Download Rally `Round the Red Flag: The Effects of Terror Attacks

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. Besides self-reported public opinion data, future
research may consider using other types of data, which suffer less from misreporting biases (e.g.,
government records in Hersh 2013), to understand how attitudes and behaviors are shaped by
terrorist attacks and other types of violence.
25
References
Abrahms, Max. 2006. “Why Terrorism Does not Work.” International Security 31:42–78.
Abrahms, Max. 2012. “The Political Effectiveness of Terrorism Revisited.” Comparative Political
Studies 45:366–393.
Alberto, Alesina and Bryony Reich. 2015. “Nation Building.” NBER Working Paper .
Bali, Valentina A. 2007. “Terror and elections: Lessons from Spain.” Electoral Studies 26(3):669–
687.
Baughn, C. Christopher and Attila Yaprak. 1993. Mapping Country-of-Origin Research: Recent
Developments and Emerging Avenues. In Product-country images: impact and role in international marketing, ed. Heslop A. Papadopoulos, Louise. New York, NY: Routledge.
Benmelech, Efraim and Claude Berrebi. 2007. “Human Capital and the Productivity of Suicide
Bombers.” Journal of Economic Perspectives 21(3):223–238.
Berman, Eli and David D Laitin. 2005. “Hard Targets: Theory and Evidence on Suicide Attacks.”
NBER Working Paper 11740 .
Berrebi, Claude and Esteban F Klor. 2008. “Are voters sensitive to terrorism? Direct evidence
from the Israeli electorate.” American Political Science Review 102(3):279–301.
Blank, Thomas. 2003. “Determinants of National Identity in East and West Germany: An Empirical Comparison of Theories on the Significance of Authoritarianism, Anomie, and General
Self-Esteem.” Political Psychology 24(2).
Blank, Thomas and Peter Schmidt. 2003. “National Identity in a United Germany: Nationalism
or Patriotism? An Empirical Test with Representative Data.” Political Psychology 24(2).
Brubaker, Rogers and David D. Latin. 1998. “Ethnic and Nationalist Violence.” Annual Review of
Sociology 24:423–52.
Bueno de Mesquita, Ethan. 2005. “The Quality of Terror.” American Journal of Political Science
49(3):515–530.
Cantoni, Davide, Yuyu Chen, David Y. Yang, Noam Yuchtman and Jane Y. Zhang. 2012. “Curriculum and Ideologoy.” NBER Working Paper 20112 .
Chanley, Virginia A. 2009. “Trust in Government in the Aftermath of 9 / 11 : Determinants and
Consequences.” Political Psychology 23(3):469–483.
26
Charnysh, Volha, Christopher Lucas and Prerna Singh. 2015. “The Ties that Bind : National
Identity Salience and Pro-Social Behavior Toward the Ethnic Other.” Comparative Political
Studies 48:267–300.
Chen, Jidong, Jennifer Pan and Yiqing Xu. 2015. “Sources of Authoritarian Responsiveness: A
Field Experiment in China.” American Journal of Political Science .
Chen, Jie and Bruce J Dickson. 2008. “Allies of the State: Democratic Support and Regime
Support among China’s Private Entrepreneurs.” The China Quarterly 196(December):780–804.
Chen, Xueyi and Tianjian Shi. 2001. “Media effects on political confidence and trust in the People’s
Republic of China in the post-Tiananmen period.” East Asia 19(3):84–118.
Chung, Chien-peng. 2006. “Confronting Terrorism and Other Evils in China : All Quiet on the
Western Front?” China and Eurasia Forum Quarterly 4(2):75–87.
Chung, Eun Bin. 2015. “Can Affirming National Identity Increase International Trust? Experimental Evidence from South Korean, Chinese, and Japanese Nationals.” International Studies
Review 16(1).
Cramer, Jane Kellett. 2007. “Militarized Patriotism: Why the U.S. Marketplace of Ideas Failed
Before the Iraq War.” Security Studies 16(3).
Crocker, Jennifer and Riia Luhtanen. 1990. “Collective Selfesteem and Ingroup Bias.” Journal of
Personality and Social Psychology 58(1).
Davenport, Christian. 2007. “State Repression and Political Order.” Annual Review of Political
Science 10(1):1–23.
Davis, Darren W. and Briand D. Silver. 2004. “Civil Liberties vs Security: Public Opinion in the
Context of the Terrorist Attacks on America.” American Journal of Political Science 48(1):28–46.
Distelhorst, Greg and Yue Hou. 2014. “Ingroup Bias in Official Behavior: A National Field Experiment in China.” Quarterly Journal of Political Science 9(2):203–230.
Distelhorst, Greg and Yue Hou. 2015. “Constituency Service Under Nondemocratic Rule: Evidence
from China.” Working Paper .
Eagly, Alice H. and Shelly Chaiken. 1993. The Psychology of Attitudes. Orlando, FL: Harcourt
Brace Jovanovich College Publishers.
Easterly, William. 2001. “The middle class consensus and economic development.” Journal of
Economic Growth 6(4):317–335.
27
Gadarian, Shana Kushner. 2010. “The Politics of Threat: How Terrorism News Shapes Foreign
Policy Attitudes.” The Journal of Politics 72(2):469.
Gadarian, Shana Kushner. 2014. “Scary Pictures: How Terrorism Imagery Affects Voter Evaluations.” Political Communication 31(2):282–302.
Gould, Eric D. and Esteban F. Klor. 2010. “Does terrorism work?” Quarterly Journal of Economics
(November).
Gries, Peter Hays. 2004. China’s New Nationalism: Pride, Politics, and Diplomacy. University of
California Press.
Herrmann, Richard K., Pierangelo Isernia and Paolo Segatti. 2009. “Attachment to the Nation
and International Relations: Dimensions of Identity and Their Relationship to War and Peace.”
Political Psychology 30(5).
Hersh, Eitan D. 2013. “Long-term effect of September 11 on the political behavior of victims’
families and neighbors.” Proceedings of the National Academy of Sciences of the United States
of America 110(52):20959–63.
Hetherington, Marc J. and Michael Nelson. 2003. “Anatomy of a Rally Effect: George W. Bush
and the War on Terrorism.” PS: Political Science & Politics 36(01).
Hoffman, Bruce. 2006. Inside Terrorism. Columbia University Press.
Hoffmann, Robert and Jeremy Larner. 2013. “The Demography of Chinese Nationalism:A FieldExperimental Approach.” The China Quarterly .
Hong, Ji Yeon and Woo Chang Kang. 2014. “Trauma and Stigma : the Long-term Effects of
Wartime Violence on Political Attitudes.” pp. 1–40.
Hopkins, Daniel J. 2009. “The Diversity Discount: When Increasing Ethnic and Racial Diversity
Prevents Tax Increases.” The Journal of Politics 71(01):160.
Huang, Haifeng. 2015. “International Knowledge and Domestic Evaluations in a Changing Society
: The Case of China.” American Political Science Review .
Huddy, Leonie and Nadia Khatib. 2007. “American Patriotism, National Identity, and Political
Involvement.” American Journal of Political Science 51(1):63–77.
Kaufmann, Chaim. 2004. “Threat Inflation and the Failure of the Marketplace of ideas.” International Security 29(1).
28
Kibris, Arzu. 2011. “Funerals and Elections: The Effects of Terrorism on Voting Behavior in
Turkey.” Journal of Conflict Resolution 55:220–247.
Ko, Jiyoung. 2015. “Nationalism and Foreign Policy Preference: Evidence from a Survey Experiment in China.” China and International Relations Graduate Research Workshop, University of
Pennsylvania .
Kosterman, Rick and Seymour Feshbach. 1989. “Toward a Measure of Patriotic and Nationalistic
Attitudes.” Political Psychology .
Krueger, Alan B. and Jitka Maleckova. 2003. “Education, Poverty and Terrorism: Is There a
Causal Connection?” Journal of Economic Perspective 17(4):119–144.
Kunovich, Robert M. 2009. “The Sources and Consequences of National Identification.” American
Sociological Review 74(4):573–593.
Lan, Xiaohuan and Ben Li. N.d. “The Economics of Nationalism.” American Economic Journal:
Economic Policy. Forthcoming.
Lorentzen, Peter. 2013. “Regularizing rioting: Permitting public protest in an authoritarian
regime.” Quarterly Journal of Political Science 8(2):127–158.
Panagopoulos, Costas. 2006. “The polls-trends: Arab and Muslim Americans and Islam in the
aftermath of 9/11.” Public Opinion Quarterly 70(4):608–624.
Pape, Robert A. 2003. “The Strategic Logic of Suicide Terrorism.” The American Political Science
Review 97(3):343–361.
Peffley, M., P. Knigge and J. Hurwitz. 2001. “A Multiple Values Model of Political Tolerance.”
Political Research Quarterly 54(2):379–406.
Potter, Philip B.K. 2013. “Terrorism in China: Growing Threats with Global Implications.”
Strategic Studies Quarterly Winter.
Saideman, S.M. and R.W. Ayres. 2008. For Kin or Country: Xenophobia, Nationalism, and War.
Columbia University Press.
Sands, Melissa L. 2014. “How Crises and Threats Affect Political Behavior : The Boston Marathon
Bombings as a Natural Experiment.” Working Paper pp. 1–23.
Schrock-Jacobson, Gretchen. 2012. “The Violent Consequences of the Nation: Nationalism and
the Initiation of Interstate War.” Journal of Conflict Resolution 56(5):825–852.
29
Shayo, Moses. 2009. “A Model of Social Identity with an Application to Political Economy: Nation,
Class, and Redistribution.” American Political Science Review 103(02):147.
Shirk, Susan. 2014. The Domestic Context of Chinese Foreign Security Policies. In Oxford Handbook
of the International Relations of Asia, ed. Aadia M. Pekkanen, John Ravenhill and Rosemary
Foot. New York: Oxford University Press.
Sniderman, Paul M., Joseph F. Fletcher, Peter H. Russell and Philip E. Tetlock. 1996. The Clash of
Rights: Liberty, Equality, and Legitimacy in Pluralist Democracy. New Haven: Yale University
Press.
Sniderman, Paul M., Louk Hagendoorn and Markus Prior. 2004. “Predisposing Factors and Situational Triggers : Exclusionary Reactions to Immigrant Minorities.” American Political Science
Review 98(1):35–49.
Truex, Rory. 2014a. “Consultative Authoritarianism and Its Limits.” Comparative Political Studies
.
Truex, Rory. 2014b. “Does China Want Change? Implicit Attitudes Towards an Authoritarian
Regime.”.
van der Schalk, Job, Agneta Fischer, Bertjan Doosje, Daniel Wigboldus, Skyler Hawk and Mark
Rotteveel. 2011. “Convergent and Divergent REsponses to Emotional Display of Ingroup and
Outgroup.” Emotion 11(2).
Verlegh, Peeter WJ. 2007. “Home country bias in product evaluation: the complementary roles of
economic and socio-psychological motives.” Journal of International Business Studies 38(3):361–
373.
Wade, Sara Jackson and Dan Reiter. 2007. “Does Democracy Matter? Regime Type and Suicide
Terrorism.” Journal of Conflict Resolution 51(2):329–348.
Wei, Shan and Gang Chen. 2011. “The Urumqi Riots and China’s Ethnic Policy in Xinjiang.” East
Asian Policy (3).
Weiss, Jessica Chen. 2013. “Authoritarian Signaling, Mass Audiences, and Nationalist Protest in
China.” International Organization 67(1):1–35.
Yee, Herbert S. 2003. “Ethnic Relations in Xinjiang: A survey of Uygur-Han relations in Urumqi.”
Journal of Contemporary China 12(36):431–452.
30
Appendix A
Table A1: Principal Component Analysis of the Nationalism Variables
Principal Component Factors
Factor1
Factor2
Factor3
1.22977
-0.01273
-0.20939
Importance
Pride
Chinalabel
Factor Loading
Factor1
0.7712
0.7673
0.215
Uniqueness(%)
40.52
41.13
95.38
Note. Factor loading of the three nationalism variable: Importance, Pride, and Chinalabel. These
three variables share one common factor.
Table A2: Balance table
Male
Age
Income (Group)
Married
Education(Group)
CCP
Atheist
Obs.
Treatment Control P-value
0.493
0.521
0.4545
38.6
37.4
0.2146
10.59
11.17
0.1131
0.69
0.76
0.0325
5.49
5.48
0.9484
0.22
0.22
0.8747
0.79
0.73
0.0729
486
312
Note. 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