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Rally ’Round the Red Flag: The Effects of Terror Attacks on Attitudes in China Yue Hou† Kai Quek‡ November 14, 2015 Abstract Are public attitudes sensitive to terror attacks in authoritarian regimes? If so, are changes in attitudes similar to those observed in democratic contexts? We study the effect of terror attacks on political attitudes in China, using original survey experiments conducted on a national sample of Chinese citizens. We find a strong “rally ’round the flag” effect: Individuals selected at random to view reports on a terror attack become significantly more nationalistic and supportive of policies that compromise civil liberty. Additional evidence reveals that the rally effects are driven by individuals’ aversion to violence and ethnic outsiders. † ‡ Center for the Study of Contemporary China, University of Pennsylvania. E-mail: [email protected] Department of Politics and Public Administration, The University of Hong Kong. E-mail: [email protected] 1 1 Introduction In the past two decades, terrorism has become a global issue, and there has been a surge in studies on the causes and consequences of terrorism. This growing body of literature provides important insight on the causes of terrorism (Bueno de Mesquita 2005; Krueger and Maleckova 2003), as well as various strategies used by terrorists (Benmelech and Berrebi 2007; Berman and Laitin 2005; Pape 2003). Scholars have also generated new knowledge on the consequences of terrorism. While on the macro level it remains inconclusive whether terrorist attacks effectively coerce governments into making political concessions (Abrahms 2006; 2012; Gould and Klor 2010; Pape 2003), studies on the micro level have shown that the electorate is sensitive to terrorism in different ways. Terror makes individuals more likely to vote for right-leaning parties (Berrebi and Klor 2008; Gould and Klor 2010; Kibris 2011); more active in politics (Hersh 2013; Sands 2014); more supportive of policies advocated by political leaders (Gadarian 2010); and more anxious towards outgroups (Panagopoulos 2006). Most of these studies speak to the effect of terrorism on the electorate in democracies, yet terrorism occurs in non-democracies as well (Wade and Reiter 2007). In the case of authoritarian regimes, where the government may not be as responsive to citizen demands for change, how might acts of terror nevertheless influence public opinion and policy outcomes? We developed and implemented an online survey experiment on a national sample of Chinese citizens, in which some respondents were randomly selected to view a report on a recent terror attack in China. We find a strong “rally ’round the flag” effect: Exposure to the terror treatment makes individuals more nationalistic — they become more proud of being a Chinese national and assign stronger importance to their national identity. The recall of a terror event also induces greater support for state policies that compromise civil liberties. A follow-up experiment reveals 2 that these effects are driven by individuals’ aversion to violence and ethnic outsiders. This article makes three contributions to the literatures on terrorism, nationalism, and state repression. First, it extends existing studies of the effect of terrorism on political attitudes in the authoritarian context. Scholars of terrorism have traditionally focused on measuring voters’ sensitivity to terrorism in democratic contexts. We propose that it is equally important to study the effect of terror on political attitudes in authoritarian contexts, especially in light of an emerging literature that challenges the assumption that authoritarian leaders ignore public opinion (e.g., Distelhorst and Hou 2015; Lorentzen 2013). Our results suggest that terror attacks in China might result in unintended and undesirable policy consequences and render those attacks counterproductive from the perspective of the perpetrators. The experimental design also provides cleaner causal estimates of the effect of terror attacks, compared to existing observational studies. Second, this article contributes to the literature on nationalism (e.g., Blank and Schmidt 2003; Herrmann, Isernia and Segatti 2009; Huddy and Khatib 2007). This article also examines the determinants of nationalism and shows that the intensity of “being nationalistic” can be unstable and manipulated at least in the Chinese context (Gries 2004; Weiss 2013). In contrast to terrorist attacks in many other countries where perpetrators are usually foreign nationals, China’s case is unique in the sense that the perpetrators, although usually ethnic minorities, are nevertheless Chinese citizens. Surprisingly, terror attacks by fellow nationals also result in a surge in nationalism. This suggests the salience of “ethnonationalism,” a particular strain of nationalism, is prevalent in the Chinese context. Third, this article speaks to the literature on state repression. There is a growing number of scholars studying state repression on single countries: Columbia, El Salvador, France, Germany, Greece, and the United States (Davenport 2007), and this article presents one of the first studies 3 estimating popular support for state repression in China. The finding on support for state repression also links back to the discussion on the causes and effects of terrorism. If state repression is a cause for grievance, which might have contributed to the rise of terrorism, then the finding that exposure to terrorism increases individuals’ support for state repression shows that terrorism might be counter-productive in forcing the state to make concession. Quite to the contrary, the Chinese state initiates more stringent policing policy mainly against ethnic others after recent terrorist attacks, and such a policy receives higher public approval, as our findings show. Relatedly, studies have shown that in democracies, individuals are willing to trade off civil liberties for greater personal safety and security under specific context (Davis and Silver 2004; Peffley, Knigge and Hurwitz 2001). This article finds that such tradeoffs also exist in authoritarian contexts where civil liberties are not known to be valued. The threat to national or personal security can induce a substantial willingness among the Chinese to further surrender their civil liberties. 2 Terror attacks, Ethnic tension, and Rally ’round the flag Defining the term terrorism is challenging, yet most terrorism scholars would agree that acts of terrorism are those that are: (i) ineluctably political in aims and motives; (ii) violent — or, equally important, threatens violence; (iii) designed to have far-reaching psychological repercussions beyond the immediate victim or target; (iv) conducted either by an organization with an identifiable chain of command or conspiratorial cell structure (whose members wear no uniform or identifying insignia) or by individuals or a small collection of individuals directly influenced, motivated, or 4 inspired by the ideological aims or example of some existent terrorist movement and/or its leaders; (v) perpetrated by a subnational group or nonstate entity (Hoffman 2006, 40). In China, although organized terrorist attacks recently started to become a concern,1 most “acts of terrorism” labeled by the government were conducted by passing groupings of individuals or were “non-violent mass mobilization” especially in the western Xinjiang and Tibet Autonomous Regions (Potter 2013, 89). Since such a categorization is different from the conventional definition of terrorism delineated above, in this study, we avoid using the terms “terrorism” and “terrorist attacks” when describing violent events that has an ethnic dimension in China. We instead refer to them as “terror attacks” or “terror incidents.” Terror incidents have occurred in China with increasing frequency since the 1980s, especially in the northwestern Xinjiang Uyghur Autonomous Region, where the majority of Muslim Uyghur population reside (Potter 2013). Some of these incidents include the 1992 Urumqi bombings, the 1997 Urumqi bus bombings, the 2009 ethnic riot in Urumqi, and outside of Xinjiang, the 2014 Kunming knife attack. The 2009 riots in Urumqi led to 200 deaths and more than 1000 arrests by the police (Distelhorst and Hou 2014). The 2014 Kunming knife attack, where eight black-clad assailants killed 29 people with knives and machetes at a railway station in the southwestern city of Kunming, was the first time Uyghurs have been accused of a major and organized attack outside Xinjiang.2 Many labeled this event as “China’s 9/11.” 3 1 The Eastern Turkestan Islamic Movement (ETIM) is widely regarded as a terrorist organization. China has at least once faced threat by al-Qaeda in the Islamic Maghreb (AQIM) in 2009. See Potter (2013) for more details. 2 http://www.theguardian.com/world/2014/mar/02/kunming-knife-attack-muslim-separatists-xinjiang-china. Accessed March 26, 2015. 3 For instance, see “Terrorists behind ‘China’s 9/11’ sentenced to death” ( http://www.telegraph.co.uk/news/worldnews/asia/china/11091532/Terrorists-behind-Chinas-911-sentencedto-death.html) and “Is the Kunming Knife Attack China’s 9-11?” (http://thediplomat.com/2014/03/is-thekunming-knife-attack-chinas-9-11/). Accessed Oct. 8, 2015. 5 Over the same period of time, ethnic minorities in China enjoy a wide range of preferential policies in family planning, education, job recruitment and promotions, tax exemptions, and representation in legislatures and other political bodies (Distelhorst and Hou 2014; Wei and Chen 2011). Yet, many argue that the limitations on religious practices, together with widening economic inequality, have caused grievances among ethnic minorities, particularly the Uyghurs (Yee 2003). Among some of the aggrieved individuals, grievance begets violence. The Chinese government has responded to the growing unrest among the Uyghurs with heightened social and religious controls. Throughout the “Strike Hard” campaign, the government tightly observes religious activities and festivals, monitors Muslims returning from their studies in Islamic schools overseas, arrests and executes suspected terrorists, reinvigorates a system of informants, and recruits reliable minority cadres to the Party and government (Chung 2006). After the 2014 Kunming knife attack, China’s security chief, Meng Jianzhu, vowed “all-out efforts” to severely punish terrorists.4 A new policy which reads that “special police officers, when encountering perpetrators of violence, do not have to follow the procedures of ‘identifying themselves and firing warning shots,’ instead, they can execute criminals on the spot,” although rendering civil liberties at risk, is welcomed by many Chinese.5 Armed troops were seen on the streets of many Chinese cities shortly after the attack. Hundreds of suspects were arrested and more than a dozen suspects were executed in 2014.6 What are the effects of terror attacks on public attitudes? We hypothesize that Chinese citizens are sensitive to terror attacks and that these events have a powerful impact on their political 4 http://www.dw.de/one-year-crackdown-in-chinas-xinjiang-region-after-bomb-attacks/a-17657024. Accessed March 26, 2015. 5 For example, see netizen comments in relevant articles appeared in the main Chinese news online outlets such as sina.com; 163.com; sohu.com;etc. 6 http://www.telegraph.co.uk/news/worldnews/asia/china/10978406/Beijing-assembles-peoples-army-to-crushChina-terrorists-with-an-iron-fist.html. Accessed March 26, 2015. 6 attitudes. First, following the findings in Berrebi and Klor (2008), Davis and Silver (2004) and Gadarian (2014), we expect these violent attacks to increase individuals’ sense of vulnerability and change their perception of personal security, and result in enhanced support for a stronger state as well as policies that trade off civil liberties for security: H1: Terror attacks make individuals more likely to support state policies that compromise civil liberties. Many studies argue that terrorism triggers a “rally-round-the-flag” effect: Following a terror attack or other dramatic events, support for the president or other state leaders might enjoy a shortlived spike, and public support for and trust in government might increase (Chanley 2009; Hersh 2013; Hetherington and Nelson 2003).7 In China, data on “trust in government” and “approval for state leaders” might suffer from social desirability biases for obvious reasons (Chen and Dickson 2008; Chen and Shi 2001; Truex 2014b). Therefore, we do not focus on these outcomes. On the other hand, Potter (2013) finds that netizens’ comments usually became increasingly and openly nationalistic after a recent terror attack. In the Chinese context, we expect to observe an indirect “rally-round-the-flag” effect in that: H2: Terror attacks make individuals more nationalistic. 3 Data and Research Design We test these hypotheses through original survey experiments in China. We embedded our experiment in an online survey fielded in February 2015. Subjects were recruited across all 31 7 There also exists a strand of literature that argues against this “rally-round-the-flag” effect. For instance, see Bali (2007) and Hong and Kang (2014). 7 provinces of China by a major survey company (see Appendix A5 for summary statistics). Subjects were recruited from the company’s online panel of respondents. Among those who finished the survey, 21 subjects asked to withdraw. The final sample yields 805 observations. It is important to note that even if our sample is not a probability sample strictly representative of the entire Chinese population, surveying the online Chinese population is informative and valuable for our research questions. First, given “the institutional and technological constraints” (Huang 2015), a nationally representative survey in China would involve face-to-face interviews, a method highly problematic especially because we are interested in politically sensitive questions such as support for government policies. Second, Chinese “netizens” are at the forefront of open public discourse, and they are younger, wealthier, better educated, and more likely to be living in urban areas (Truex 2014a). Scholars have argued that these young and educated internet users constitute the present and future middle class, whose political attitudes might matter the most in the policy making process (Easterly 2001; Lan and Li Forthcoming). The survey started with an introduction screen (“We would like to understand your views on domestic public affairs.”). It transited thereafter to the experimental screen, at which subjects were randomly assigned into either the treatment condition or the control condition. Subjects randomized into the control group saw a screen displaying “Please wait while the webpage loads” before proceeding directly to the dependent variable question. Like the treatment screen, the control screen was programmed with an enforced minimum of 10 seconds before a button appeared for subjects to click and move on. The Terror treatment is designed to trigger the subject’s recall of a real-life terror incident in China: the Kunming incident of March 1, 2014. Our Terror treatment used a short vignette that simply focused on the main factual features of the incident: 8 At 9pm on March 1st, 2014, violent knife attacks occurred at the Kunming rail station. According to the statistics collected by 5pm on March 2nd, 29 people were stabbed to death. 143 more individuals were injured, among which 73 were heavily injured and 70 lightly injured. To enhance realism, we adopted the descriptions from actual news coverage on the incident, which consistently emphasized the same set of objective facts. The only difference is the removal of all subjective commentaries and condemnations, since they would have confounded the Terror treatment. There were randomized variations in the wordings of the Terror treatment. One third of the subjects received this vignette. One third received this vignette and an additional line that reads “all victims were Han Chinese.” The last third received this vignette and an additional line that showed the names of the Uyghur perpetrators. These variations in the vignette reproduce the variations in the actual news reportage of the incident. See Appendix B for these variations in Chinese. The experimental screen was programmed with an enforced minimum of 10 seconds before a button appeared for the respondent to click and move on. After being exposed to their treatment conditions, subjects proceeded to a series of questions on attitudes and background. We measure public support for the Shootfirst policy, which has the following wording: Recently, social conflict is on the rise. Would you support the following policy if it were to be proposed: “Special police officers, when encountering a perpetrator of violence, do not have to follow the procedures of ‘identifying themselves and firing warning shots,’ instead, they can execute the other party on the spot.” The response options are: strongly agree, somewhat agree, somewhere in the middle, somewhat disagree, and strongly disagree. 9 To test the causal impact of terror attacks on nationalism, we use three dependent variables to measure nationalism: Importance, Pride, and Chinalabel. Instead of treating nationalism as a unidimensional concept, we isolate two key components of nationalism and measure each component separately. Studies have suggested that “the positive bond with the nation is a multidimensional system of beliefs,” which includes an emotional disposition — “an overall positive emotion toward the nation” — as well as an identity salience — the “significance of national affiliation in the overall identity of an individual” (Blank and Schmidt 2003; Crocker and Luhtanen 1990; Eagly and Chaiken 1993). To capture the emotional-disposition component of a respondent’s nationalism, we use the dependent variable Pride, based on the question that asks how proud a respondent feels regarding her nationality (Blank 2003; Blank and Schmidt 2003). To capture the identity-significance component, we adopt a question frequently used in the empirical scholarship on nationalism that asks how important one’s national affiliation is to the subject (Herrmann, Isernia and Segatti 2009; Huddy and Khatib 2007; Kosterman and Feshbach 1989; Kunovich 2009). Aside from isolating the different dimensions of nationalism, we are also interested to understand whether the “rally effect” extends beyond the respondent’s political opinion to her economic consumption choices. To this end, we include a third dependent variable, Chinalabel, to measure what some might call “economic nationalism.” The Chinalabel item is inspired by an observation in the management literature that links nationalism to one’s preference to products made in her home country (Baughn and Yaprak 1993; Verlegh 2007). We asked subjects to choose from a list of products they wish to purchase, one of which is a Chinese brand. Below are the wordings of these questions. Pride: Do you feel proud to be a Chinese citizen? (options: extremely proud; somewhat proud; somewhat not proud; and not proud at all) 10 Importance: How much importance do you assign to being a Chinese citizen? (options: completely unimportant; not very important; somewhat important; and very important) Chinalabel : If you were to buy a new television set, which of the following brands would you consider, assuming they have exactly the same quality and characteristics? (options: Samsung; Phillips; Changhong; and Sony)8 Finally, we asked several demographic and background questions including age, gender, ethnicity, marital status, education, household income, the Chinese Communist Party (CCP) membership, religiosity, occupation, and location. 4 Results: the Effect of Terror on Attitudes Public support for Shootfirst is generally high. The majority of the subjects either “strongly agree” (24%) or “agree” (27%) with Shootfirst (Figure 1). 8 Samsung is a South Korean brand; Phillips is a Dutch brand; Changhong is a domestic Chinese brand, and Sony is a Japanese brand. We believe it is safe to assume that most subjects knew that Changhong is a Chinese brand, while the other three are foreign. 11 Figure 1: Support for Shootfirst Policy Percentage 20 10 0 1 Strongly Disagree 2 3 4 5 Strongly Agree Note. Histogram of percentage of subjects who strongly disagree, disagree, neutral, agree, and strongly agree with the Shootfirst policy. N=788. On average, subjects are proud of their Chinese national identity and regard it as important. Only 4.4% and 13.28% of the subjects think that a Chinese national identity is “not important at all” or “not very important” to them. Similarly, only 5.84% and 8.80% of the subjects are “not proud at all” or “not very proud” of their Chinese identity. On the other hand, when given a choice of four different brands of television sets, only 29.09% of the subjects went for the Chinese brand “Changhong” (Figure 2). Answers to the three questions are positively correlated, and a principal component analysis shows that the three variables load onto one common dimension of information, but each contains unique variations (Table A1). 12 Figure 2: Nationalism Measures 40 40 60 20 Percentage 30 Percentage Percentage 30 20 40 20 10 10 0 0 0 0.25 Not important 0.5 0.75 I mport ance 1 Very Important 0.25 Not important 0.5 0.75 P roud 1 Very Important 0 Foreign Brand 1 China Brand C h i nal ab el Note. Histograms of the values for the three nationalism dependent variables: Importance, Pride, and Chinalabel. N=788. When they feel threatened, people are more likely to support state policies that might compromise civil liberties. The support for Shootfirst increased by a significant 0.44 of a scale when subjects were exposed to the description of the Kunming knife attack event (Table 1). On a scale of 1-5 where the maximum change one can have is 4, a 0.44 increase represent at least 10% change in individuals’ attitudes, which is substantively large. The estimate of the treatment effect is robust to the inclusion of individual covariates in OLS and ordered logistic regressions (Tables 3 and A3). 13 Table 1: Treatment Effect on Shootfirst Support for Shootfirst Obs. Treatment Control Difference 3.530 3.091 0.439*** (0.060) (0.076) (0.098) 453 274 Note. Estimated mean value of support for the Shootfirst policy across control and treatment groups. Standard errors (in parentheses). Two-sided t-test. *** p<0.01, ** p<0.05, * p<0.1 Exposure to the terror prime also makes individuals more nationalistic. When exposed to the Terror prime, individuals become more proud of their Chinese citizenship and believe that their Chinese national identity is more important to them (Table 2). Again, since the treatment prime does not include any subjective commentaries and condemnations which often appeared in media reports, these estimated treatment effects are likely to be under-estimates of the effect of individuals’ reading actual media reports on the same terror attack. The treatment effect is statistically insignificant on Chinalabel, suggesting that the “rally effect” does not extend beyond the respondent’s political opinion to her economic consumption choices. In the above analysis, we grouped three variants of Terror treatments together, but the findings are robust if we look at the effect of each of the variant separately. 14 Table 2: Treatment Effect on Nationalism Measures Pride Importance Chinalabel Treatment Control Difference 0.823 0.766 0.056*** (0.010) (0.014) (0.016) 0.812 0.782 0.029* (0.010) (0.013) (0.016) 0.329 0.284 0.045 (0.022) (0.027) (0.045) Note. Estimated mean across control and treatment groups. Standard errors (in parentheses). *** p<0.01, ** p<0.05, * p<0.1 Other Variables of Interests To further understand the determinants of support for repression and nationalism, we incorporate additional political, socio-economic, and demographic variables into the analysis. We include subjects’ gender (Male), Age, whether one has a college degree or higher (College), whether one is married (Married ), the Chinese Communist Party membership (CCP ), and religiosity (whether one is an Atheist). HighIncome is a dummy variable indicating whether a subject’s income level is above the sample mean. We also control for occupation and current location (Table 3). The estimates of the treatment effect remain robust to the inclusion of these covariates in the OLS regressions. 15 Table 3: Treatment Effects: Results from OLS Terror Male Age College Married CCP HighIncome Atheist Job Region Constant Observations R-squared Shootfirst 0.443*** (0.010) 0.017 (0.102) 0.002 (0.005) 0.285** (0.113) 0.176 (0.140) 0.069 (0.125) 0.182* (0.109) -0.228** (0.116) Importance 0.018 (0.016) -0.036** (0.017) -0.001 (0.001) 0.002 (0.019) -0.044** (0.022) 0.005 (0.021) -0.009 (0.018) -0.002 (0.020) Pride 0.049*** (0.016) -0.038** (0.017) -0.001 (0.001) 0.007 (0.019) -0.049** (0.023) 0.047** (0.020) -0.004 (0.017) 0.003 (0.021) Chinalabel 0.029 (0.036) 0.013 (0.037) 0.001 (0.002) 0.036 (0.041) -0.055 (0.055) 0.058 (0.045) -0.008 (0.040) 0.026 (0.043) 3.263*** (0.512) 714 0.135 1.002*** (0.067) 746 0.117 0.951*** (0.070) 746 0.141 0.314* (0.165) 749 0.070 Note. Results from OLS regressions. The dependent variables are: Shootfirst, Importance, Pride, and Chinalabel. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Several interesting findings emerge from the analysis. First, individuals who are better educated and wealthier9 are more likely to support Shootfirst. This finding might appear counter-intuitive, since studies of other countries usually find a negative association between education (and income) and support for state policies or trust in government (e.g., Davis and Silver 2004). Two factors 9 Income and Education are highly correlated. 16 might have contributed to this unusual correlation. First, the Shootfirst policy, which is usually exercised in localities with strong ethnic tension, is not likely to affect and be exercised upon those with high income and high education. This rational calculation might result in support for compromising civil liberties, because in their cases only others’ civil liberties will be compromised. Second, nationalistic education constitutes a major part in college curriculum in China (Cantoni et al. 2012), and a higher support for state policy might indicate that such an education with a strong propaganda touch has in fact been effective. Finally, we find no clear correlation between income or education and sentiment of nationalism, unlike the findings in Huddy and Khatib (2007) or Kunovich (2009). Female subjects appear to be more nationalistic than male, contradicting some existing studies on nationalism (e.g., Lan and Li Forthcoming) but confirming others (Hoffmann and Larner 2013). Future research could investigate these correlations in greater depth. 5 Mechanisms Next, we consider possible mechanisms through which terror attacks shift individuals’ attitudes. The Kunming terror attack had two distinct components: It was carried out by ethnic minorities and it was violent. Therefore, the treatment effect on Shootfirst and nationalism could have been explained by either the “violent” component or the “ethnic” component of the treatment, or their interaction. We conducted a follow-up experiment to disentangle the two possible mechanisms. 17 5.1 Theoretical Predictions Mechanism I: Violence aversion. The first possible mechanism is that violence alone, regardless of who commits it, triggers individuals’ sense of insecurity and therefore boost support for Shootfirst policy and increase nationalistic sentiments. Sniderman et al. (1996) argue that individuals’ commitment to civil liberties might collide with other important values or concerns. Empirically, Davis and Silver (2004) find that American people’s sense of threat lower their support for civil liberties. Scholarship in security studies suggests that terror attacks might result in “threat inflation” and induce patriotism (Cramer 2007; Kaufmann 2004). Therefore, regardless of the ethnic element, individuals’ aversion to violence can lead to stronger support for the Shootfirst policy. As the extant literature has not touched much upon the relationship between violence aversion and nationalism, we test this relationship explicitly in our experiment. Mechanism II: Outgroup aversion. The second possible mechanism is that the knife attack treatment, with its clear ethnic components — Uyghur perpetrators and Han victims — immediately trigger subjects’ outgroup fear and aversion (Distelhorst and Hou 2014; van der Schalk et al. 2011). Outgroup aversion and animosity can reduce tolerance (Hopkins 2009) and trigger nationalism (Huddy and Khatib 2007; Shayo 2009). Thus, the “outgroup aversion” mechanism suggests that individuals’ aversion to outgroup can lead to stronger support for the Shootfirst policy and trigger nationalistic sentiment. A third possibility is that both of the ethnicity and violence mechanisms could be at work, causing the rally effect we observe. 18 5.2 Recruitment and Design In August 2015, we recruited 1,356 subjects from a popular Chinese crowd-sourcing website, which is similar to Amazon’s Mechanical Turk, to participate in an online survey.10 As shown in Appendix A5, subjects recruited from this platform are largely similar to those of the subjects recruited in the original experiment (Section 3). The main difference between this follow-up sample and the original sample is that the subjects recruited through the Chinese Mechanical Turk are younger, and therefore less wealthy and less likely to have been married. Differences in these variables might have resulted in differences in baseline in support for Shootfirst and nationalism. We return to this issue later in the section. Subjects were randomly assigned into one of the following four treatments. In the group Terror, subjects read a description of the Kunming incident, identical to the Terror treatment in the original experiment, but without variations in wording. In the group Ethnicity, subjects were primed to think about their own ethnic identity by reading a prompt adopted from a standard identity priming treatment (Sniderman, Hagendoorn and Prior 2004): [Group Ethnicity] People belong to different types of groups. One of the most important and essential of these groups is the ethnicity that you belong to. Each ethnicity is different. For example, you belong to the (dropdown) ethnicity. In the groups Violence1 and Violence2, subjects read a description of a recent terror attack which was violent but had no ethnic element. In both cases, it was clear that there was no ethnic dimension to who committed the violence and who were targeted. The event described in Violence1 10 Subjects recruited were directed to a US-based website to take the survey anonymously. Each unique IP address and account at the recruiting platform was allowed to participate only once and could quit at any moment during the survey. For other study using the same platform, see Huang 2015. 19 was similar to the Terror event in terms of number of casualty. The incident described in Violence2 resulted in a smaller number of casualty but it was an attack targeting public officials. We use two different Violence primes because we are also interested in understanding whether the nature of the targets (e.g. citizens and public servants) mattered.11 Both events were heavily covered by Chinese media. [Group Violence1 ] At 8pm on June 5th 2009, a perpetrator Zhang Yunliang set a bus on fire in Chengdu, Sichuan Province. The fire caused 28 deaths and many injuries. [Group Violence2 ] At 4pm on July 30 2010, a bomb went off in a building in the tax bureau of Changsha, Hunan province. The perpetrator Liu Zhuiheng dropped the bomb under a conference room table, causing 4 deaths and 19 injuries. Subjects in the control group did not see any prompt. All subjects then proceeded to answer a series of questions on attitudes and background identical to the previous experiment including support for Shootfirst and the same three nationalism variables — Proud, Importance, and Chinalabel. 5.3 Results Public support for Shootfirst was lower compared to the support level in the original survey, but still relatively high. About 14% of the subject “strongly agree” and 23% “agree” with the policy. Another 28% of subjects felt “neutral” about the policy. Table 4 shows the sizes of treatment effect across four groups. Subjects randomized into all of the four treatment groups were more likely to support the Shootfirst policy compared to those in the control condition. However, the effect sizes in the Ethnicity condition or the two Violence 11 The perpetrator has received some support from individuals who have a negative view of governments (e.g. see netizens comments following the newsreport in http://www.infzm.com/content/48713, accessed Oct.8, 2015). 20 conditions are much smaller than the treatment effect in the Terror condition, and only the treatment effect in the Terror condition is statistically significant at the conventional threshold. Further, the differences between the effect of Terror and the effect of Violence are statistically significant at the 0.01 level. These results suggest that an attack resulting in comparable loss of life without the element of ethnic terrorism increase support for Shootfirst less than a violent terror attack with an ethnic dimension. The difference between the effect of Terror and the effect of Ethnicity on Shootfirst is also statistically significant at the 0.01 level, suggesting that “outgroup aversion” alone does not trigger as strong support for the Shootfirst policy as a terror attack. Table 4: Treatment Effect on Support for Shootfirst (Follow-up Survey) Treatment Groups Terror Ethnicity Violence1 Violence2 Treatment Control Difference 3.141 2.917 0.224** (0.083) (0.082) (0.116) 3.064 2.917 0.147 (0.077) (0.082) (0.113) 3.059 2.917 0.142 (0.079) (0.082) (0.113) 3.054 2.917 0.138 (0.082) (0.082) (0.116) Note. DV: Support for Shootfirst. Estimated mean value of support for the Shootfirst policy across control and treatment groups in the follow-up survey. Standard errors (in parentheses). *** p<0.01, ** p<0.05, * p<0.1 Next, we examine the three nationalism measures. Our follow-up experiment did not replicate the effect of Terror on three nationalism outcomes — the direction of the effects are positive, as expected, but the effect sizes were significantly smaller than those in the original experiment, and the effect sizes are indistinguishable from 0. Further, subjects in the Ethnicity and the two 21 Violence conditions also did not become more nationalistic (Table 5). Table 5: Treatment Effect on Nationalism Measures (Follow-up Survey) Nationalism Measures Pride Importance Chinalabel Groups Terror Ethnicity 0.008 0.050*** (0.019) (0.017) 0.003 0.009 (0.018) (0.018) 0.065 0.005 (0.046) (0.043) Violence1 0.011 (0.019) 0.002 (0.019) -0.009 (0.044) Violence2 0.011 (0.019) 0.002 (0.019) -0.009 (0.044) Note. Estimated mean across control and treatment groups. Standard errors (in parentheses) and 95% confidence intervals reported below. *** p<0.01, ** p<0.05, * p<0.1 One possible explanation that the effect of the Terror treatment on nationalism outcomes was insignificant in the follow-up experiment is a higher baseline of nationalistic sentiment due to a national military parade and its huge media coverage in September 2015, when our follow-up experiment took place.12 Among the subjects in the control condition, 62.05% felt “very proud” of their national identity and 49.55% felt that their national identity was “very important” to them, which were higher than the numbers in the original experiment: 44.61% and 43.40%. Because nationalistic sentiment surged around the parade time, the majority of individuals already reached the highest possible value of “being nationalistic” in September. Given that randomization was done successfully (i.e., subjects in the control condition are not different from those in the treatment 12 For news coverage on the military parade, See “China Stages a Massive Military Parade to Commemorate the End of World War II” (Alan Taylor, the Atlantic). http://www.theatlantic.com/photo/2015/09/chinastages-a-massive-military-parade-to-commemorate-the-end-of-world-war-ii/403627/. And “China flexes muscles with World War II military extravaganza” (By Katie Hunt, Steven Jiang and Will Ripley, CNN) http://www.cnn.com/2015/09/02/asia/china-world-war-ii-military-parade/. In the CNN report, one Chinese citizen expressed that “I felt very proud. The army of my country is truly grand and strong!” Accessed Oct. 6, 2015. 22 conditions in the characteristics we can observe), it was therefore impossible to observe any increase in nationalistic sentiments among those who were already “very proud,” no matter how strong and relevant a prime might be. Again, the follow-up sample is younger and poorer than the original sample, and these differences might have also contributed to the differences in baseline for both Shootfirst and the nationalism measures. There are, nevertheless, meaningful inferences we can draw from the analysis. First, the direction of the effects on Pride and Importance in the four conditions — Terror, Ethnicity, Violence1 and Violence2 — are all positive, suggesting that these primes have triggered individuals’ nationalistic sentiments. Second, the fact that both the ethnicity and the violence conditions make some individuals more nationalistic suggests that both “out-group aversion” and “violence aversion” might have contributed to the “rally effect” of terror attacks. 6 Discussion This article presents one of the first studies that provide clear causal estimates on the effects of terror attacks on attitudes in an authoritarian context. Our findings provide strong support for a “rally ’round the flag” effect: Individuals become more nationalistic and are more willing to support a repressive policy after exposure to a terror attack. We, of course, do not want to overstate the importance of our findings from one experiment, and the usual concerns about external validity apply. However, this project presents a systematic attempt to understand the “effectiveness” of terror attacks in China, with significant policy implications. 23 Many argue that terrorism works in democracies where voters are sensitive to violence, and governments are forced into granting political and/or territorial concessions (Gould and Klor 2010). This is essentially why Pape (2003) argues that democracies are more vulnerable to terrorist attacks. Recent research on public opinion and policy-making in authoritarian contexts has highlighted how citizen attitudes may influence leaders even in the absence of formal electoral mechanisms. In particular, several studies that focus on China (e.g. Chen, Pan and Xu 2015; Distelhorst and Hou 2015; Lorentzen 2013; Truex 2014a) have challenged the assumption that authoritarian leaders are immune to public influence. Based on the latest research, it appears that policymakers and public opinion in authoritarian regimes are not insulated and independent, but rather, influence each other. Here, we find that citizens in China are also sensitive to terror attacks, and they react by becoming more nationalistic. Since nationalism is usually associated with racism, intolerance, and conflict (Brubaker and Latin 1998; Ko 2015; Saideman and Ayres 2008; Schrock-Jacobson 2012), we might worry that terror attacks enhance nationalistic sentiments among the Han Chinese and intensify their intolerance towards ethnic minorities, which in turn encourage more violence domestically and even regional confrontations.13 This is the kind of vicious cycle that citizens do not want to experience.14 Individuals’ strengthened willingness to compromise civil liberty and increased support for a hardline policy are equally worrisome, because increased security might further fuel existing grievances and entrench the divide “between a freer and more prosperous east and a security state in the west” in China (Potter 2013). Government crackdowns might also 13 Potter (2013) argues that “to the extent that the attack has links to organizations operating abroad, popular demands for action could drag China into regional confrontations with Pakistan or Afghanistan” (85). 14 On the other hand, we acknowledge another body of literature that argues that nationalism induces altruism and tolerance towards ethnic others. For instance, see Charnysh, Lucas and Singh (2015) and Chung (2015). 24 foment ideological opposition to the government and make mobilization of terrorist organizations more effective (Bueno de Mesquita 2005). Over the last decade, there is an emerging consensus among scholars and observers of Chinese politics that the government is far from immune to nationalist public opinion. A recent review by Shirk (2014), for example, singled out “[a] leadership that is highly responsive to nationalist public opinion” as one of the “three features of the Chinese political system” (392). If this is indeed the case, authoritarian policymakers might face the pressure to appease nationalist sentiments and to exhibit state strength by moving in the direction that might not be strategically wise. On the other hand, an authoritarian state can manipulate nationalistic sentiments to achieve goals it cannot achieve otherwise (Weiss 2013). To some extent, the “rally ’round the flag” effect creates new opportunity for the state to implement policies that in normal times would not be as welcomed by the public. Further, when the threat to authoritarian rule is high, an autocrat might undertake further homogenization policy (Alberto and Reich 2015). These policy shifts might not be what perpetrators have wished for. Extensions to this research may further pursue the mechanisms that underlie the changes in attitudes following exposure to terror attacks. 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Means of pretreatment covariates for individuals in the treatment (Terror ) and control groups. The third column reports p-values for two-sided t-tests. The variables Married and Atheist are less well-balanced than others, and we control them in the OLS analysis. The F-test for regressing treatment status on all covariates gives a p-value of 0.8119. 31 Table A3: Robustness Check: Ordered Logistic and Logistic Models Terror Male Age College Married CCP HighIncome Atheist Job Region Observations (1) Shootfirst -0.661*** (0.141) 0.00186 (0.151) -0.00453 (0.00756) -0.410** (0.163) -0.278 (0.199) -0.203 (0.187) -0.319** (0.160) 0.391** (0.166) (2) Importance 0.223 (0.153) -0.286* (0.153) -0.00760 (0.00759) -0.0383 (0.179) -0.349 (0.218) 0.0775 (0.198) -0.0391 (0.167) -0.0508 (0.190) 748 746 (3) (4) Pride Chinalabel 0.482*** 0.142 (0.156) (0.174) -0.311* 0.0695 (0.161) (0.179) -0.0118 0.00576 (0.00779) (0.00867) -0.0485 0.180 (0.188) (0.202) -0.571** -0.279 (0.239) (0.264) 0.450** 0.278 (0.206) (0.208) 0.0542 -0.0498 (0.166) (0.191) -0.0765 0.135 (0.204) (0.219) 746 742 Note. Results from ordered logistic regressions (1)-(3) and logistic regression (4).Robust standard errors in parentheses. ** p<0.01, ** p<0.05, * p<0.1 32 Table A4: Heterogenous Treatment Effect Terror Terror * College Terror * HighIncome Terror * CCP Male Age College Married CCP HighIncome Atheist Job Region Constant Observations R-squared Shootfirst -0.428** (0.176) 0.122 (0.220) -0.278 (0.208) 0.277 (0.245) -0.001 (0.101) -0.002 (0.005) -0.352** (0.176) -0.194 (0.135) -0.269 (0.203) -0.040 (0.167) 0.247** (0.112) Importance 0.034 (0.030) 0.001 (0.038) 0.006 (0.034) -0.092** (0.041) -0.039** (0.017) -0.001 (0.001) 0.003 (0.032) -0.044** (0.022) 0.063** (0.032) -0.012 (0.029) -0.002 (0.020) Pride 0.070** (0.031) 0.016 (0.038) -0.024 (0.035) -0.085** (0.039) -0.040** (0.019) -0.001 (0.001) -0.002 (0.032) -0.049** (0.023) 0.101*** (0.033) 0.012 (0.030) 0.003 (0.021) Chinalabel 0.063 (0.063) 0.017 (0.076) 0.016 (0.074) -0.247*** (0.091) 0.006 (0.037) 0.001 (0.001) 0.029 (0.063) -0.056 (0.055) 0.213*** (0.076) -0.017 (0.061) 0.026 (0.043) 2.616*** (0.490) 748 0.140 0.994*** (0.0712) 746 0.124 0.942*** (0.0745) 746 0.148 0.301* (0.164) 749 0.081 Note. Results from OLS regressions.Robust standard errors in parentheses. ** p<0.01, ** p<0.05, * p<0.1 33 Table A5: Comparing Samples in the Original and Follow-up Surveys Variable Male Age Education(group) Income CCP Married College Postgrad Ethnicity (Han) (Zhuang) (Man) (Hui) (Miao) (Uyghur) (tibetan) Education (primary school or lower) (junior high) (senior high) (2-year college) (4-year college) (grad school) Observations Original Survey Follow-up Survey Mean Mean 0.522 0.624 37.672 25.867 5.529 5.279 10.976 7.969 0.234 0.162 0.711 0.238 0.599 0.407 0.066 0.029 (percentage) 95.1% 12.0% 12.0% 10.0% 1.0% 1.0% 0% (percentage) 93.6% 2.0% 1.3% 0.7% 0.6% 1.8% 0% 4.6% 2.9% 12.0% 20.7% 57.1% 7.0% 805 7.0% 4.1% 13.9% 32.7% 45.1% 3.5% 1,356 Note.Income(measured by annual family income):1.below 6,999...15.100,000-199,999;16. Above 200,000. Unit:Yuan. 34 3,000;2.3,000-4,999;3.5,000- Appendix B Figure B1: Survey Wording Treatment 2014 3 1 21 29 143 2 73 Treatment_variation1 2014 3 1 21 29 143 73 Treatment_variation2 2014 3 1 21 29 143 73 70 ) Importance Pride Chinalabel ) 35 ! ! 2 5 2 5 70 Shootfirst ( 5 70 Appendix C: OLS Results: Follow-Up Experiments Table C1: Follow-up Experiment: Effect of Terror Attack Terror Male Age College Married CCP HighIncome Region Constant Observations R-squared Shootfirst 0.286** (0.122) 0.301** (0.122) 0.00302 (0.00848) 0.0999 (0.119) 0.277* (0.145) 0.303** (0.150) 0.0607 (0.118) Importance -0.00178 (0.0199) -0.0179 (0.0204) 0.00122 (0.00123) 0.0123 (0.0211) -0.00345 (0.0236) 0.00562 (0.0248) -0.00379 (0.0197) 1.870*** (0.436) 390 0.161 0.763*** (0.0646) 383 0.085 36 Pride Chinalabel 0.00458 0.0518 (0.0209) (0.0489) -0.00633 -0.0776 (0.0213) (0.0522) -0.00107 0.00398 (0.00161) (0.00431) 0.0256 0.0189 (0.0232) (0.0523) -0.00780 -0.00205 (0.0267) (0.0611) 0.0125 -0.177*** (0.0261) (0.0563) -0.0271 -0.0428 (0.0219) (0.0509) 0.832*** (0.0859) 384 0.061 0.235 (0.175) 390 0.100 Table C2: Follow-up Experiment: Effect of Ethnicity Ethnicity Male Age College Married CCP HighIncome Region Constant Observations R-squared Shootfirst 0.193 (0.118) 0.376*** (0.120) 0.0102 (0.00863) 0.0195 (0.119) 0.0604 (0.145) 0.0860 (0.164) 0.241** (0.119) Importance 0.0156 (0.0193) 0.00196 (0.0197) 0.00188 (0.00128) 0.0236 (0.0198) -0.0113 (0.0246) 0.0261 (0.0245) 0.0134 (0.0195) Pride 0.0538*** (0.0177) 0.00144 (0.0181) -0.00142 (0.00136) 0.00656 (0.0181) -0.0115 (0.0220) 0.00452 (0.0261) -0.00592 (0.0169) Chinalabel -0.00356 (0.0466) 0.0248 (0.0482) 0.00277 (0.00397) 0.0132 (0.0482) 0.0567 (0.0601) -0.108* (0.0577) -0.0542 (0.0474) 2.171*** (0.394) 415 0.137 0.762*** (0.0622) 407 0.114 0.955*** (0.0492) 407 0.109 0.142 (0.158) 415 0.097 37 Table C3: Follow-up Experiment: Effect of Violence1 Violence1 Male Age College Married CCP HighIncome Region Constant Observations R-squared Shootfirst 0.196 (0.123) 0.237* (0.129) -0.00252 (0.0107) 0.0170 (0.124) 0.445*** (0.155) 0.401** (0.168) 0.120 (0.119) Importance -0.00459 (0.0208) -0.000423 (0.0214) 0.000756 (0.00218) 0.00799 (0.0207) 0.00693 (0.0281) 0.0164 (0.0278) 0.0229 (0.0203) Pride 0.000588 (0.0207) -0.00630 (0.0217) -0.00343* (0.00202) -0.0143 (0.0215) 0.0105 (0.0288) 0.0112 (0.0290) -0.0111 (0.0210) Chinalabel -0.0153 (0.0482) -0.0151 (0.0498) 0.00483 (0.00400) 0.0757 (0.0511) -0.0251 (0.0626) -0.117** (0.0597) -0.0294 (0.0471) 2.471*** (0.468) 393 0.125 0.807*** (0.0833) 386 0.079 0.963*** (0.0888) 387 0.070 0.229 (0.186) 393 0.094 38 Table C4: Follow-up Experiment: Effect of Violence2 Violence2 Male Age College Married CCP HighIncome Region Constant Observations R-squared Shootfirst 0.174 (0.118) 0.385*** (0.122) 0.0111 (0.00876) 0.0567 (0.122) 0.194 (0.147) 0.0138 (0.163) -0.00561 (0.119) Importance 0.00476 (0.0195) -0.0156 (0.0207) 0.00112 (0.00139) 0.0145 (0.0198) -0.0224 (0.0240) 0.0348 (0.0250) 0.0216 (0.0199) 2.375*** (0.412) 413 0.119 0.835*** (0.0605) 408 0.099 39 Pride Chinalabel 0.0229 0.0511 (0.0200) (0.0462) -0.0205 -0.0144 (0.0196) (0.0491) -0.00174 0.00262 (0.00143) (0.00394) -0.0162 0.0613 (0.0207) (0.0485) -0.00726 0.0560 (0.0243) (0.0570) 0.0105 -0.172*** (0.0289) (0.0594) 0.0195 -0.0883* (0.0206) (0.0465) 0.939*** (0.0575) 408 0.066 0.344** (0.171) 413 0.123