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Public sentiment in times of terror

View more insights in Social Sciences - Economics - Microeconomics

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View the page of Ashani Amarasinghe

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Ashani Amarasinghe examines how terror events affect government perceptions. For 11 months after successful attacks, fine-grained analysis indicates 11% more unhappiness. Violence, political institutions, and government competency contribute.

Image courtesy of interviewee. January 9, 2024

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This video video discusses a study on public sentiment towards governments following terrorist attacks, focusing on how citizens react differently based on the context of the event. It contrasts the strong support for the U.S. government after the 9/11 attacks with the backlash against the Spanish government after the 2004 Madrid train bombings.

The researcher examines a global sample of 135 countries from 2002 to 2020, emphasizing short-term public sentiment fluctuations rather than long-term voting data. This video video approach allows for a more immediate understanding of how citizens hold their governments accountable post-attack.

Using a unique dataset called the GAL dataset, which compiles real-time news articles, the researcher quantifies public discontent towards governments by analyzing negative sentiment in events targeting government actions. The study finds that following a terror attack, public discontent tends to rise significantly, with an effect lasting up to five months.

The research also investigates various mechanisms that might drive these sentiments, concluding that the backlash is specifically aimed at government accountability rather than general discontent related to other factors. Ultimately, the findings demonstrate a consistent system of checks and balances where citizens monitor and hold their governments accountable, reflecting significant implications for the citizen-state relationship.

Future research plans include examining public sentiment in other contexts, such as climate change, to further explore how public opinions and government responses interact over time.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.185 --> 00:02.245
So the context of the study that,

00:02.265 --> 00:03.725
we are discussing today, the study

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that's called public sentiment in times of terror, is

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how do pub the people react

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to governments following terror attacks?

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Right? So, famously,

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following the nine 11 terror attack,

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the US public really rallied around the government.

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There was lots of support

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for the government highest level since the 1970s.

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But contrast that with the situation in Spain in 2004,

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following the Madrid train bombings, where there was a lot

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of backlash against the government.

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And this terror attack was also cited as a recent

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for the government's loss at the subsequent election.

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So there's two distinct terror attacks.

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They may be different along many dimensions,

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but one thing that stands out is

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that the public's reaction deferred enormously, right?

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So this then led me to, you know,

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investigate this question further on whether there is any

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particular trends that we can observe where in with respect

00:59.965 --> 01:01.525
to how the public,

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respond following terror attacks, right?

01:03.905 --> 01:07.485
So in order to examine this question, I look at,

01:07.645 --> 01:11.165
a global sample of countries, 135 countries over the years,

01:11.185 --> 01:12.725
2002 to 2020.

01:13.425 --> 01:17.165
And the real novelty in my study actually comes from the

01:17.165 --> 01:19.445
fact that I'm looking at short-term

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fluctuations in public sentiments towards governments.

01:23.625 --> 01:25.125
I'm not looking at voting data

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or any kind of annual long-term data,

01:27.745 --> 01:31.125
but I'm really trying to exploit the truly short-term

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dynamics of the citizen's state relationship

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following terror attacks.

01:35.345 --> 01:39.085
So terrorism has been an area of interest for economics

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for a long, long time, and you find many,

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studies looking at the effects of terror attacks on say,

01:45.485 --> 01:49.125
employment wages, GDP migration, many areas.

01:49.545 --> 01:51.645
But then within this literature,

01:51.945 --> 01:56.905
my work actually contributes to the kind of niche,

01:56.905 --> 01:59.785
component which looks at the outcomes of terrorism

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and how terrorism affects political

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outcomes in particular, right?

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So, but as I mentioned

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before, most of what has been done

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so far relies on voting data.

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For example, Alvo 2011 or

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or Bali 2007,

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they look at this particular Madrid train attacks

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and look at how the,

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incumbent government was actually voted out in

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the subsequent election.

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But the problem with using voting data is that, you know,

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elections happen only five or six years.

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Um, and then there's lots going on in between,

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and it's very difficult for us to say it's this one event

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that affected the voting outcome, right?

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That seems a little farfetched.

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And there's actually quite a bit of literature that sh

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shows that events that happened closer

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to the election actually has more salience

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in the voters' minds.

02:45.325 --> 02:48.145
Um, and then, you know, people actually react to

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what happened closer to the election,

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and they kind of forget what happened further away.

02:52.005 --> 02:55.145
So that's why looking at these short-term dynamics becomes

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really, really important.

02:56.885 --> 03:00.805
Um, if you look at short-term dynamics, then you can observe

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how the gov people respond

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to the government's actions consistently throughout time

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and not being limited to, election cycles.

03:08.705 --> 03:10.645
And that's really the new contribution

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that I'm trying to make with this paper.

03:12.425 --> 03:13.485
And, I've,

03:13.665 --> 03:17.925
my broader research area agenda is also linked to these kind

03:17.925 --> 03:20.845
of, you know, short term,

03:21.325 --> 03:23.845
fluctuations in the citizen state relationship.

03:24.105 --> 03:26.245
You can imagine that the key challenge was

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that there exists almost,

03:29.105 --> 03:32.965
no data on consistently measuring public sentiments towards

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government as very fine temporal intervals, right?

03:36.225 --> 03:39.605
So I had to come up with a new, kind of quantification

03:39.785 --> 03:41.005
to answer this question.

03:41.185 --> 03:43.885
And actually, in my previous paper,

03:43.885 --> 03:47.005
diverting Domestic Turmoil, which came out in 2022 Journal

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of Public Economics, that's

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where I first introduced this idea

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of a quantified indicator, a quantified consistent indicator

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of public discontent, towards the government.

03:57.185 --> 04:00.365
And I use this massive dataset called the GAL Dataset,

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which is a real time, data set of news media articles

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that are being reported across the world.

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It's updated every 15 minutes.

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And then what G DAL does is it takes all the key information

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from these news articles, say, who are the actors involved?

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Who was it targeted, against,

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which country, which location?

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And, you know, what, what kind of an event was it?

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Was it a positive event or an aggressive event?

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And it also assigns a sentiment score to each

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of these events called the, Goldstein score.

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So in my, dataset to quantify public discontent,

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I filtered through this massive g data set,

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and I retained the subset

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of events which were particularly targeted towards the

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government of a country.

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And then I take the share, I take the number

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of events which were targeted against the government,

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and which had a very high negative sentiment score,

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and I expressed that as a, as a share

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of the total number of events

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that were targeted towards the

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government in the same period.

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And that's how I arrive at this quantified indicator

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of public dis content.

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And I have this dataset as, let's say, if I want

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to quantify public discontent at the day level

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or the week level or the month level, I can still do that

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for the purpose of this study.

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I use monthly, deed data,

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for public this content.

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So that's the key outcome variable of my study.

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And then obviously, because I'm looking at terrorist

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attacks, I did data on, terror events.

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And, for that I use data from the global terrorism

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dataset, which is like the gold standard

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for terrorism datasets.

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And then I combine that with lots of other data sets,

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which help me examine mechanisms

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and, you know, eliminate certain, things

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that one could suggest are going on, like

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for these results to, appear.

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And then in terms of the methodology I use,

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I use a combination of event study

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and difference indifferent, methodologies where I'm,

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I take a two-step approach to my identification.

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So firstly, I look at, I just compare country months

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where terror attacks occurred with country months

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where no terror attacks occurred, right?

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So, okay, in one country month there was a terror attack in

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another, there was no terror attack.

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And then I look at, okay,

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what happens when there's a terror attack?

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What's the difference? And then I can understand whether

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people rally around the government

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or whether people start criticizing the government more.

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However, this kind of comparison

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is not exactly an appropriate comparison

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for causal purposes,

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because there could be so many reasons why these two units

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of observation are different from each other, right?

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So we are not exactly comparing apples to apples, right?

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So therefore, I go the next step

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and use the identification strategy, which was,

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which has been little bit used in the literature,

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for example, most recently in broader 2018,

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where the comparison is between country months,

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where there was a successful terror attack against country

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months where there was a failed terror attacks.

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So we are essentially restricting our analysis

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to country months where terror attacks occurred,

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but for some random reason,

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the attack was successful in one set of country months,

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and the attack failed in the other set of country months.

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So the key assumption here is that,

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conditional on the timing

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and location of the event,

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whether a terrorist attack is successful

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or not is entirely random.

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And this random nature of my identification allows me

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to make causal, interpretation of my estimates.

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Primarily, what I find is that following a terror attack,

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there is a ri huge rise in public

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discontent towards the government.

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So people are actually very unhappy

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with the government following terror attacks.

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So remember at the beginning I mentioned that it could be

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that people really support the government,

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or it could be that people really criticize the government.

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So on average, the effect that materializes is actually

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that, people are actually holding the government

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accountable and criticizing the government

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for not keeping them safe.

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That's the, the first kind of result,

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the baseline estimates that I find.

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So this basically suggests that, you know,

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there's a very strong direction of the effect,

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and that this effect is actually very important even in the

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very short run, right?

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For example, in the event study estimates, I find that,

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public discontent rise for up

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to five months following a terror attack.

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Now, if you were using election data,

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we wouldn't observe this, right?

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So that's where the real novelty comes in.

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And, following this, I go on to explore what kind

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of mechanisms could be driving these estimates, right?

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So is it government accountability itself

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or could it be that, for example,

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people feel sympathy with terrorists?

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Or could it be that there is a general increase in this

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content not related to the terror attack, right?

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So it could be like discontented disco content targeted at

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opposition or some other parties.

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But then I'm able to rule out these kind of hypothesis

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because I can quantify public sentiments towards terror

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groups, public sentiments towards businesses, elites,

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opposition, and I'm unable

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to find any effects on these outcome variables

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following a terror attack.

09:05.305 --> 09:08.045
So suggests this, you know, overall suggests

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that this is actually, discontent,

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specifically targeted towards the government, right?

09:14.105 --> 09:18.205
And then I also compile a number of data sets on,

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let's say country level characteristics,

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attack level characteristics,

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and leader level characteristics.

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So I find that,

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public discontent rises specifically in countries which have

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non-democratic political institutions, in countries

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with low levels of military capacity, for example.

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So all of this suggests

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that countries a country level characteristics matter.

09:40.705 --> 09:43.645
And then I also find that people are more likely

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to be forgiving towards the government if there is a female

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leader in power, or if there's a new

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or young leader in power,

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or if the leader has some military experience.

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So people actually take into consideration all these

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available information when,

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deciding on their reaction towards a government following a

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terror attack and taking together,

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eliminating certain channels

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and providing support in favor of certain channels.

10:12.105 --> 10:14.405
Um, together the overall picture is

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that people actually do hold the government accountable,

10:17.665 --> 10:21.605
and that seems to be the key, underlying mechanism,

10:22.185 --> 10:23.485
in my estimates.

10:23.635 --> 10:25.765
Basically, the conclusion here is that yes,

10:25.765 --> 10:28.685
people do hold their government accountable,

10:28.785 --> 10:31.245
and that this is actually an important part

10:31.345 --> 10:35.365
of the short term relationships between, people

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and their government, right?

10:36.705 --> 10:38.485
So this, we observed that people

10:39.125 --> 10:41.165
actually keep monitoring the behavior

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of the government even in the short term

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and not necessarily when it's closer to any election.

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So this is especially important in the context

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of the citizen state relationship

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and how governments use their power.

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It's important that the government know

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that they are consistently being monitored by the people.

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There's a consistent system of checks

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and balances that operates by the public.

11:03.105 --> 11:07.125
Um, and, and, that probably can be a deterrent

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for the government if, if they're to make suboptimal,

11:10.605 --> 11:14.045
levels of, you know, suboptimal decisions, so to say.

11:14.345 --> 11:18.725
So basically the whole idea is that I want to convey is

11:18.725 --> 11:20.765
that this study provides, some

11:20.765 --> 11:24.285
of the first evidence about the short term,

11:24.925 --> 11:27.725
feedback loop between people and the government, right?

11:27.905 --> 11:31.405
And in my broader research agenda, I'm,

11:31.545 --> 11:34.285
now using this public disco content index,

11:34.305 --> 11:36.405
to look at extended areas.

11:36.745 --> 11:38.405
For example, how does the public

11:38.545 --> 11:41.605
and government interact when it comes to the climate change,

11:42.145 --> 11:43.205
dialogue, right?

11:43.625 --> 11:47.645
And how do, the government respond when they know

11:47.645 --> 11:49.125
that people are unhappy with them.

11:49.505 --> 11:52.845
Um, so basically that's, that's the broad research

11:52.845 --> 11:56.445
Agenda that I'm pursuing with the, temporarily

11:57.005 --> 11:59.525
granular data on public

12:00.065 --> 12:03.925
and how public, provide their feedback to governments

12:03.925 --> 12:07.765
and how governments, then respond to how, public,

12:08.065 --> 12:09.685
to public sentiments, right?

12:09.985 --> 12:12.165
So this feedback loop is really important

12:12.225 --> 12:15.805
to be observed in the fine temporal, dimensions.

12:15.945 --> 12:19.045
And for example, I'm working now on extending this

12:19.045 --> 12:22.165
to other areas such as the climate change discussion,

12:22.165 --> 12:24.445
where it's very important for governments

12:24.505 --> 12:27.365
and, public to interact,

12:27.385 --> 12:29.365
and many other aspects of this,

12:29.365 --> 12:31.405
citizen state relationship, which can be

12:31.925 --> 12:34.085
observed at these really fine temporal dimensions.

00:00 - So the context of the study that, we are discussing today, the study that's called public sentiment in times of terror, is how do pub the people react to governments following terror attacks? Right? So, famously,

00:13 - following the nine 11 terror attack, the US public really rallied around the government. There was lots of support for the government highest level since the 1970s. But contrast that with the situation in Spain in 2004, following the Madrid train bombings, where there was a lot of backlash against the government. And this terror attack was also cited as a recent for the government's loss at the subsequent election. So there's two distinct terror attacks.

00:42 - They may be different along many dimensions, but one thing that stands out is that the public's reaction deferred enormously, right? So this then led me to, you know, investigate this question further on whether there is any particular trends that we can observe where in with respect to how the public, respond following terror attacks, right? So in order to examine this question, I look at, a global sample of countries, 135 countries over the years,

01:11 - 2002 to 2020. And the real novelty in my study actually comes from the fact that I'm looking at short-term fluctuations in public sentiments towards governments. I'm not looking at voting data or any kind of annual long-term data, but I'm really trying to exploit the truly short-term dynamics of the citizen's state relationship following terror attacks. So terrorism has been an area of interest for economics

01:39 - for a long, long time, and you find many, studies looking at the effects of terror attacks on say, employment wages, GDP migration, many areas. But then within this literature, my work actually contributes to the kind of niche, component which looks at the outcomes of terrorism and how terrorism affects political outcomes in particular, right? So, but as I mentioned before, most of what has been done

02:06 - so far relies on voting data. For example, Alvo 2011 or or Bali 2007, they look at this particular Madrid train attacks and look at how the, incumbent government was actually voted out in the subsequent election. But the problem with using voting data is that, you know, elections happen only five or six years. and then there's lots going on in between,

02:29 - and it's very difficult for us to say it's this one event that affected the voting outcome, right? That seems a little farfetched. And there's actually quite a bit of literature that sh shows that events that happened closer to the election actually has more salience in the voters' minds. and then, you know, people actually react to what happened closer to the election, and they kind of forget what happened further away.

02:52 - So that's why looking at these short-term dynamics becomes really, really important. if you look at short-term dynamics, then you can observe how the gov people respond to the government's actions consistently throughout time and not being limited to, election cycles. And that's really the new contribution that I'm trying to make with this paper. And, I've, my broader research area agenda is also linked to these kind

03:17 - of, you know, short term, fluctuations in the citizen state relationship. You can imagine that the key challenge was that there exists almost, no data on consistently measuring public sentiments towards government as very fine temporal intervals, right? So I had to come up with a new, kind of quantification to answer this question. And actually, in my previous paper, diverting Domestic Turmoil, which came out in 2022 Journal

03:47 - of Public Economics, that's where I first introduced this idea of a quantified indicator, a quantified consistent indicator of public discontent, towards the government. And I use this massive dataset called the GAL Dataset, which is a real time, data set of news media articles that are being reported across the world. It's updated every 15 minutes. And then what G DAL does is it takes all the key information from these news articles, say, who are the actors involved?

04:15 - Who was it targeted, against, which country, which location? And, you know, what, what kind of an event was it? Was it a positive event or an aggressive event? And it also assigns a sentiment score to each of these events called the, Goldstein score. So in my, dataset to quantify public discontent, I filtered through this massive g data set, and I retained the subset of events which were particularly targeted towards the

04:42 - government of a country. And then I take the share, I take the number of events which were targeted against the government, and which had a very high negative sentiment score, and I expressed that as a, as a share of the total number of events that were targeted towards the government in the same period. And that's how I arrive at this quantified indicator of public dis content.

05:04 - And I have this dataset as, let's say, if I want to quantify public discontent at the day level or the week level or the month level, I can still do that for the purpose of this study. I use monthly, deed data, for public this content. So that's the key outcome variable of my study. And then obviously, because I'm looking at terrorist attacks, I did data on, terror events. And, for that I use data from the global terrorism

05:30 - dataset, which is like the gold standard for terrorism datasets. And then I combine that with lots of other data sets, which help me examine mechanisms and, you know, eliminate certain, things that one could suggest are going on, like for these results to, appear. And then in terms of the methodology I use, I use a combination of event study and difference indifferent, methodologies where I'm,

05:57 - I take a two-step approach to my identification. So firstly, I look at, I just compare country months where terror attacks occurred with country months where no terror attacks occurred, right? So, okay, in one country month there was a terror attack in another, there was no terror attack. And then I look at, okay, what happens when there's a terror attack? What's the difference? And then I can understand whether people rally around the government

06:21 - or whether people start criticizing the government more. However, this kind of comparison is not exactly an appropriate comparison for causal purposes, because there could be so many reasons why these two units of observation are different from each other, right? So we are not exactly comparing apples to apples, right? So therefore, I go the next step and use the identification strategy, which was, which has been little bit used in the literature,

06:46 - for example, most recently in broader 2018, where the comparison is between country months, where there was a successful terror attack against country months where there was a failed terror attacks. So we are essentially restricting our analysis to country months where terror attacks occurred, but for some random reason, the attack was successful in one set of country months, and the attack failed in the other set of country months. So the key assumption here is that,

07:16 - conditional on the timing and location of the event, whether a terrorist attack is successful or not is entirely random. And this random nature of my identification allows me to make causal, interpretation of my estimates. Primarily, what I find is that following a terror attack, there is a ri huge rise in public discontent towards the government. So people are actually very unhappy

07:39 - with the government following terror attacks. So remember at the beginning I mentioned that it could be that people really support the government, or it could be that people really criticize the government. So on average, the effect that materializes is actually that, people are actually holding the government accountable and criticizing the government for not keeping them safe. That's the, the first kind of result, the baseline estimates that I find.

08:01 - So this basically suggests that, you know, there's a very strong direction of the effect, and that this effect is actually very important even in the very short run, right? For example, in the event study estimates, I find that, public discontent rise for up to five months following a terror attack. Now, if you were using election data, we wouldn't observe this, right? So that's where the real novelty comes in.

08:24 - And, following this, I go on to explore what kind of mechanisms could be driving these estimates, right? So is it government accountability itself or could it be that, for example, people feel sympathy with terrorists? Or could it be that there is a general increase in this content not related to the terror attack, right? So it could be like discontented disco content targeted at opposition or some other parties. But then I'm able to rule out these kind of hypothesis

08:53 - because I can quantify public sentiments towards terror groups, public sentiments towards businesses, elites, opposition, and I'm unable to find any effects on these outcome variables following a terror attack. So suggests this, you know, overall suggests that this is actually, discontent, specifically targeted towards the government, right? And then I also compile a number of data sets on, let's say country level characteristics,

09:20 - attack level characteristics, and leader level characteristics. So I find that, public discontent rises specifically in countries which have non-democratic political institutions, in countries with low levels of military capacity, for example. So all of this suggests that countries a country level characteristics matter. And then I also find that people are more likely to be forgiving towards the government if there is a female

09:48 - leader in power, or if there's a new or young leader in power, or if the leader has some military experience. So people actually take into consideration all these available information when, deciding on their reaction towards a government following a terror attack and taking together, eliminating certain channels and providing support in favor of certain channels. together the overall picture is

10:14 - that people actually do hold the government accountable, and that seems to be the key, underlying mechanism, in my estimates. Basically, the conclusion here is that yes, people do hold their government accountable, and that this is actually an important part of the short term relationships between, people and their government, right? So this, we observed that people actually keep monitoring the behavior

10:41 - of the government even in the short term and not necessarily when it's closer to any election. So this is especially important in the context of the citizen state relationship and how governments use their power. It's important that the government know that they are consistently being monitored by the people. There's a consistent system of checks and balances that operates by the public. and, and, that probably can be a deterrent

11:07 - for the government if, if they're to make suboptimal, levels of, you know, suboptimal decisions, so to say. So basically the whole idea is that I want to convey is that this study provides, some of the first evidence about the short term, feedback loop between people and the government, right? And in my broader research agenda, I'm, now using this public disco content index, to look at extended areas. For example, how does the public

11:38 - and government interact when it comes to the climate change, dialogue, right? And how do, the government respond when they know that people are unhappy with them. so basically that's, that's the broad research Agenda that I'm pursuing with the, temporarily granular data on public and how public, provide their feedback to governments and how governments, then respond to how, public, to public sentiments, right?

12:09 - So this feedback loop is really important to be observed in the fine temporal, dimensions. And for example, I'm working now on extending this to other areas such as the climate change discussion, where it's very important for governments and, public to interact, and many other aspects of this, citizen state relationship, which can be observed at these really fine temporal dimensions.

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