.180). By contrast, the interaction effects in Model 5 and Model 6 are not statistically significant at the 0.05 level, so there is no empirical evidence for H7a and H8a. Overall, internal threat-induced anxiety, as hypothesized, reinforces the system-justifying effect of conservative ideology. Yet, it is less conclusive whether internal threat-induced anxiety moderates the relationships between conservatism and domestic/foreign policy opinions. Table 5 OLS models analyzing moderating effects of anxiety induced by internal threats (NSAS 2019). Conservatism Institutional trust (4) Domestic policy (5) Foreign policy (6) 0.09* 0.29* 0.22* (0.03) (0.04) (0.04) Anxiety -0.11* 0.02 0.07 (0.03) (0.03) (0.04) Conservatism × Anxiety 0.02* -0.01 -0.02 (0.01) (0.01) (0.01) Private ownership -0.02* 0.05* 0.07* (0.01) (0.01) (0.01) Interpersonal trust 0.02* -0.03* -0.02* (0.01) (0.01) (0.01) Egocentric perception 0.08* 0.04* 0.05* (0.01) (0.01) (0.01) Sociotropic perception 0.11* 0.12* 0.05* (0.01) (0.01) (0.02) Political interest 0.11* 0.12* 0.14* (0.01) (0.01) (0.01) Official media 0.16* 0.17* 0.16* (0.03) (0.04) (0.04) Foreign media 0.02 -0.00 0.02 (0.02) (0.02) (0.02) Age 0.03* 0.05* 0.09* (0.01) (0.01) (0.01) Male -0.06* -0.04 0.02 (0.02) (0.02) (0.02) CCP membership -0.02 0.02 -0.04 (0.02) (0.03) (0.03) Education 0.02 -0.02 0.01 (0.01) (0.01) (0.01) Income -0.01* -0.03* -0.02* (0.01) (0.01) (0.01) Upper class 0.01 0.07* 0.02 (vs. middle class) (0.03) (0.03) (0.04) Lower class -0.01 -0.02 0.03 (vs. middle class) (0.02) (0.02) (0.03) R 2 0.29 0.24 0.17 Observations 4,882 4,882 4,882 Note : Standard errors in parentheses. Residence dummies and constants are omitted for space constraints. * p < 0.05 (two-tailed). Concerning H6b, H7b, and H8b, I rely on an identification strategy in the unexpected event during survey design (Muñoz et al., 2020 ). Specifically, it assumes that the occurrence of an unexpected event assigns participants into the treatment/control group as if randomly so survey schedule (e.g., interview dates) can be regarded as an exogenous source of variation. In this case, the wave dummy (2020 vs. 2019) is proposed to be a moderating variable that reflects anxiety, albeit indirectly, triggered by the external threat of COVID-19 pandemic. On the other hand, although the outbreak of COVID-19 is unanticipated for both waves of respondents, individuals interviewed in 2019 (pre-outbreak) and those interviewed in 2020 (post-outbreak) may differ in background characteristics for reasons pertinent to sampling procedure. The odds are that imbalance on pre-treatment covariates, implicative of self-selection, undermines the assumption of as-if random assignment. In response to this concern about confounding, I employ coarsened exact matching (CEM), a data pre-processing technique that boosts balance on the observed covariates (i.e., age, gender, education, and party membership) while minimizing the “sparse-data bias” (Iacus et al., 2012 ; Mansournia et al., 2018 ). Table 6 . OLS models analyzing moderating effects of pandemic threat (NSAS 2019-20). Institutional trust (7) Domestic policy (8) Foreign policy (9) Conservatism 0.15* 0.31* 0.20* (0.01) (0.01) (0.01) 2020 (vs. 2019) 0.14* -0.19* -0.30* (0.05) (0.07) (0.08) Conservatism × 2020 0.01 0.08* 0.10* (0.02) (0.03) (0.03) Private ownership -0.02* -0.04* 0.02* (0.01) (0.01) (0.01) Interpersonal trust 0.02* -0.05* -0.07* (0.01) (0.01) (0.01) Egocentric perception 0.10* 0.04* 0.01 (0.01) (0.01) (0.01) Sociotropic perception 0.20* 0.21* 0.14* (0.01) (0.01) (0.01) Political interest 0.06* 0.04* 0.10* (0.01) (0.01) (0.01) Official media 0.18* 0.31* 0.16* (0.02) (0.03) (0.03) Foreign media -0.06* -0.17* -0.07* (0.02) (0.02) (0.02) Age -0.00 0.02* 0.04* (0.00) (0.01) (0.01) Male -0.08* 0.01 0.04* (0.01) (0.02) (0.02) CCP membership 0.02 0.08* -0.02 (0.02) (0.02) (0.02) Education -0.03* -0.08* -0.08* (0.01) (0.01) (0.01) Income -0.02* 0.01 -0.02* (0.00) (0.01) (0.01) Upper class 0.03 -0.00 -0.00 (vs. middle class) (0.02) (0.03) (0.03) Lower class -0.06* -0.06* -0.10* (vs. middle class) (0.02) (0.02) (0.03) R 2 0.34 0.32 0.21 Observations 6,514 6,514 6,514 Note: Standard errors in parentheses. Residence dummies and constants are omitted for space constraints. * p <0.05 (two-tailed). Table 6 presents the weighted regression models. The interaction terms ( Conservatism × 2020 ) in Model 8 ( B = 0.08, SE = 0.03) and Model 9 ( B = 0.10, SE = 0.03) are statistically significant and positively signed, which lend credence to H7b and H8b. Under the pandemic threat, the associations between conservatism and domestic/foreign policy preferences in 2020 get stronger compared to those in 2019. Figure 4 depicts the marginal effects plots where the slope coefficients for 2020 are much larger than those for 2019. Although the estimated effects of conservatism on policy attitudes are positive in both waves, those in 2020 (vs. 2019) increase by 26.29% for domestic policy (effect size from 0.312 to 0.394) and by 47.89% for foreign policy (effect size from 0.200 to 0.296). However, since the interaction effect in Model 7 is not statistically significant at the 0.05 level, there is no empirical evidence for H6b. It is thus inconclusive whether the relationship between conservatism and institutional trust depends on the pandemic threat. Taken together, the moderating effects of internal threat- and external threat-induced anxiety reveal distinct patterns. On the one hand, the correlation between conservatism and institutional trust increases given internal threat, but not external threat. On the other hand, the correlation between conservatism and domestic/foreign policy preferences increases given external threat, but not internal threat. In the meantime, I perform a set of robustness checks. First, I use traditionalism and anti-egalitarianism, the subcomponents of conservatism, for hypothesis testing. Second, I employ causal mediation analysis (Keele et al., 2015 ), rather than structural equation analysis, to test whether institutional trust plays a mediating role. Third, to alleviate concerns about omitted variable bias, I rely on sensitivity analysis tools developed by Cinelli and Hazlett ( 2020 ) and quantify how strong a confounder would have to be so as to explain away the effects of conservatism. Alternatively, I select two instrumental variables (IVs)—intolerance of ambiguity and need for order/structure—and utilize two-stage least squares (2SLS) regression. Fourth, with regard to the multiplicative interaction terms, I employ Hainmueller et al. (2019)’s binning estimation to check whether the conditional marginal effects are model dependent and subject to excessive interpolation or extrapolation. Fifth, instead of least squares regression, I draw on (inferential) Lasso regression and maximum agreement regression (Bottai et al., 2022 ) for statistical analysis. Beyond internal validity, following Frank and Min ( 2007 ), I further assess the extent to which the sampling process would have to be biased in order to overturn the research conclusions. Lastly, I use data from the 2017–2018 NSAS to validate the results based on the 2019–2020 NSAS. All of the above-mentioned analyses are reported and elaborated in Supplementary Section C. On the whole, the empirical findings are relatively robust to different modeling and specification techniques. Conclusion and discussion For the sake of between-country comparison, this article draws on a psychological approach to political ideology in contemporary China and defines conservatism (vs. liberalism) as a combination of two elements: traditionalism and anti-egalitarianism. Analyses of the 2019–2020 NSAS shows that Chinese conservatives are, by and large, similar to their WEIRD counterparts in terms of system-justifying tendencies and domestic/foreign policy preferences. First, Chinese citizens who endorse conservative ideology are prone to support authoritarian policies at home and hawkish policies abroad (H1, H2). Second, conservatism plays a system-justifying role in that individuals high in conservatism are more trusting of the current institutions led by the CCP (H3). Third, given the party-state’s intensified repression and assertiveness in the era of Xi Jinping, Chinese conservatives’ institutional trust spills over and partially explains their support for authoritarian-hawkish policies (H4, H5). Fourth, under the circumstances of (internal/external) threat-induced anxiety, Chinese citizens scoring higher on conservativism are motivated to double down on their attachment to the CCP-dominated institutional status quo (H6a) and authoritarian-hawkish policies (H7b, H8b), resulting in significant interaction effects. This study also bears implications on authoritarian resilience and democratization in post-COVID China. As continuous economic slowdown undermines the CCP’s performance-based legitimacy, some scholars point out that public discontent and anxiety about declining living standards and economic well-being will, over time, give rise to anti-government protests, political instability, and a potential path toward democracy (Beckley and Brands, 2023 ). However, empirical evidence for H6a indicates the heterogeneity of Chinese public opinion. For individuals high in conservatism, growing concerns about a series of internal threats (e.g., economic slowdown and unemployment) prompt them to reduce psychological discomfort on behalf of the existing system, manifested by a stronger association between conservatism and institutional trust. In this regard, conservative ideology can be a barrier to mass mobilization and regime change in China. Despite the CCP’s diminishing performance, individuals on the political right seem to be “committed” system justifiers when they feel anxious. On the other hand, this article has limitations worthy of further reflection and investigation. To begin with, the two types of threat-induced anxiety are found to exert differential influences. For internal threat, there are no significant interaction effects on domestic and foreign policy preferences (no evidence for H7a, H8a). For external threat, there is no significant interaction effect on institutional trust (no evidence for H6b). Notably, as identified in Model 7 (Table 5 ), the coefficient of wave dummy 2020 is statistically significant and positively signed. That is, the COVID-19 pandemic, an unanticipated external threat, brings about a rally-round-the-flag effect where Chinese people are pushed to unite in favor of the CCP leadership regardless of their ideological positions. Future work may try to shed light on the differences between internal and external threats and on their distinct correlations with political ideology or public opinion. Moreover, system threat engenders feelings of not merely anxiety but also anger, two emotions predictive of different attitudinal and behavioral outcomes. For instance, based on a survey experiment in Germany, Auer and Freitag ( 2024 ) find that (economic and pandemic) threat-induced angry fosters populist attitudes and reduces political support, especially when the established political system is perceived to be responsible. Concerning China’s zero-COVID containment policy between 2019 and 2022, Liu et al. ( 2024 ) reveal a pronounced shift in public sentiment: from initial support (due to fear of the virus) to increasing disappointment and dissatisfaction (due to mental/physical strain from frequent testing and prolonged lockdowns). These negative anger-related emotions culminated in distrust in the government and widespread protests, known as the White Paper Movement (Guan et al., 2025 ). It is thus worthwhile to assess whether threat-induced anger plays a (dis)similar role as threat-induced anxiety in moderating the ideology-political attitude relationships. Finally, in light of cross-sectional data, the research conclusions should be interpreted with caution for causal inference. It is plausible that some omitted variables may confound the correlational findings. Particularly, in testing the moderating effects of COVID threat, despite the application of CEM, data imbalance in unobserved characteristics, such as unequal sampling accessibility between the two rounds of participants, may still exist. Besides, the NSAS data were collected through non-probability samples biased toward young, educated Internet users in urban China. Nonetheless, given politically sensitive questions, online anonymous surveys, compared to face-to-face interviews, can mitigate respondents’ intention to offer socially desirable responses (Huang and Yeh, 2019 ). Online samples in China are also found to be valid in recent empirical studies (Li et al., 2018 ). In the future, longitudinal and/or experimental research, drawing on nationally representative data, can unpack the causal mechanism linking ideology to political attitudes in a more rigorous way. For example, by use of six panel surveys, Vishwanath ( 2025 ) finds that traditionalism and egalitarianism are two significant causal predictors of American voters’ attitudes toward welfare and transgender policies. Declarations Author Contribution The research is conducted solely by G.C., who is responsible for the following: conceptualization of the study, designing the methodology, data analysis, and interpretation. G.C. also prepared and revised the manuscript for publication, approved the final version, and takes full responsibility for the accuracy, integrity, and completeness of the work. 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J Chin Polit Sci 29(1):1–29 Zhao SXB, Wong DWH, Shao CH, Liu KM (2024) Rising income and wealth inequality in China: empirical assessments and theoretical reflections. J Contemp China 33(147):544–559 Footnotes Pan and Xu ( 2018 ) argue that nationalism is a third dimension. Ma and Lewis ( 2020 ) argue that assessment of Mao Zedong’s legacy is a third dimension. Symmetrically, Chinese liberalism is composed of anti-traditionalism and egalitarianism. Although internal threat is, in theory, distinguished from external threat, they may also be interconnected, such as a longer-run slowdown in economic growth caused by a global health crisis. Empirically, both internal and external threats are found to trigger public anxiety and shape political attitudes (Albertson and Gadarian, 2015 ; Henderson and Oden, 2024 ). Data on the Netizens’ Social Awareness Survey are available online at http://www.cnsda.org/ , with the permission of Chinese National Survey Data Archive. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9443334","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":634153275,"identity":"48179f0e-0c9e-4cda-975e-1e4829c0198d","order_by":0,"name":"Gong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3OsQrCMBCA4SuFuAS6JtS+QyBQF6mvogR0Kbh2LBTq5qzoQxQE50CgLn0ABwel0MlFEHFTWxwl1s0h/3IE7uMCYDL9Y+Q9HbCO9bTi1oTGNvuRMNmWOKskP12jQ7DZCVRi6HuZtKuj9sghn/BuUYltUXY4hjHPJOoxHWEk9F2aKuHvp7mLQY0yiRHRk+mtIXwh0Is82pAQ0UuqAkYaIr8Tsh/7LhRqSIoS0TUTfKmQryXOQlT0HqmBMxOInKPAm++SSkvqbAwwimFY/7N+ftt/Zd0BBtAQk8lkMn3oCdqIRpGuHPEmAAAAAElFTkSuQmCC","orcid":"","institution":"Qufu Normal University","correspondingAuthor":true,"prefix":"","firstName":"Gong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-04-17 03:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9443334/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9443334/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108675715,"identity":"2201612e-cae9-4832-8082-9e1ace8228a2","added_by":"auto","created_at":"2026-05-07 08:30:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22441,"visible":true,"origin":"","legend":"\u003cp\u003eSEM linking conservativism to domestic policy preferences (NSAS 2019-20).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Entries are unstandardized path coefficients. Other variables and constants are omitted for space constraints. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 (two-tailed).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9443334/v1/804a19f732d01cbd2ab0f3ad.png"},{"id":108806987,"identity":"e42f6939-bb65-4186-8b89-cfb72a8fc8b9","added_by":"auto","created_at":"2026-05-08 15:29:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22741,"visible":true,"origin":"","legend":"\u003cp\u003eSEM linking conservativism to foreign policy preferences (NSAS 2019-20).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Entries are unstandardized path coefficients. Other variables and constants are omitted for space constraints. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 (two-tailed).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9443334/v1/014e703c212e6d9fc4f51f9a.png"},{"id":108675717,"identity":"3474b5ed-e6c4-4d45-b0b8-b7a04d59e3e3","added_by":"auto","created_at":"2026-05-07 08:30:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72621,"visible":true,"origin":"","legend":"\u003cp\u003eModerating effects of anxiety (induced by internal threats).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eThe solid line represents marginal effects, and the dashed lines indicate 95% confidence intervals. Figure 3 is based on Model 4 in Table 5.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9443334/v1/976ae445b224cd0df9c31ac9.png"},{"id":108806637,"identity":"0055a07a-fbaa-4383-8650-b0bf861bcc63","added_by":"auto","created_at":"2026-05-08 15:29:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154746,"visible":true,"origin":"","legend":"\u003cp\u003eModerating effects of pandemic threat (NSAS 2019-20).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eThe shaded areas indicate 95% confidence intervals. Figures 4a and 4b are based on Models 8 and 9 in Table 6 respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9443334/v1/6942adfc7bbc61bfad9556bc.png"},{"id":108810004,"identity":"681f48a9-60a0-439f-ac7b-512df7725de6","added_by":"auto","created_at":"2026-05-08 15:56:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1036040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9443334/v1/f2bcf75d-ec2a-4d9c-b3c4-83907650e8aa.pdf"},{"id":108805373,"identity":"07328378-e0f4-47ad-916d-48f9e7610f91","added_by":"auto","created_at":"2026-05-08 15:25:45","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1241600,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.doc","url":"https://assets-eu.researchsquare.com/files/rs-9443334/v1/d2af989618c054b61ae4c8d5.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"When system justifiers feel anxious: conservatism, institutional trust, and policy preferences in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite a variety of stimulus measures, China\u0026rsquo;s post-COVID economy shows few signs of recovery due to continuing headwinds, including the deflation of a massive real estate bubble, local government off-balance-sheet debt, foreign capital outflows, excess capacity in manufacturing, high youth unemployment, and weak consumer and investor confidence (Horowitz, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Piao and Cui, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Because the Chinese Communist Party (CCP), to a large extent, bases its legitimacy on economic performance, it is expected that Chinese people, under increasingly harsher conditions, will become anxious and pessimistic about their economic well-being, skeptical and critical of the government, and even willing to struggle for civil liberties and political rights (Alisky et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Beckley and Brands, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, popular reactions to system threat in general and poor regime performance in particular can also be contingent. For example, according to Neundorf et al.\u0026rsquo;s (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) study of Turkey, during a severe economic crisis, government voters, compared to non-government voters, were less likely to defect from the ruling regime party (Justice and Development Party, AKP) and more likely to remain loyal to it. Indeed, for those strongly attached to the AKP, negative economic evaluations enhanced the probability of voting again for it in the subsequent election. Although China, unlike Turkey and other electoral autocracies/democracies, is a single-party authoritarian regime that lacks partisan competition, this article attempts to explore whether and how ideology (i.e., conservatism) shapes Chinese people\u0026rsquo;s political attitudes (i.e., institutional trust and domestic/foreign policy preferences), especially in times of societal threat and public anxiety.\u003c/p\u003e \u003cp\u003eExtending Jost\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) political psychological model from the West to China, right/conservative and left/liberal ideologies are differentiated by two fundamental tradeoffs: (1) tradition versus progress and (2) hierarchy versus equality. With a focus on the right (vs. left) in this paper, I define conservatism (vs. liberalism) in contemporary China as a combination of traditionalism (vs. anti-traditionalism) and anti-egalitarianism (vs. egalitarianism). Drawing on the psychological approach to political ideology, I argue that Chinese conservatives, because of their acceptance of traditional values and social inequalities, tend to support authoritarian policies at home and hawkish policies abroad. Additionally, conservatism is a system-justifying ideology that satisfies people\u0026rsquo;s needs for certainty and security. As such, individuals who endorse conservative ideology are hypothesized to place more trust in the current institutions led by the CCP. Given the party-state\u0026rsquo;s increasingly repressive and assertive policies in the era of Xi Jinping, I further expect institutional trust to be a mediator that partially accounts for the link between ideology and domestic/foreign policy opinions. More importantly, under the circumstances of (internal/external) threat-induced anxiety, Chinese citizens scoring higher on conservatism are motivated to double down on their attachment to the CCP-dominated institutional status quo and authoritarian-hawkish policies. In other words, threat-induced anxiety is suggested to be a moderator that reinforces the effects of conservatism. Analyses of the Netizens\u0026rsquo; Social Awareness Survey (2019\u0026ndash;2020) are largely supportive of the above arguments.\u003c/p\u003e \u003cp\u003eThis article contributes to the existing literature in three ways. First, the psychological approach to political ideology is not regime- or culture-specific, which facilitates between-country comparison. To date, much of the research has employed preferences over political (liberal-democratic vs. CCP-dictatorial) and economic (market-oriented vs. state-directed) policy issues to measure the mass public\u0026rsquo;s ideology in China (Beattie et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma and Lewis, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pan and Xu, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wu and Meng, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This regime-specific operationalization, although widely adopted in China studies, may lead to inconsistent findings and hinder comparative studies. Contrarily, as psychological dispositions, unlike political or economic policy opinions, are pre-political or non-political (Jost, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the psychological approach to ideology (tradition vs. progress, hierarchy vs. equality) helps to explore the degree to which Chinese conservatives/liberals are (dis)similar to their Western and democratic counterparts.\u003c/p\u003e \u003cp\u003eSecond, this work engages with the studies on system justification in China. An emerging literature in social/political psychology has probed system-justifying beliefs among the Chinese public. Some revolve around the impacts of system justification on life satisfaction and corruption perception (Li, Wu, and Kou, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while others round the antecedents of system justification like socioeconomic status and conspiracy mentality (Li, Yang, Wu, and Kou, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Valdes et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To the best of my knowledge, this is the first study examining both the causes (political ideology) and consequences (domestic/foreign policy attitudes) of system justification, embodied by Chinese citizens\u0026rsquo; trust toward the current institutions. More specifically, institutional trust is proposed to be a mediator linking ideology to policy attitudes.\u003c/p\u003e \u003cp\u003eThird, apart from individual-level ideological factors, this research deems public anxiety induced by internal/external threats to be a moderator and analyzes the contextual variability in ideology-political attitude relationships. In particular, the majority of previous research about ideological asymmetries in response to system threat is based on Western, educated, industrialized, rich, and democratic (WEIRD; Henrich et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) cases, which makes the inference, at least geographically speaking, narrowly defined. After all, collective threats, such as national economic downturns (internal) and global public health crises (external), are potential problems that all states may face. To expand the scope and boundary conditions, this study distinguishes between internal and external threats and moves beyond Euro-American-centrism with samples obtained from China, a non-WEIRD society.\u003c/p\u003e"},{"header":"A psychological approach to political ideology in China","content":"\u003cp\u003eIn a communist regime short of electoral accountability and multiparty competition while transitioning from a centrally planned economy to an open market economy, many Chinese people find it difficult to place them on a left-right spectrum, a common measure of political ideology in democracies (JY Wu, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Considering that the conventional labels of left and right lack a coherent programmatic meaning, the extant literature suggests that China\u0026rsquo;s ideological divide, distinct from that in the West, is organized politically around competitive democracy versus single-party dictatorship and economically around free-market capitalism versus government intervention in the economy (Beattie et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma and Lewis, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pan and Xu, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wu and Meng, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003csup\u003e1\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e Yet, the empirical findings are not conclusive.\u003c/p\u003e\n\u003cp\u003eStudies drawing on online samples find that the political dimension and the economic dimension are highly correlated, so a combined political-economic framework can be used to explain ideological cleavage in China (Beattie et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma and Lewis, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pan and Xu, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While the \u0026ldquo;liberal right\u0026rdquo; embraces liberal democracy and market economy, the \u0026ldquo;conservative left\u0026rdquo; sticks to single-party rule and state intervention. Conversely, Wu and Meng (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reveal that the political dimension is largely orthogonal to the economic one in urban China. For example, individuals who are pro-market do not necessarily prefer political democracy to dictatorship. Likewise, through in-depth interviews, Mulvad (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) points out that China\u0026rsquo;s ideological spectrum is not one-dimensional but two-dimensional, resulting in four ideal typical ideological quadrants (2 political \u0026times; 2 economic).\u003c/p\u003e\n\u003cp\u003eDiffering from past work that utilizes preferences over political-economic policy issues to capture ideology in China, this research engages with the debate from a psychological perspective. According to Jost (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), political ideology originates from basic, underlying psychological dispositions that are pre-political or non-political: Tradeoffs regarding (1) tradition versus progress and (2) hierarchy versus equality are the core differences between the right/conservative and the left/liberal. On average, conservatives prefer tradition and hierarchy, whereas liberals are in favor of progress and equality. Notably, the psychological approach to political ideology is not regime- or culture-specific, which facilitates between-country comparison. For this reason, following Jost (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), I theorize that China\u0026rsquo;s ideological divide can also be characterized by the above psychological tradeoffs. With a focus on the political right, I argue that Chinese conservatism, like its Western and democratic counterpart, is composed of two elements: traditionalism and anti-egalitarianism.\u003csup\u003e2\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e\u003c/p\u003e"},{"header":"Conservativism and domestic/foreign policy preferences","content":"\u003cp\u003eAs Jost (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) points out, there exists a slippery slope from conservatism to authoritarianism. In Western societies, people on the political right (vs. left) generally support policies limiting gay rights and abortion while maintaining traditional gender roles and family structures where the husband is the provider and the wife, subordinate to the husband, is the homemaker. For conservatives, traditional and hierarchical relations between men and women, husbands and wives, and parents and children are a central concern (Marietta, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Hence, attachment to tradition and hierarchy makes them object to same-sex marriage, gender equality, and feminism. Extending from gender and family to broader social issues, political intolerance/authoritarianism is also more endemic to conservatives than liberals. For psychological reasons, conservatives (vs. liberals) are less willing to extend civil rights to those with whom they disagree (Altemeyer, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Davis and Silver, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sniderman et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Across liberal and electoral democracies, conservativism, albeit to varying degrees, is connected with political intolerance/authoritarianism in general and with homophobia, misogyny, and xenophobia in particular (Norris and Inglehart, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eEmpirical studies have consistently indicated such ideological asymmetries in public policy preferences. For instance, during the 2016 US presidential election, Republican voters, compared to their Democratic counterparts, scored higher on authoritarianism (Womick et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Particularly, Trump supporters were more likely to believe that \u0026ldquo;what our country needs instead of more civil rights is a good stiff dose of law and order\u0026rdquo; and that only a strong, determined leader can \u0026ldquo;crush the evil and set us in our right way again.\u0026rdquo; Unlike liberals, conservatives tend to endorse censorship across messages of different political content, an anti-democratic policy that restricts individual freedoms (Fisher et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Lindner and Nosek, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Facing a tradeoff between civil liberties and security, liberals are found to favor the former while conservatives the latter (Davis and Silver, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eConservatives are oriented not only to authoritarian policies in domestic affairs but also to hawkish policies in foreign affairs. A good deal of evidence has demonstrated that conservatives (vs. liberals) are more inclined toward militant internationalism (Brewer and Steenbergen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), more hostile toward foreign outgroups (De Zavala et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and more supportive of competitive foreign policies (Binning, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). On the basis of these findings, I argue that Chinese people who endorse conservative (traditional and anti-egalitarian) ideologies, similar to their Western counterparts, tend to favor policies on the right. That is, individuals high in conservatism are more likely to support authoritarian policies domestically (H1) and hawkish policies internationally (H2).\u003c/p\u003e"},{"header":"Conservatism as a system-justifying ideology in China","content":"\u003cp\u003eAccording to Jost et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), epistemic motives to mitigate uncertainty and existential motives to assuage insecurity are two fundamental antecedents of conservative and right-wing beliefs. In comparison to liberals, conservatives are characterized by greater needs for (epistemic) certainty and (existential) security. Meanwhile, system justification, such as generalized trust in existing political, economic, and social institutions, serves a palliative function in addressing people\u0026rsquo;s epistemic and existential concerns (Jost, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As a consequence, those on the political right (vs. left) are prone to bolster and rationalize the institutional status quo (Jost, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The positive correlation between conservatism and system justification or institutional trust has been observed longitudinally in the United States (Jost et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and cross-sectionally in Argentina, Finland, Germany, Hungary, Latvia, Lebanon, New Zealand, Poland, Sweden, and the United Kingdom (Jost, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the case of China, I likewise posit that individuals high in conservatism are more likely to trust institutions led by the CCP (H3).\u003c/p\u003e \u003cp\u003eOn top of that, I expect institutional trust to partially account for Chinese conservatives\u0026rsquo; policy preferences because the party-state\u0026rsquo;s domestic and foreign policies in recent years\u0026mdash;featured by mounting authoritarianism and nationalism\u0026mdash;are mostly favored by these right-wing citizens. Since Xi Jinping took office, China has become increasingly repressive at home and aggressive abroad (Beckley and Brands, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Economy, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Domestically, departing from the liberalizing reform in post-Mao China, a variety of coercive measures\u0026mdash;jailing dissidents and human rights activists, cracking down on religious groups and civil society organizations, suppressing local identity in Xinjiang, Tibet, and Hong Kong, and deepening digital surveillance, censorship, and propaganda\u0026mdash;have been taken to strengthen the CCP\u0026rsquo;s control of leadership and its hegemony over society (Beckley and Brands, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; B\u0026eacute;ja, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Internationally, in lieu of the low-profile policy stance advanced by Deng Xiaoping, China under Xi Jinping has adopted the \u0026ldquo;wolf warrior diplomacy\u0026rdquo; and become more assertive in consolidating control over offshore territorial claims, making incursions into Taiwan\u0026rsquo;s air defense identification zone, and engaging in a series of diplomatic conflicts with the United States over trade, human rights, intellectual property rights, and high technology (Miura, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; X Wu, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As such, it is plausible that Chinese conservatives\u0026rsquo; system-justifying tendencies spill over and relate to their domestic and foreign policy opinions.\u003c/p\u003e \u003cp\u003eIn brief, I contend that institutional trust mediates the association between political ideology and policy attitudes (ideology\u0026rarr;institutional trust\u0026rarr;domestic/foreign policy). Chinese conservatives\u0026rsquo; preferences for authoritarian domestic policies and their preferences for hawkish foreign policies are, in part, explained by their higher levels of institutional trust (H4, H5).\u003c/p\u003e"},{"header":"Moderating effects of threat-induced anxiety","content":"\u003cp\u003eThreatening events, such as economic recessions and natural disasters, are a contextual factor that arouses public anxiety (Henderson and Oden, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Generally, societal threats are categorized into two types: Internal (or endogenous) threats (e.g., economic stagnation and social immobility) are chronic and long-standing, whereas external (or exogenous) threats (e.g., pandemic diseases and terrorist attacks) are acute and unanticipated (Pizarro et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; You et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Both exert negative influences over the public\u0026rsquo;s well-being, with the former more cumulative while the latter more immediate.\u003csup\u003e3\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e As Albertson and Gadarian (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) point out, anxiety is a negatively valenced emotional reaction to (perceived) threat. People are prompted to cope with this negative emotion by endowing trust in the government and/or by supporting protective policies that are potentially anti-democratic or militaristic (Albertson and Gadarian, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Henderson and Oden, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBased on this strand of literature, I argue that threat-induced anxiety moderates the impacts of political ideology: In a time of high anxiety, individuals high (vs. low) in conservativism tend to reinforce their trust in the current system as well as their support for authoritarian-hawkish policies. As noted earlier, conservatives (vs. liberals) are characterized by greater needs for certainty and security, so they are more sensitive to threatening environments and feelings of anxiety. To reduce psychological discomfort, conservatives are thus motivated to double down on their pre-existing views. For instance, during the COVID-19 pandemic, conservatives (vs. liberals) are found to become more accepting of anti-democratic forms of governance (Pizarro et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), more antagonistic toward outgroup members (Gordils et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), more patriotic and anti-immigration (Rigoli, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and more supportive of nationalistic policies (Su and Shen, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn recent years, China has encountered a series of internal and external threats. On the one hand, rapid economic growth, better quality of life, and optimism about upward mobility\u0026mdash;the hallmarks of China\u0026rsquo;s reform era\u0026mdash;are disappearing (Minzner, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Alongside economic slowdown, there has been a sharp increase of income and wealth disparity (Xie and Zhou, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nowadays, the income share of the top 1% equals that of the bottom 50%, while the middle-class share of national wealth fell from 43% in 1980-95 to 26% in 2015 (Zhao et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These problems have resulted in growing public concerns about intergenerational immobility, wealth and status loss, and economic insecurity more generally (Lei, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, the COVID-19 pandemic emerged in Wuhan, Hubei and spread nationwide between December 2019 and early 2020, which posed an unprecedented threat to Chinese people\u0026rsquo;s physical health and lives. Owing to its rapid human-to-human transmission and the potential for fatality, this pandemic caused widespread public anxiety and panic, especially following the strict quarantine of Wuhan (Qiu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By February 2020, there were 74,675 confirmed COVID-19 cases and more than 2,000 deaths in China, about 780 million citizens were under travel restrictions, and cities outside of Hubei were subject to lockdowns (Chang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qiu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAlthough many studies have analyzed how threat shapes Chinese public opinion (Cai, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lei, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; You et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the issue of ideological asymmetries remains underexplored. Particularly, this omitted heterogeneity may explain why there are divergent findings. Take, for example, the impact of COVID-19 on political attitudes in China. While some research points to a higher level of popular support for the Chinese government (Cai, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), others uncover a decline in political trust (You et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or a mix of positive and negative evaluations (Lu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The odds are that political ideology (conservatism) interacts with societal threat in affecting public opinion. Consequently, I hypothesize that the effects of conservatism on institutional trust, domestic policy preferences, and foreign policy preferences will increase given (internal/external) threat-induced anxiety (H6a, H7a, H8a/H6b, H7b, H8b). For clarity, I do not have a priori expectation regarding whether or how internal threat and external threat may function differentially, and I examine the two types of threat separately in the subsequent empirical sections. For internal threat, I focus on sociopolitical and economic problems like decline in employment and income (H6a, H7a, H8a). For external threat, I look at the COVID-19 pandemic (H6b, H7b, H8b). Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the hypotheses to be tested.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOutline of research hypotheses.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003econservatism \u0026rarr; authoritarian domestic policy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003econservatism \u0026rarr; hawkish foreign policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003econservatism \u0026rarr; institutional trust\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003econservatism \u0026rarr; institutional trust \u0026rarr; authoritarian domestic policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003econservatism \u0026rarr; institutional trust \u0026rarr; hawkish foreign policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eH6(a/b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003econservatism \u0026times; (internal/external) threat-induced anxiety \u0026rarr; institutional trust\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eH7(a/b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003econservatism \u0026times; (internal/external) threat-induced anxiety \u0026rarr; authoritarian domestic policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eH8(a/b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003econservatism \u0026times; (internal/external) threat-induced anxiety \u0026rarr; hawkish foreign policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Data and measurement","content":"\u003cp\u003eData are drawn from the Netizens\u0026rsquo; Social Awareness Survey (NSAS), a Chinese national survey administered by Professor Deyong Ma and his research team at Renmin University of China. The NSAS solicits responses regarding a variety of sociopolitical and economic issues from Internet users on WeChat (a WhatsApp-like messaging app), Sina Weibo (a Twitter/X-like social networking app), and Love Research (an online crowdsourcing platform akin to Amazon\u0026rsquo;s Mechanical Turk). Although this survey is not population-based, the demographic features (e.g., age, education, and gender) of its participants are found to be representative of Chinese Internet users accounting for more than 70% of the national population (Xia, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A growing number of research has drawn on the NSAS in exploring public opinion and political orientation in China (Ma and Lewis, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ma and Lu, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xia, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; You et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003csup\u003e4\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIn view of the same survey items and question wordings, I use the 2019 wave and the 2020 wave for hypothesis testing. There are 4,882 respondents in the former survey (administered in October 16\u0026ndash;29, 2019) and 2,028 respondents in the latter one (administered in January 23-February 14, 2020). The two rounds of data were collected at an interval of about three months, with the second round conducted immediately after the onset of COVID-19. This helps to test H6b, H7b, and H8b concerning the moderating effects of anxiety caused by the COVID-19 pandemic, an unanticipated external threat.\u003c/p\u003e\n\u003ch3\u003eDependent variables\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eDomestic policy preferences.\u003c/em\u003e In H1, H4, and H7, the dependent variable of interest is preference for authoritarian domestic policies, tapped by three items in the 2019\u0026ndash;2020 NSAS: (1) \u0026ldquo;Teachers are not allowed to criticize the party-state in the classroom,\u0026rdquo; (2) \u0026ldquo;Government censorship of news reports is necessary,\u0026rdquo; and (3) \u0026ldquo;Foreign news and social media websites must be blocked for too much harmful information.\u0026rdquo; Respondents were asked the extent to which they agreed with each of the statements on a five-point scale from 1 \u0026ldquo;strongly disagree\u0026rdquo; to 5 \u0026ldquo;strongly agree.\u0026rdquo; The three items are averaged into an index where higher scores indicate more authoritarian policy preferences (Cronbach\u0026rsquo;s \u0026alpha;\u0026thinsp;=\u0026thinsp;0.76, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.60, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.88).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eForeign policy preferences.\u003c/em\u003e In H2, H5, and H8, the dependent variable of interest is preference for hawkish foreign policies, tapped by three items in the 2019\u0026ndash;2020 NSAS: (1) \u0026ldquo;If feasible, Taiwan should be reunified by force,\u0026rdquo; (2) \u0026ldquo;China has been too weak in handling territorial and trade disputes and should be more assertive,\u0026rdquo; and (3) \u0026ldquo;We must fight against the United States at all costs and never compromise in this trade war.\u0026rdquo; Respondents rated the extent to which they agreed with the statements on a five-point scale (1\u0026thinsp;=\u0026thinsp;\u003cem\u003estrongly disagree\u003c/em\u003e, 5\u0026thinsp;=\u0026thinsp;\u003cem\u003estrongly agree\u003c/em\u003e). The three items are averaged into an index where higher scores indicate more hawkish policy preferences (Cronbach\u0026rsquo;s \u0026alpha;\u0026thinsp;=\u0026thinsp;0.67, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.59, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.89).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInstitutional trust.\u003c/em\u003e In H3 and H6, the dependent variable of interest is institutional trust. Respondents assessed the extent to which they trusted the following eight institutions: the central government, local governments, hospitals, schools, state-run news media, public security bureaus, the police, and the courts (1\u0026thinsp;=\u0026thinsp;\u003cem\u003edon\u0026rsquo;t trust at all\u003c/em\u003e, 5\u0026thinsp;=\u0026thinsp;\u003cem\u003etrust a lot\u003c/em\u003e). Item responses are averaged into an index where higher scores indicate higher levels of institutional trust (Cronbach\u0026rsquo;s \u0026alpha;\u0026thinsp;=\u0026thinsp;0.88, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.93, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.67). At the same time, institutional trust is the intervening variable in H4 and H5.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eKey explanatory variable\u003c/h2\u003e\n \u003cp\u003eThe key explanatory variable, conservatism, is composed of two subfactors: traditionalism and anti-egalitarianism. First, I draw on traditional Confucian values\u0026mdash;filial piety and loyalty\u0026mdash;for the operationalization of traditionalism. This subfactor is assessed with two items on a five-point scale: \u0026ldquo;Even if parents\u0026rsquo; demands are unreasonable, children should do what they ask\u0026rdquo; and \u0026ldquo;It is taken for granted that subordinates loyally obey those of a higher-rank\u0026rdquo; (1\u0026thinsp;=\u0026thinsp;\u003cem\u003estrongly disagree\u003c/em\u003e, 5\u0026thinsp;=\u0026thinsp;\u003cem\u003estrongly agree\u003c/em\u003e). Item responses are averaged into an index where higher scores indicate higher levels of traditionalism (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.84, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.03). Second, anti-egalitarianism is tapped by two items: \u0026ldquo;People are born unequal, and let\u0026rsquo;s not change the unequal situation\u0026rdquo; and \u0026ldquo;The poor themselves are responsible for their poverty, which has nothing to do with the government\u0026rsquo;s policy.\u0026rdquo; Analogously, both are given on a five-point scale and are averaged into an index where higher scores indicate higher levels of anti-egalitarianism (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.81, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09). As expected, traditionalism and anti-egalitarianism are positively correlated (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.60), which points to the reasonability of combining them into a single scale. An additive index of conservatism is thus formed by averaging the two subfactors (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.83, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eModerating variables\u003c/h3\u003e\n\u003cp\u003eAccording to H6, H7, and H8, the moderator is threat-induced anxiety. First, for internal threat (H6a, H7a, H8a), respondents rated, on a scale from 1 \u0026ldquo;strongly disagree\u0026rdquo; to 5 \u0026ldquo;strongly agree,\u0026rdquo; the degree to which they were anxious about the following issues: economic slowdown/unemployment/reduced income, substandard food and drugs, asset devaluation/confiscation, inadequate access to healthcare services, personal information leakage, regime instability, lack of social credit, erosion of mainstream values, worsened public safety, and worsened national security. The ten items are averaged into an index where higher scores indicate higher levels of anxiety (Cronbach\u0026rsquo;s \u0026alpha;\u0026thinsp;=\u0026thinsp;0.90, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.52, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00). Since these survey questions are not included in the 2020 wave, I only use the 2019 wave to test H6a, H7a, and H8a.\u003c/p\u003e\n\u003cp\u003eSecond, for external threat (H6b, H7b, H8b), I look at the COVID-19 outbreak. A year dummy\u0026mdash;2020 (vs. 2019)\u0026mdash;is generated to indicate whether respondents were exposed to the pandemic threat. It is expected that individuals interviewed in 2020, compared to those in 2019, had higher levels of anxiety due to the COVID-19 pandemic. I will shed more light on the reasonability of this operationalization in the subsequent statistical analysis.\u003c/p\u003e\n\u003ch3\u003eControl variables\u003c/h3\u003e\n\u003cp\u003eTo present a more fully specified model, I add a battery of control variables: economic beliefs (support for private ownership), interpersonal trust, egocentric perception, sociotropic perception, political interest, official media use, foreign media use, age, gender (male), CCP membership, education, income, social class (upper/middle/lower), and the year dummy (2020 vs. 2019). Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the descriptive statistics. Also see Supplementary Section A, Table A1 (2019 wave) and Table A2 (2020 wave) for two separate summary statistics.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics (NSAS 2019\u0026ndash;2020).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eDomestic policy preferences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eForeign policy preferences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eInstitutional trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eConservatism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAnxiety (induced by internal threats)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2020 (vs. 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePrivate ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eInterpersonal trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEgocentric perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSociotropic perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePolitical interest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOfficial media use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eForeign media use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCCP membership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLower class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMiddle class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUpper class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Model and analysis","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cp\u003eFor ease of interpretation, I treat the dependent variables as continuous and rely on ordinary least square (OLS) regression modeling. Residence dummies are added to correct for within-cluster correlation. To begin with, the three models in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e are concerned with H1-H3. As expected, conservatism has a statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) association with institutional trust and domestic/foreign policy preferences. The estimated coefficients are all positive, so Chinese citizens scoring higher on conservative ideology are more likely to support the current party-state system (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), authoritarian domestic policies (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), and hawkish foreign policies (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Relative to other variables in Model 1, conservatism, the key explanatory variable, accounts for about 23.07% of all the information in predicting the outcome (Model 2: 41.04%; Model 3: 35.35%). Considering control variables, egocentric perception, sociotropic perception, political interest, and official media use are positively correlated with the three dependent variables, whereas foreign media use is negatively correlated with them. Graphical tests of normality are presented in Supplementary Section B, Figures A10-A12. The histograms of residuals, Q-Q plots, and P-P plots indicate that the residuals in Models 1\u0026ndash;3 are approximately normally distributed, so OLS modeling is seen as appropriate.\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOLS models analyzing effects of conservatism (NSAS 2019-20).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eConservatism\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eInstitutional trust\u003c/p\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eDomestic policy\u003c/p\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eForeign policy\u003c/p\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.17*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.33*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.23*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2020 (vs. 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.15*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePrivate ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eInterpersonal trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEgocentric perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSociotropic perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.17*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.18*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePolitical interest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOfficial media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.23*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.21*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eForeign media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.13*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCCP membership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUpper class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e(vs. middle class)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLower class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e(vs. middle class)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6,910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6,910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6,910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: Standard errors in parentheses. Residence dummies and constants are omitted for space constraints. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eNext, I employ structural equation modeling (SEM) to test H4 and H5. The major findings are illustrated in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (H4) and Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (H5), and the full results are reported in Supplementary Section A, Table A4. Beyond the direct pathway linking conservatism to authoritarian-hawkish policy preferences, institutional trust, as manifested in Figs. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, is a significant factor in the indirect pathway that mediates this linkage. In other words, the association between Chinese citizens\u0026rsquo; ideology and policy attitudes is partially explained by their trust in the current system. As hypothesized, Chinese conservatives\u0026rsquo; system-justifying tendencies spill over and relate to their support for authoritarian domestic policies and hawkish foreign policies. Table 4 summarizes the effect sizes where the indirect effects are estimated by the Baron and Kenny\u0026rsquo;s approach. According to the Sobel\u0026rsquo;s test, both of the mediation effects are statistically significant at the 0.05 level. In detail, the mediation effect in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is 0.115 with a 95% confidence interval of [0.105, 0.125]; the mediation effect in Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is 0.087 with a 95% confidence interval of [0.078, 0.097]. On average, institutional trust accounts for 32\u0026ndash;35% of the total (direct plus indirect) effect identified via SEM.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e. Summary of direct, indirect, and total effects in SEM.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePercent mediated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eFig. 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eFig. 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eThen, I draw on the 2019 wave of NSAS to test H6a, H7a, and H8a where internal threat-induced anxiety is a moderator. According to Model 4 in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, there exists a statistically significant and positive association between the interaction term (\u003cem\u003eConservatism\u003c/em\u003e\u0026times;\u003cem\u003eAnxiety\u003c/em\u003e) and institutional trust (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). In support of H6a, the impact of conservatism on institutional trust is conditional on anxiety induced by internal threats. When Chinese conservatives feel anxious about a series of internal threats, they will double down on their system-justifying tendencies and become more trusting of the existing institutions. As displayed in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, if the level of anxiety changes from 1 (min) to 5 (max), for example, the system-justifying effect of conservatism will increase by about 72.25% (effect size from 0.104 to 0.180). By contrast, the interaction effects in Model 5 and Model 6 are not statistically significant at the 0.05 level, so there is no empirical evidence for H7a and H8a. Overall, internal threat-induced anxiety, as hypothesized, reinforces the system-justifying effect of conservative ideology. Yet, it is less conclusive whether internal threat-induced anxiety moderates the relationships between conservatism and domestic/foreign policy opinions.\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOLS models analyzing moderating effects of anxiety induced by internal threats (NSAS 2019).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eConservatism\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eInstitutional trust\u003c/p\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eDomestic policy\u003c/p\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eForeign policy\u003c/p\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.29*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.22*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eConservatism \u0026times; Anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePrivate ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eInterpersonal trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEgocentric perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSociotropic perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePolitical interest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.14*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOfficial media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.17*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eForeign media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCCP membership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUpper class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e(vs. middle class)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLower class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e(vs. middle class)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4,882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4,882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4,882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: Standard errors in parentheses. Residence dummies and constants are omitted for space constraints. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eConcerning H6b, H7b, and H8b, I rely on an identification strategy in the unexpected event during survey design (Mu\u0026ntilde;oz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, it assumes that the occurrence of an unexpected event assigns participants into the treatment/control group as if randomly so survey schedule (e.g., interview dates) can be regarded as an exogenous source of variation. In this case, the wave dummy (2020 vs. 2019) is proposed to be a moderating variable that reflects anxiety, albeit indirectly, triggered by the external threat of COVID-19 pandemic. On the other hand, although the outbreak of COVID-19 is unanticipated for both waves of respondents, individuals interviewed in 2019 (pre-outbreak) and those interviewed in 2020 (post-outbreak) may differ in background characteristics for reasons pertinent to sampling procedure. The odds are that imbalance on pre-treatment covariates, implicative of self-selection, undermines the assumption of as-if random assignment. In response to this concern about confounding, I employ coarsened exact matching (CEM), a data pre-processing technique that boosts balance on the observed covariates (i.e., age, gender, education, and party membership) while minimizing the \u0026ldquo;sparse-data bias\u0026rdquo; (Iacus et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mansournia et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e. OLS models analyzing moderating effects of pandemic threat (NSAS 2019-20).\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eInstitutional trust\u003c/p\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eDomestic policy\u003c/p\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eForeign policy\u003c/p\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eConservatism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.15*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.31*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.20*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2020 (vs. 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.14*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-0.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.30*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eConservatism \u0026times; 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePrivate ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eInterpersonal trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEgocentric perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSociotropic perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.20*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.21*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.14*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePolitical interest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eOfficial media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.18*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.31*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eForeign media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-0.17*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eCCP membership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eUpper class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e(vs. middle class)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eLower class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e(vs. middle class)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e6,514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6,514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6,514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" valign=\"bottom\" style=\"width: 596px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Standard errors in parentheses. Residence dummies and constants are omitted for space constraints. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 (two-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTable\u0026nbsp;6 presents the weighted regression models. The interaction terms (\u003cem\u003eConservatism\u003c/em\u003e\u0026times;\u003cem\u003e2020\u003c/em\u003e) in Model 8 (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and Model 9 (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) are statistically significant and positively signed, which lend credence to H7b and H8b. Under the pandemic threat, the associations between conservatism and domestic/foreign policy preferences in 2020 get stronger compared to those in 2019. Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the marginal effects plots where the slope coefficients for 2020 are much larger than those for 2019. Although the estimated effects of conservatism on policy attitudes are positive in both waves, those in 2020 (vs. 2019) increase by 26.29% for domestic policy (effect size from 0.312 to 0.394) and by 47.89% for foreign policy (effect size from 0.200 to 0.296). However, since the interaction effect in Model 7 is not statistically significant at the 0.05 level, there is no empirical evidence for H6b. It is thus inconclusive whether the relationship between conservatism and institutional trust depends on the pandemic threat. Taken together, the moderating effects of internal threat- and external threat-induced anxiety reveal distinct patterns. On the one hand, the correlation between conservatism and institutional trust increases given internal threat, but not external threat. On the other hand, the correlation between conservatism and domestic/foreign policy preferences increases given external threat, but not internal threat.\u003c/p\u003e\n \u003cp\u003eIn the meantime, I perform a set of robustness checks. First, I use traditionalism and anti-egalitarianism, the subcomponents of conservatism, for hypothesis testing. Second, I employ causal mediation analysis (Keele et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), rather than structural equation analysis, to test whether institutional trust plays a mediating role. Third, to alleviate concerns about omitted variable bias, I rely on sensitivity analysis tools developed by Cinelli and Hazlett (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and quantify how strong a confounder would have to be so as to explain away the effects of conservatism. Alternatively, I select two instrumental variables (IVs)\u0026mdash;intolerance of ambiguity and need for order/structure\u0026mdash;and utilize two-stage least squares (2SLS) regression. Fourth, with regard to the multiplicative interaction terms, I employ Hainmueller et al. (2019)\u0026rsquo;s binning estimation to check whether the conditional marginal effects are model dependent and subject to excessive interpolation or extrapolation. Fifth, instead of least squares regression, I draw on (inferential) Lasso regression and maximum agreement regression (Bottai et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for statistical analysis. Beyond internal validity, following Frank and Min (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), I further assess the extent to which the sampling process would have to be biased in order to overturn the research conclusions. Lastly, I use data from the 2017\u0026ndash;2018 NSAS to validate the results based on the 2019\u0026ndash;2020 NSAS. All of the above-mentioned analyses are reported and elaborated in Supplementary Section C. On the whole, the empirical findings are relatively robust to different modeling and specification techniques.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion and discussion","content":"\u003cp\u003eFor the sake of between-country comparison, this article draws on a psychological approach to political ideology in contemporary China and defines conservatism (vs. liberalism) as a combination of two elements: traditionalism and anti-egalitarianism. Analyses of the 2019\u0026ndash;2020 NSAS shows that Chinese conservatives are, by and large, similar to their WEIRD counterparts in terms of system-justifying tendencies and domestic/foreign policy preferences. First, Chinese citizens who endorse conservative ideology are prone to support authoritarian policies at home and hawkish policies abroad (H1, H2). Second, conservatism plays a system-justifying role in that individuals high in conservatism are more trusting of the current institutions led by the CCP (H3). Third, given the party-state\u0026rsquo;s intensified repression and assertiveness in the era of Xi Jinping, Chinese conservatives\u0026rsquo; institutional trust spills over and partially explains their support for authoritarian-hawkish policies (H4, H5). Fourth, under the circumstances of (internal/external) threat-induced anxiety, Chinese citizens scoring higher on conservativism are motivated to double down on their attachment to the CCP-dominated institutional status quo (H6a) and authoritarian-hawkish policies (H7b, H8b), resulting in significant interaction effects.\u003c/p\u003e\n\u003cp\u003eThis study also bears implications on authoritarian resilience and democratization in post-COVID China. As continuous economic slowdown undermines the CCP\u0026rsquo;s performance-based legitimacy, some scholars point out that public discontent and anxiety about declining living standards and economic well-being will, over time, give rise to anti-government protests, political instability, and a potential path toward democracy (Beckley and Brands, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, empirical evidence for H6a indicates the heterogeneity of Chinese public opinion. For individuals high in conservatism, growing concerns about a series of internal threats (e.g., economic slowdown and unemployment) prompt them to reduce psychological discomfort on behalf of the existing system, manifested by a stronger association between conservatism and institutional trust. In this regard, conservative ideology can be a barrier to mass mobilization and regime change in China. Despite the CCP\u0026rsquo;s diminishing performance, individuals on the political right seem to be \u0026ldquo;committed\u0026rdquo; system justifiers when they feel anxious.\u003c/p\u003e\n\u003cp\u003eOn the other hand, this article has limitations worthy of further reflection and investigation. To begin with, the two types of threat-induced anxiety are found to exert differential influences. For internal threat, there are no significant interaction effects on domestic and foreign policy preferences (no evidence for H7a, H8a). For external threat, there is no significant interaction effect on institutional trust (no evidence for H6b). Notably, as identified in Model 7 (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the coefficient of wave dummy \u003cem\u003e2020\u003c/em\u003e is statistically significant and positively signed. That is, the COVID-19 pandemic, an unanticipated external threat, brings about a rally-round-the-flag effect where Chinese people are pushed to unite in favor of the CCP leadership regardless of their ideological positions. Future work may try to shed light on the differences between internal and external threats and on their distinct correlations with political ideology or public opinion.\u003c/p\u003e\n\u003cp\u003eMoreover, system threat engenders feelings of not merely anxiety but also anger, two emotions predictive of different attitudinal and behavioral outcomes. For instance, based on a survey experiment in Germany, Auer and Freitag (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) find that (economic and pandemic) threat-induced angry fosters populist attitudes and reduces political support, especially when the established political system is perceived to be responsible. Concerning China\u0026rsquo;s zero-COVID containment policy between 2019 and 2022, Liu et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reveal a pronounced shift in public sentiment: from initial support (due to fear of the virus) to increasing disappointment and dissatisfaction (due to mental/physical strain from frequent testing and prolonged lockdowns). These negative anger-related emotions culminated in distrust in the government and widespread protests, known as the White Paper Movement (Guan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It is thus worthwhile to assess whether threat-induced anger plays a (dis)similar role as threat-induced anxiety in moderating the ideology-political attitude relationships.\u003c/p\u003e\n\u003cp\u003eFinally, in light of cross-sectional data, the research conclusions should be interpreted with caution for causal inference. It is plausible that some omitted variables may confound the correlational findings. Particularly, in testing the moderating effects of COVID threat, despite the application of CEM, data imbalance in unobserved characteristics, such as unequal sampling accessibility between the two rounds of participants, may still exist. Besides, the NSAS data were collected through non-probability samples biased toward young, educated Internet users in urban China. Nonetheless, given politically sensitive questions, online anonymous surveys, compared to face-to-face interviews, can mitigate respondents\u0026rsquo; intention to offer socially desirable responses (Huang and Yeh, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Online samples in China are also found to be valid in recent empirical studies (Li et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the future, longitudinal and/or experimental research, drawing on nationally representative data, can unpack the causal mechanism linking ideology to political attitudes in a more rigorous way. For example, by use of six panel surveys, Vishwanath (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) finds that traditionalism and egalitarianism are two significant causal predictors of American voters\u0026rsquo; attitudes toward welfare and transgender policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe research is conducted solely by G.C., who is responsible for the following: conceptualization of the study, designing the methodology, data analysis, and interpretation. G.C. also prepared and revised the manuscript for publication, approved the final version, and takes full responsibility for the accuracy, integrity, and completeness of the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are drawn from the Netizens\u0026rsquo; Social Awareness Survey (NSAS), administered by Professor Deyong Ma and his research team at Renmin University of China. The questionnaire and data are available in public domain for research purpose (http://www.cnsda.org/), with the permission of Chinese National Survey Data Archive.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlbertson B, Gadarian SK (2015) Anxious politics: democratic citizenship in a threatening world. 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J Contemp China 33(147):544\u0026ndash;559\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Pan and Xu (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) argue that nationalism is a third dimension. Ma and Lewis (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argue that assessment of Mao Zedong\u0026rsquo;s legacy is a third dimension.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Symmetrically, Chinese liberalism is composed of anti-traditionalism and egalitarianism.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Although internal threat is, in theory, distinguished from external threat, they may also be interconnected, such as a longer-run slowdown in economic growth caused by a global health crisis. Empirically, both internal and external threats are found to trigger public anxiety and shape political attitudes (Albertson and Gadarian, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Henderson and Oden, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Data on the Netizens\u0026rsquo; Social Awareness Survey are available online at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cnsda.org/\u003c/span\u003e\u003cspan address=\"http://www.cnsda.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, with the permission of Chinese National Survey Data Archive.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9443334/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9443334/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe article aims to investigate how conservative ideology in contemporary China shapes the public\u0026rsquo;s institutional trust and domestic/foreign policy preferences. To facilitate between-country comparison, this study draws on a psychological approach to political ideology and defines conservatism as a combination of two elements: traditionalism and anti-egalitarianism. Similar to their Western counterparts, Chinese conservatives, accepting of traditional values and social inequalities, are hypothesized to support authoritarian policies at home and hawkish policies abroad. Additionally, conservatism plays a system-justifying role in satisfying people\u0026rsquo;s needs for certainty and security. As such, individuals who endorse conservative ideology are expected to be more trusting of the current institutions led by the Chinese Communist Party. Given the Party\u0026rsquo;s increasingly repressive and assertive policies in the era of Xi Jinping, it is further argued that institutional trust partially mediates the link between ideology and policy opinions. More importantly, under the circumstances of (internal/external) threat-induced anxiety, Chinese citizens scoring higher on conservativism are motivated to double down on their attachment to the Party-dominated institutional status quo and authoritarian-hawkish policies, resulting in significant interaction effects. Analyses of the Netizens\u0026rsquo; Social Awareness Survey (2019\u0026ndash;2020) are largely supportive of the above arguments. Implications on China\u0026rsquo;s bottom-up regime change in times of diminishing economic performance are also discussed.\u003c/p\u003e","manuscriptTitle":"When system justifiers feel anxious: conservatism, institutional trust, and policy preferences in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 08:30:41","doi":"10.21203/rs.3.rs-9443334/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-10T10:20:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T06:12:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T01:24:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113416652339564758743614093479705428988","date":"2026-05-02T01:16:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102717630949635548143522942828751373228","date":"2026-05-01T12:41:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325560100846034552401699006030936209450","date":"2026-04-30T04:52:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31376589544555262732007506402609098083","date":"2026-04-29T10:19:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-29T09:52:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-29T09:03:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T08:50:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-24T04:45:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-04-17T02:51:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f0cf4f62-5823-421f-bc5c-8c8318f7ac7e","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-10T10:20:16+00:00","index":41,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T06:12:07+00:00","index":40,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T01:24:32+00:00","index":38,"fulltext":""},{"type":"reviewerAgreed","content":"113416652339564758743614093479705428988","date":"2026-05-02T01:16:46+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"102717630949635548143522942828751373228","date":"2026-05-01T12:41:49+00:00","index":36,"fulltext":""},{"type":"reviewerAgreed","content":"325560100846034552401699006030936209450","date":"2026-04-30T04:52:15+00:00","index":35,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67486519,"name":"Social science/Politics and international relations"},{"id":67486520,"name":"Biological sciences/Psychology"},{"id":67486521,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-07T08:30:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 08:30:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9443334","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9443334","identity":"rs-9443334","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}