Targeting Party Policies: Are Certain Governments
More Susceptible to Transnational Terrorism
Kathleen Deloughery∗
March 15, 2008
Abstract
The goal of this paper is to determine if the political orientation of the leadership
of a country affects the likelihood of that country being the target of a terrorist attack.
Econometric techniques are used to reduce the bias due to potential two-way causation
by correcting for the possibility that being the target of terrorist attacks in the past
determines which political party is voted into power in the future. I will correct for
this by utilizing variables that influence elections as instruments. This paper will build
on the existing literature discussing who is most likely to be the target of a terrorist
attack. More importantly, this research can provide individuals a way to reduce the
likelihood of their country being attacked through the leaders they elect. Additionally,
countries will be able to better measure the risk they face. Finally, it might be possible
to learn more about the mechanism through which terrorists choose their targets by
determining if political orientation affects the probability of becoming a target.
Results show that joining political or economic unions with other countries is highly
correlated with the number of terrorist attacks a country faces. However, separation of
church and state, and government control over production and the economy are issues
that appear to predict terror activity under several specifications. Results show that
countries with capitalist markets face more terrorist attacks. Additionally, countries
that support state involvement in religion also experience more terrorist attacks. A
ten percent change in the policy scores of these issues could influence the number of
terror attacks in a country by anywhere between 0.3 and 0.76 attacks in a given year1 .
∗
Please email deloughery.1@osu.edu for any questions or comments about this paper. This is a preliminary
draft, please do not quote or cite without permission.
1
This research was performed under an appointment to the Department of Homeland Security (DHS)
Scholarship and Fellowship Program, administered by the Oak Ridge Institute for Science and Education
(ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and DHS.
ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE contract number DE-AC0506OR23100. All opinions expressed in this paper are the author’s and do not necessarily reflect the policies
and views of DHS, DOE, or ORAU/ORISE.
1
1
Introduction
While terrorism has been an international security issue for decades, more researchers from
economics, political science, international relations, and sociology have examined aspects of
terrorism since the attacks of September 11th, 2001. Most of these studies have examined
either the optimal response to terrorism or the consequences and causes of terrorism. Despite the growth of the amount of research on terrorism, there have been very few studies
examining the choices made by terrorist organizations. Economists are well suited to answer
questions about terrorist actions because terrorist organizations are thought to behave rationally by responding to incentives2 . Since terrorist organizations behave rationally, they must
have objectives: gaining supporters and coercing target governments. These objectives will
be at the forefront in all of their decisions. Like other individuals, terrorists maximize their
utility by consuming goods subject to their limited resources. Unlike other individuals, these
goods can be produced through terrorist and non-terrorist activities. Examples of goods produced through terrorism are political instability, media attention, and declining resolve of
the target government. In order to produce these commodities, terrorist organizations must
decide the location, method, timing, and target of each attack. Additionally, few studies
have been done in economics on how to reduce terrorism. Most research of this nature has
focused on whether countries should follow a strategy of deterrence or preemption in fighting
terrorism. However, these decisions are usually made after terrorist attacks occur. Instead,
it is important to examine why terrorists choose certain countries to attack and not others.
Therefore, a paper analyzing governmental policies and how they affect terrorism in that
country is important.
The first aspect to undertake when writing this paper is to define terrorism.
According to Alex Schmid and Albert Jongman, there are over 100 definitions of terrorism,
both scholarly and political. However, these researchers find that there are two main ingredients in any terrorism definition: the presence or threat of violence and a political or
social motive. The three secondary aspects included in most of the definitions are the victim, perpetrator, and audience. Additionally there are two important aspects that separate
terrorism from crime. First, terrorism has a ideological objective, where premeditated crime
usually has a purely economic motive3 . Second, a terrorist act is carried out in the hope
of affecting a group larger than the immediate targets. This difference between crime and
2
Sandler and Enders The Political Economy of Terrorism has a section in the first chapter about the
rationality of terrorist organizations
3
Krueger and Maleckova make this argument in their 2003 paper
2
terrorism plays into the goal of the terrorist organization to coerce a targeted government to
alter its policies. A commonly used definition is: terrorism is the premeditated threat or use
of excessive violence to obtain a political, religious, or ideological objective by intimidation
or fear. Terrorist attacks appear random to the general population, perpetuating the fear
that they bring about. However, researchers have examined terrorist attacks and found patterns. For instance, evidence shows that democratic countries are the most likely targets of
suicide attacks. This finding occurs because eliciting fear in society has the most impact in a
democratic society. Enacting change is easier in these societies, because individuals have the
power to elect their officials. Terrorist organizations hope that the fear and anxiety caused
by an attack, and the possibility of future violence, will cause citizens to persuade their
government to make concessions. Additionally, terrorism is more likely to succeed when the
group operates in an open society because of unrestricted travel and free press.
This paper plans to build on these previous findings by examining whether the
political makeup of the leadership of a country determines its likelihood of being the target of
a terrorist attack. I will study policy stances on different issues and their correlation with the
number and intensity of terrorist attacks a country faces. The main motivation of this paper
is to better understand if the policies in place affect a nations likelihood of being targeted. For
example, suppose the issue of importance is support of the military. In this case, a country
with a strong positive stance believes in infusing resources into the armed forces. One can
imagine that a country with a strong military presence might be better able to deter terrorist
attacks. In addition, terrorist organizations might fear retaliatory attacks from countries with
strong militaries, which could then make the terrorist organization want to shift attacks away
from those countries. Both of these effects would have the overall result of lowering terrorist
attacks on countries with large militaries. On the other hand, a country that is anti-military,
meaning they argue against the presence of security forces larger than necessary to maintain
their borders, could illicit fewer attacks because either they are no longer a provocative
target, or because their military policies4 do not offend terrorist organizations. The question
becomes, which of these explanations is correct. Even if both explanations are valid, which
one dominates? Additionally, this paper can help policymakers discover which policies are
the most important for deterring terrorism. For instance, is military support important to
terrorist organizations, or is it social welfare issues, or religious issues? Finally, this paper
can also shed light on how terrorist organizations choose their targets. Few papers examine
terrorist decision making, but understanding how these organizations operate is critical.
4
For instance, building military installations in foreign countries
3
2
Literature Review
The most common line of research on terrorism deals with the consequences of terrorism.
For instance, Abadie and Gardeazabal (2003) examine how terrorist violence affects GDP
in the Basque Country. Drakos and Kutan (2003) study how terrorism affects regional
tourism in mediterranean countries. Beyond that, most researchers, especially in economics,
choose to examine optimal responses to terrorists. For instance, Sandler and Siqueiara
(2003) examine the decision of a targeted government to quell future attacks by employing
a strategy of deterrence or preemption. Additionally, Arce and Sandler (2002) examine
whether or not governments should negotiate with hostage-taking terrorist organizations.
Little research has been done in economics on why terrorist organizations choose certain
countries to target. However, these studies are potentially the most important. If we can
understand the mechanism through which terrorist organizations decide who to target, then
we will be better equipped to predict and thwart future attacks.
In The Strategic Logic of Suicide Terrorism, Robert Pape examines the option
of terrorist organizations to employ suicide attacks as a method. Suicide attacks can be
less costly for terrorist organizations. The planning that goes into a suicide mission is not
as complex, because the perpetrator does not need to orchestrate escape or contingency
plans. Also, the attacker can make last minute adjustments to ensure that the mission is
successful. Additionally, Pape finds that suicide attacks are more lethal. While only three
percent of terrorist attacks are suicide attacks, these attacks account for almost half of all
deaths due to terrorism. An individual who is willing to die for their organization is more
likely to ensure that their mission is completed. Finally, terrorist organizations that employ
suicide attackers have an increased credibility that they will attack again, because the act of
suicide implies the attacker cannot be dissuaded. Suicide attacks also evoke the most fear in
society because they are deadlier than other forms of terrorism, and garner the most media
attention. However, suicide attacks automatically diminish the pool of potential individuals
in the organization who can carry out attacks. Pape believes that organizations utilize suicide
terrorism because it is effective in coercing target governments. At the same time, it is not
the only method of attack used because it can have the effect of alienating an audience who
might be sympathetic to their plight or losing more moderate supporters of their cause.
Additionally, Pape has examined who is most likely to be a target of suicide
terrorism and finds that democratic countries are the most likely targets. He proposes this
finding occurs because eliciting fear in society has the most impact in a democratic society.
In these countries individuals have the power to enact change by voting for officials who
4
might have different policy stances. Terrorist organizations hope that the fear and anxiety
caused by an attack, and the possibility of future violence, will cause citizens to persuade
their government to make concessions. However, he does not extend this study to include
other types of terrorism, even though Pape admits that suicide attacks only account for
three percent of all attacks. Further, Pape does not empirically examine his claim that
democracies are more likely to be targeted than any other type of government.
In the international relations literature some work has been done on the characteristics of countries where terrorism is most likely to develop. In Dynamics of Terrorism,
Hamilton and Hamilton build on social contagion models to determine how terrorism spreads
throughout a society. They find that terrorism is easier and safer in open societies because
of unrestricted travel and free press. They even find that the spread of terrorism is reversible
in countries with low democracy. The unit of observation in this paper is a terrorist organization, not a terrorist attack. Of course, it is possible that the number of active terrorist
organizations is not as easily discernible in countries with low civil liberties. Most of these
countries suppress the freedoms of association, travel, and the press. Therefore, countries
will only know about groups that either succcessfully carry out attacks and claim credit for
them, or are caught while attempting an attack. Thus, the results of this study must be
viewed with some skepticism. In addition, this study does not examine where terrorists are
most likely to strike. While it is important to know where terrorist organizations are located,
it is equally important to learn how far organizations are willing to travel in order to carry
out their attacks.
The political science literature has also examined terrorism. Birnir (2006)
studied when a domestic group will chose to exit the political process and resort to terrorist
violence against the targeted government. She finds that the longer a group is left out of
the political process, the more likely they are to turn to terrorism as a way of being heard.
The group does not have to be intentionally left out of the political process. It could be that
the group has not been elected to office in recent years. Again this paper differs from my
research because I am not looking at domestic groups. Finally, Burgoon (2006) examined
terrorists responses to a coutry’s social welfare policies. He finds that countries that have
more progressive social welfare policies are less likely to be targets of political violence. While
this paper is more in line with my research, I will examine several issues, including some
social welfare issues. Also, Burgoon’s study covers only terrorism in European countries,
whereas mine will be a cross country survey.
Finally, some economic modeling of the relationship between the level of ter-
5
ror attacks and some governmental policies have been examined. For instance, Frel and
Luechinger (2002) argue that deterrence is not an optimal strategy to employ in fighting
terrorism. With an increase in deterrence policy, governments play a more central role in
the decision making process. They argue that having a centralized decision maker in both a
political and economic realm can induce more terrorism because the benefit to the terrorist
or carrying out an attack is higher.
This paper will examine if the political party composition of different countries
makes them a more likely target for terrorist organizations. For instance, does having a more
conservative government, induce more or less terrorist attacks against a country. There are
two ways to answer this question intuitively. First, conservative governments usually spend
more on national security5 , and thus one might believe are better able to deter against
attacks. On the other hand, the stringent policies put in place by a conservative government
might have the opposite effect of inducing more attacks since a successful attack will now
bring about more utility to a terrorist organization. The goal of this paper is to empirically
examine whether the political affiliations of the leadership of a country has any effect on its
vulnerability as a target to terrorist organizations.
This research not only examines political party composition of the governing
body, but also studies which issues are most important to terrorists in choosing their targets.
I observe each political party’s stance on twelve different policy issues and then assign each
country a score on each issue. Therefore, this research will go beyond simply saying that
liberal or conservative governments are targeted most often. Instead, I will be able to deduce
that governments who have either left or right leanings on certain issues are more likely to
be targeted. A conclusion of this type can help form policy decisions of a government hoping
to reduce the amount of terrorism it faces.
3
Data
Data for this project consists of terrorist attacks, a time series of the political makeup of
targeted countries, and measures of how liberal or conservative the political parties are in
different countries. The terrorism data comes from International Terrorism: Attributes of
Terrorist Events (ITERATE), which is a project that collects ”characteristic data on groups,
5
The political parties in this dataset who have scores above average on support of the military were also
more likely to be ranked as a right leaning party
6
events, and environments in which attacks take place.”6 ITERATE7 contains data on every
transnational terrorist attack that took place from 1968 through 2004. The data on terrorist
attacks takes the form of both event and fatality counts. The definition of transnational
terrorism used by the ITERATE dataset is:
”The use, or threat of use, of anxiety-inducing, extra-normal violence for political
purposes by any individual or group, whether acting for or in opposition to
established governmental authority, when such action is intended to influence
the attitudes and behaviour of a target group wider than the immediate victims
and when, through the nationality or foreign ties of its perpetrators, its location,
the nature of its institutional or human victims, or the mechanics of its resolution,
its ramifications transcend national boundaries.8 ”
This definition will guide which events will and will not be coded in the terrorism dataset.
It is important to note that national boundary is loosely defined, causing some events that
appear to be domestic terror on initial examination to still be coded in the dataset. For
instance, while Kashmir, the Gaza Stip, and the West Bank are not listed as countries in
the dataset, any attacks that cross these borders are considered transnational terror9 .
There are benefits and drawbacks to using the ITERATE dataset. First, it is
the most user friendly and widely used open source dataset. The dataset allows different
ways of measuring terrorism, such as event or fatality counts, amount of damage done, and
counts of active terror organizations. Additionally, researchers can explore different options
in identifying the target of the attack, such as location or nationality of victims. For this
project, I am focusing on event and fatality counts to measure the amount of terrorism and
the location of the attack as a proxy for the target. One main drawback of ITERATE is
that acts of domestic terror are not included. However, only one open source dataset codes
domestic terror, and they have done so only since 1996. Another drawback of ITERATE is
that there is a possibility of bias since the incidents are coded from public FBI files and news
6
7
This description comes from the ITERATE Codebook
I recognize support from the Center for Risk and Economic Analysis of Terrorist Events (CREATE) at
the University of Southern California for providing me with this dataset. This research was supported by
the United States Department of Homeland Security through the Center for Risk and Economic Analysis
of Terrorism Events (CREATE) under grant number N00014-05-0630. However, any opinions, findings, and
conclusions or recommendations in this document are those of the author and do not necessarily reflect views
of the United States Department of Homeland Security.
8
This definition comes from the ITERATE Codebook.
9
Northern Ireland is listed as a separate country in the ITERATE dataset
7
articles. Therefore, the results may only be picking up the effect of ”newsworthy” terror
attacks. In the future, I hope to combine this dataset with other open source datasets in
order to enhance the robustness of the results.
The next portion of data needed for this project is cross-country political data
from the same time period as the terrorism sample. Therefore, the makeup of the political
leadership of targeted countries is gathered from 1968-2004. The number of members of
the national assembly belonging to each major political party is gathered for each country
during that time period. It is important that there is continuity in comparing the ideologies
of political parties in different countries. Just because parties have the same, or similar,
names in different countries does not mean they share the same beliefs on certain issues.
Therefore, I use Kenneth Jandas Political Parties: A Cross-National Survey, which ranks
political parties on twelve issues including government ownership of production, government
role in economic planning, social welfare, support of the military, (supra)national integration,
and protection of civil rights. These rankings give each political party an orientation score for
each issue. The data is coded so that a positive score implies that the party is pro government
action in that area, and is generally viewed as a leftist stance. Parties can receive a score
ranging from -5 to 5. Therefore, for each issue, parties are placed on a left/right continuum.
There are some potential problems with Janda’s dataset. First, not all countries are included in the study. However, the countries that are included in his research,
which can be found in Table 1 were drawn from a random sample stratified by geography.
The United States and United Kingdom were added to the dataset, bringing the number
of countries included to 49. Janda began performing his research in the early 1960’s, before the beginning of modern terrorism. Thus, we have no worries that the countries might
have been added because of the amount of terrorism they face. Another drawback is that
parties that do not exist before 1978 are not coded. Finally, Janda assigns each party an
issue orientation score only once for the time period we are examining. Therefore, I will not
compute a new score for each party in each time period. Instead, I will assume that the
issue orientation score of the party stays constant over the time period of the sample. Additionally, some of the policital parties in Janda’s dataset do not have scores for each policy.
The missing policy scores were imputed. Information on how values were imputed can be
found in Appendix A. Finally, for each country in Janda’s sample I have the proportion of
the governing body held by each active political party. When possible I have the data for all
years 1968-2004. Currently the research only examines the political makeup of the legislature/parliament/assembly as the governing body. In the future, I will add information about
8
head of state and determine how to appropriately balance the decision making capabilities
of the head of state and governing body in deciding policy. Hypotheses on the predicted
relationship between the number of terrorist incidents and the policy score on different issues
can be found in Table 4.
4
Results
Now that the political parties represented in the sample have an orientation score for each
issue, each country in the sample is assigned a score for each year. The score is assigned by
first calculating the sum of the stance of each party times the proportion of the governing
body that party holds in that year. This value is then divided by the total proportion of
the governing body accounted for by the sampled parties in that year. The country scores
represent weighted average of a country’s stance on the policy issue and are assigned as
follows:
X
scorei,j,t =
stancei,p × Shareof Governmentp,j,t
p
X
(1)
Shareof Governmentp,j,t
p
Where p represents the political party, i represents the issue, j represents the country,
and t represents the year. This calculation is done for each issue, meaning each country
gets 12 scores in each year. Due to the way issue orientation scores are assigned, the unit of
observation for this analysis will be a ”country-year.” In other words, the dependent variable
will be the number of events (fatalities) that occur in each country in each year. In the full
data set, there are just over 8,000 target-year combinations. At least one terrorist attack
occurs in over 1/3 of the observations. According to Table 2 there are an average of about
1.4 incidents per target year. However, once you look at only countries where at least one
terrorist incident occurred in that year, that average goes up to 5.48 incidents.
To provide some background information, over the 37 year period of the sample, there are only 12,000 coded terrorist events. These events have resulted in the deaths of
fewer than 20,000 individuals. Terrorists target some countries more than others. Therefore,
terrorism should be an important part of the research agenda in those countries. Table 3
shows that the United States has been a favorite target for terrorist group attacks. Even
9
though few individuals were wounded or killed on US soil due to transnational terrorism
before the events of 9/11, the US still faced the second highest number of attacks. The analysis presented in this paper covers two important aspects that the reader should remember.
First, all of the results presented include the events of September 11, 2001. I repeated the
analysis dropping 9/11 from the observations and the results did not change significantly.
Second, the target of the terrorist attack is being defined as the location where the attack
took place10 .
This paper is interested in answering whether the political orientation of the
leadership of the country affects the likelihood of that country being targeted by terrorist
attacks. The twelve issues we are considering political stances on are government ownership
of means of production, government role in economic planning, redistribution of wealth, social welfare, secularization of society, support of the military, anti-colonialism, supranational
integration, national integration, electoral participation, protection of civil rights, and interference with civil liberties. Remember, a country receiving a net positive score on any of
these issues implies that the country is in favor of government action in that area. For each
of those 12 issues, the following equation was estimated:
N umberof Incidentsj,t = α + βIssuei,j,t +
X
δj Countryj +
j
X
γt Y eart + i,j,t
(2)
t
where j represents the targeted country, t represents the year of the attack, and i represents the issue being analyzed. By including time dummy variables, the equation estimates
the within country variation in the Number of Incidents changes in the country’s score for
Issuei .The equations are also all run with Number Killed as the dependent variable11 .
These equations are run on the limited sample of 49 countries from Janda’s
book. Since a large number of the observations report no terrorist incidents, the underlying
distribution of the data is left censored at zero. Therefore, the parameters will be estimated
using a Tobit model. As can be seen in Table 5 three of the issues, government ownership
10
In the case where an attack crossed national boundaries, the end location of the attack is chosen as the
target.
11
Number wounded was considered for use as a dependent variable, however, some problems exist with
using it. First, more data was missing on number wounded. The technique used to calculate number killed
did not work in this scenario. Therefore, when number wounded was missing, I used the calculation that
the number wounded is usually 7 times larger than the number killed. Additionally, only wounded who
are treated are listed as wounded. Finally, the damage done to those wounded can vary drastically from
being permanently disabled to needing stitches, but having no lasting side effects. Because of these practical
problems, number wounded was not used as a dependent variable.
10
of production, redistribution of wealth, and supranational integration, are statistically significant at the five percent level in both of the specifications. Additionally, anti-colonialism
is significant at the 10 percent level. Adding any of these issues explains about one percent
more variation than a specification with only country and time fixed effects. The regressions
were also run allowing for standard errors that are clustered within the same country. Policy
scores adjust slowly over time because the political process does not change quickly throughout an entire country. Therefore, it is possible that the standard errors are correlated over
time in the same country.
Now, we will turn to examining the meaning behind the significant issues,
starting with those significant at the five percent level. The negative point estimate on government ownership of production implies that countries that favor government control face
fewer terror attacks. Therefore, being a capitalist country is correlated with experiencing
more terror attacks. Another significant issue was redistribution of wealth. This variable
codes how parties feel about equalization of income across society. Countries with governments that are in favor of redistribution are less likely to be targeted by terrorist attacks. A
study by Burgoon (2006) shows that having a more equalizing social welfare policy reduces
domestic and international terrorism in Europe. The results from this paper line up with
the findings of Burgoon. Supranational integration was coded by Janda as a political and
economic issue, not a military one. A positive point estimate means that countries whose
policies favor unions with other states are more frequently targeted by terrorists. This is
especially interesting with the growth of coalition forming in recent years. Since 1995 the
WTO has been formed and the number of countries joining the European Union has grown.
Anti-colonialism was another significant issue, although only at the 10 percent
level. This issue is harder to code, because countries can either be in a superior or subordinate
position when it comes to colonialism. For countries in a superior position, the negative point
estimate implies that countries who favor relinquishing control over colonies face fewer terror
attacks. Likewise, for countries in a subordinate position, the negative point estimate means
that countries who favor obtaining complete independence face fewer terror attacks. In
order to obtain more exact estimates for anti-colonialism, it would be necessary to identify
each country as either a superior or subordinate country in each year of the data set. In a
baseline specification, regressing Number of Incidents on only year and country dummies, the
Psuedo R2 was 0.1442. Therefore, for each of the significant policy issues, adding that issue
to the equation raises the baseline specification by between 1 and 2 percent. Additionally,
11
coefficients on 10 of the year dummies were statistically significant at the five percent level12 .
The significant years spanned every decade in the sample. Also of note, the point estimates,
while not all statistically significant, were postive for each year between 1970 and 1995. For
every since since 1995, the point estimate has been negative, implying that less terrorist
activities can be explained by those years than by the base year.
Since the US faces more terror attacks each year than any other country in
the limited sample set, the results were also run after dropping the US from the dataset.
By dropping the US, we can be sure that none of the results are driven solely by US policy.
The results can be found in Table 6. Every policy issue that was significant with the US,
is still significant once the US is dropped from the dataset. Interestingly, two more policies
are significant once the US is dropped from the dataset. Therefore, it appears including
the US in the dataset is actually dampening the affect of some of the policy scores. The
two variables that are now significant are government role in economic planning and social
welfare. The negative point estimate on government role in economic planning implies that
countries in favor of government control over allocation of resources face fewer terror attacks.
The negative point estimate on social welfare means countries in favor of universal coverage
and compulsory public assistance face fewer terror attacks, which is consistent with the
estimate on redistribution of wealth.
Additionally these equations were run with number killed as the dependent
variable, but none of the results were significant at the five percent level. This finding means
that policy has little effect on the choice of how deadly to make the terrorist attack. There
exist two reasons for this result. First, it is possible that there is too much noise in the
number killed variable. This variable might not be as precisely measured as the number of
events. First, a small percentage of the values for Number Killed were missing and had to
be imputed. Additionally, in order to calculate the true number of individuals killed in an
attack, researchers would have to follow news reports for at least several weeks to determine
whether or not the victims who are severely wounded recover. Combined, these problems
can lead to measurement error, and thus ambiguous results. Additionally, the number of
people killed in an attack will be a function of not only the location, but also the timing and
method of attack. It is possible that factors other than policies of the targeted governments
influence these decisions.
Other specifications of the empirical strategy were also considered, but did not
yield significant results. First, election years were examined to check for an additional impact
12
The base year for specification is 1968, the first year of the sample
12
on terrorist attacks. The reasoning behind this argument is that when a new government
comes to power, there exists some uncertainty about the policies the new government will
enact. If this uncertainty exists and terrorists react to policies, than one might expect
terrorist attacks to occur in the first year of a newly elected government. However, the
election years were never significant. There are two possible reasons for this finding. First,
it is possible that terrorist attacks continue occuring with the same frequency because the
terrorist organization wants to affect the policy decisions of the new government immediately.
Second, given the party that has the majority of the power, it is possible that there is little
uncertainty about which policies will be enacted. Finally, terrorist organizations might be
more interested in attacking governments that hold extreme views as opposed to moderate
ones. A score of zero represents a country that has a moderate stance. Taking the absolute
value of each score gives measure of how extreme the country is in that year. The farther
the value is from zero, the more extreme the government’s stance on that issue. However,
the variables significant under this specification are the same as those significant under the
specification from Equation 4.
4.1
Instrumental Variable Approach
Currently, the results are possibly all biased given that the the issues are potentially endogenous. This endogeneity would occur because it is possible that given terrorist attacks have
occurred in the past, countries elect certain officials who will take strong stances against terrorists in the future. The chosen IV must be correlated with a country’s score on that issue
but not correlated with the unexplained portion in the number of terrorist attacks that occur
in the country. The best choices probably include economic variables that may explain which
parties are in power but are not directly related to the the number of terrorist incidents. A
good example of a variable of this sort is the unemployment rate. Since the terror data is
transnational, unemployment should not be a direct explanatory variable for the number of
terror attacks. However, it is true that the unemployment rate can have a significant impact
on whether or not the incumbent government is elected in the future, and thus is strongly
related to how the political score changes over time. Additionally, according to Cell [1974]
voters often make their decisions on the basis of punishing a government whose policies they
did not like instead of voting for the politicians who will maximize their expected utility.
Keeping this model of voter behavior in mind, it is reasonable that high unemployment is a
characteristic that motivates voters to remove current governments.
Data on unemployment rates was gathered from the United Nations Yearly
13
Tables. These tables carried data on unemployment from 1969 through the present. Therefore, 1968 is dropped from the dataset when running the IV specification. However, the
unemployment rate by itself is not the best instrument to use for the policy score for several
reasons. First, the unemployment rate has the ability to change every time period, while the
country’s policy score only changes in an election year. Additionally, unemployment does not
predict which parties will be elected to power, but simply whether or not the party currently
in power will continue to be in power. When unemployment climbs, the incumbent government is often blamed, and then punished by voters during the next election. Therefore, a
potential instrument is an interaction between the policy score and the unemployment rate.
The instrument is constructed by interacting the policy score of the incumbent government
with the difference between the current unemployment rate and the average unemployment
rate since the last election. This instrument is calculated as follows:
Instrumenti,t = (
t−1
X
1
U nemploymentj −U nemploymentt )×P olicyScorei,LastElectionY ear
n j=LastElection
(3)
where i represents the policy issue and t represents the year. This equation is calculated
in each election year with non-election years receiving the same instrument as the previous
year. By examining the average of unemployment since the last time period, voters are
taking into consideration any deviations from trend that occured between election years. To
check the validity of the instrument, a first stage equations was estimated as follows:
P olicyScorei,j,t = δ0 +
X
δ1 Y eart +
t
X
δ2 Countryj + θInstrumenti,j,t
(4)
j
The results from this first stage can be found in Table 7. However, since the dependent
variable is censored, a Two Stage Least Squares approach will not yield consistent estimates.
Therefore, either a Maximum Likelihood Estimation (MLE) or a two stage Newey Minimum
Chi-squared technique needs to be used. For the results that follow, MLE was performed to
yield consistent and efficient estimates. The results using this specification for the instrument
can be found in Table 513 .
Two policies are significant under both specifications with the instrumental
13
A second specification of the instrumental variables is also calculated.
This instrument is chosen
by interacting the change in the unemployment rate since the last election with the policy score of the
incumbent government. This instrument is calculated as follows:
14
variable. These issues are government ownership of production and secularization of society. The results show that governments who favor ownership of production face fewer terror
attacks. In Hamilton and Hamilton (1983) they find that liberal democracies have more
terrorist groups in their countries. These findings seem to suggest that liberal democracies
also are the site of more terror attacks. One goal of terrorist organizations is to coerce target
governments. Additionally, terror attacks evoke fear in society, and can alter the behavior of
individuals. In liberal democracies, the attitudes of citizens should be closely mirrored by the
officials elected. In other words, citizens have more power to enact change in liberal democracies than in other political frameworks. Therefore, terrorist organizations can best coerce
target governments by attacking liberal democracies. Additionally, these findings imply that
capitalist countries face more terror attacks. Another goal of terrorist organizations is economic destabilization. In capitalist markets there are many decision makers, as opposed to
a single decision maker that emerges when the government controls commodity production.
Therefore, it is possible that economic destabilization is more feasible in a country where
many people are economic actors. This finding is counter to the theory suggested by Frey
and Luechinger (2002) that having a decentralized governments makes the marginal benefit
of terror attacks fall.
Additionally, secularization of society codes whether or not governments favor
monetary support of religious activities or even the establishment of a state religion. The
negative point estimate on this variable implies that governments who favor the separation
of church and state face fewer terror attacks. It could be that countries with more religious
diversity are faced with fewer terrorist attacks from religious terror organizations. This
variable also codes whether or not governments support or allow religious schools. Therefore,
this variable could be showing that with fewer religious schools, fewer terrorists will come
from that region, and thus, fewer attacks will happen in that country. There are several
avenues through which this variable could work, and I plan to investigate them using direct
measures of religious freedom in the robustness check section of the paper. Finally, two policy
issues are significant under only the second instrumental variable specification. First, social
welfare has a negative point estimate, implying that governments who favor universal welfare
Instrumenti,t = (U nemploymentlastelectionyear − U nemploymentt ) × P olicyScorei,lastelectionyear
(5)
By computing the difference in current unemployment and previous unemployment the voter is looking at
the total change in unemployment during the current administration. Results do not differ too much between
the two specifications. I will be adding a table with this specification to the Appendix.
15
face fewer terror attacks. This finding is in line with the finding of Burgoon (2006) even
when the analysis has been extended beyond Europe. Finally, the negative point estimate
on protection of civil rights means that governments with progressive anti-discrimination
policies face fewer terror attacks. Again, this could be true because these governments
produce fewer ”home-grown” terrorists, and that could cut back on the number of terror
attacks in a country. Approximately one-third of the attacks in the ITERATE database
take place in the home country of one of the attackers. Therefore, diminishing the number
of home grown terrorists can reduce the number of attacks in a country. The instrumental
variable specification was run again after dropping the US from the dataset. The results do
not differ from the results on the full dataset. They can be found in Table 6.
In order to examine the magnitude of the change in the number of incidents
with small changes in policy scores, I calculated the elasticity with respect to each of these
issues. Keep in mind that policy scores are coded on the range from -5 to 5 and thus, a 10
percent change in a party score will only move the party score at most 0.5 points from it’s
previous point. Elasticity scores were calculated for significant variables under both specifications. Changing the score for government ownership of production by 10 percent leads
to a 7 – 17.9 percent change in the number of terror attacks. For the level of secularization
in society, the number is a 9.2 percent change in the number of attacks with a 10 percent
change in the policy score. Therefore, a 10 percent change in these given policy variables
could yield a change in the number of terror attacks by 0.3 – 0.76 attacks in a year.
4.2
Regression Discontinuity Design
So far, the policy score of a country has been treated as variable that affects the number of
incidents in a uniform and continuous manner. However, it is plausible that the relationship
between the policy score and the number of incidents is not uniform. For instance, in the
US moving from 40 to 42 percent Democrat is much different than moving from 49 to 51
percent Democrat. Therefore, one should allow for the possibility that at some break point,
small changes in the actual policy score of a country can lead to large swings in the potential
policies enacted. Regression Discontinuity Design (RDD) will allow me to better capture
these complexities. This method fits the data well because the probability of facing additional
terrorist incidents changes discontinuously with the policy score of a country. The first order
of business is choosing the cut-off score for each policy issue. As a first pass at this problem,
I allowed the break point to be equal to the median score for that issue of all the political
parties in Janda’s book. Countries with a policy score above the median political party will
16
recieve a 1 while 0 will be coded for all other countries. Then, the following equation will
be estimated:
X
X
N umberof Incidentsj,t = α+ηAboveM ediani,j,t +βIssuei,j,t +
δj Countryj +
γt Y eart +i,j,t
j
t
(6)
where j represents the targeted country, t represents the year of the attack, and i represents
the issue being analyzed. Therefore η represents the change in intercept that occurs when
the average party in a country is more in favor of government action on Issuei than the
median party in the sample. Results from this estimation can be found in Table 6. In a
Tobit framework, this dummy variable is signification for two policy scores: social welfare
and protection of civil rights. The point estimate for the actual issue scores is negative for
both of these variables, meaning that governments who favor action on these policies face
fewer terror attacks. However, the point estimate for η is positive for these policies. For
instance, governments in favor of protecting civil rights experience fewer terror attacks. This
result is expected, as can be seen in Table 4. However, when a country’s policy score on
civil rights rises, we see a fall in terror incidents until the country score for civil rights climbs
above the median score. Then, we see a one time jump in the number of terror incidents.
This blip is abated if the policy score for protection of civil rights continues to rise, since the
point estimate on the slope parameter is negative.
Again, it is important to correct for endogeniety using an instrumental variable
approach and Maximum Likelihood Estimation. The same interaction between the unemployment rate since the last election and the policy score of a country during its last election
will be utilized as an instrument for Issue again. The results from this estimation can be
found in Table 6. While the point estimates on Issue are quite similar to those under a continuous instrumental variable approach, the dummy variable that allows for a discontinuous
effect of issue scores on number of incidents is statistically significant under three different
policy scores: government ownership of production, government role in economic planning,
and secularization of society.
As expected, an increase in the score for government ownership of production
leads to a reduction the number of terror attacks. However, when a country’s average
score outstrips the median party score, a one time jump in the number of terror attacks
occurs. In order for the overall downward trend to offset this effect, the policy score for
government ownership of production would have to be 1.45 points higher than the median
score. An increased score on government role in economic planning has a negative, but
17
imprecise, relationship with the number of terrorist incidents. Therefore, when the average
score for government role in economic planning climbs above the median score, there is a
persistent negative shock to the number of terror attacks. The negative relationship between
an increased government role in economic planning and the number of terrorist incidents
is expected since economic destabilization is more easily achieved. Finally, similar to the
IV approach countries more in favor of state sponsored religion face fewer terror attacks.
However, when a country crosses the median score threshold, they should experience a one
time increase in the number of terror attacks. In order for the overall downward trent to
offset this effect, the policy score for secularization of society must be 1.34 points higher
than the median score.
This section provided some evidence that the underlying relationship between
policy scores and the number of terrorist incidents is not continuous over the entire distribution of policy scores. Therefore, regression discontinuity design is an appropriate technique
to deal with these issues. However, while RDD did provide evidence of a discontinuous
relationship between the two variables, the overall results of the estimation did not change
drastically. Therefore, RDD simply provided a better understanding of how the variables are
related. In the future, different specifications of the breakpoint for RDD will be examined.
4.3
Robustness Checks
Since the results above were obtained on a limited sample set, it is prudent to test the
robustness on the full sample of observations. Therefore, I will use more direct measures
of the variables found to be significant to determine the mechanism through which they
affect the number of terrorist attacks. To directly measure the level of secularization in
society, I will use estimates of the number of religions represented by at least 10 percent of
the population, along with other measures of religious practice. I believe these more direct
measures offer a suitable check since the parameters can be estimated on the full model.
Additionally, the direct measures enable us to check the implementation of policy instead of
just the political leanings of the governing body.
In order to test for the level of secularization in society, I gathered information
on the different religions represented within the population of each country. Unfortunately,
this information is not available for each year of my sample. Currently, I have gathered data
from 2001. I plan to run the regressions in two ways. I will examine both the total number
of attacks in a country in relation to the religious composition in 2001 and I will also run
the tests using only the number of attacks in that country in 2001. In the meantime, I plan
18
to supplement the data with religious composition in additional years. The regression will
take the following form:
N umberof Incidentsj = α + βReligiousP racticej + j,t
(7)
where j represents the country. The level of religious practice will also be measured in
several different ways. First, the number of religions represented by more than 10 percent of
the population and the total number of religions represented in a country will be counted.
These variables will capture the level of religious diversity in a country. Additionally, both
the percentage of people who are coded as nonreligious and the percentage of citizens who
practice new religions will be utilized as independent variables. A measure of the percentage
of people who are not religous can relay how important religion is in the daily actions of the
government. The percentage of people practicing new religions could show how open the
country is to new religious ideas, and thus also captures a type of religious freedom. The
results from each of these regressions can be seen in Table 8.
Two of the variables are significant at the five percent level in this robustness
check. Those two variables were the total number of religions and the number of religions
represented by at least 10 percent of the population. The point estimate on the total number
of religions is positive, implying that countries with more religions represented within their
population have more terror attacks. However, the point estimate on the number of religions
represented by at least 10 percent of the population was negative. At a first glance, these
estimations appear to be at odds with each other. So an additional specification was run as
follows:
N umberof Incidentsj = α + βT otalReligionsj + ηOver10P ercentj + j,t
(8)
The point estimates are similar in this specification, as can be seen in Table 8. Therefore, it
is not just having a broad range of religions represented that can reduce terrorism. Instead,
it is having a population that actively practice different religions, where no one religion is
strictly dominant, that reduces terrorism. In other words, countries with some equality in
the number of followers across several religions face fewer terrorist attacks. This fits with
the results found earlier, that countries with no state religion face fewer terror attacks. It
would be interesting to examine if this result is driven by the number of attacks carried out
by religious terror organizations.
19
5
Future Work
The question of this paper, whether the policies put into place by a countries leaders effect
the number of terrorist attacks that country faces, can easily be asked in reverse. In other
words, it would be interesting to explore whether experiencing terrorist attacks can affect
the leaders who citizens will elect in the future. People have hypothesized that terrorists
do consider the timing of elections when planning their attacks. For instance, many people
believe that the Madrid bombings in 2004 affected the outcome of the election. Therefore, it
would be interesting to discover if the targets react to these attacks in the way that terrorists
expect.
Again, an instrumental variable approach would need to be taken in order
to reduce endogeneity because of reverse causation. In this case, the IV will have to be a
variable that is related to terrorist attacks in a target country but does not explain which
parties are elected in the targeted countries. Additionally, the enaction of policy and terror
attacks could be happening through simultaneous processes. Therefore, another planned
extension is to delve into the interplay between the enaction of policy and the timing of
terrorist attacks using a simultaneous equations model. Using this simultaneous equations
model, I will be able to better estimate how each of these variables effects the other on a
longer time horizon.
Finally, the fact that secularization of society is a significant issue can raise
future research questions. Mainstream media has introduced the argument that secular
schools have the potential of creating future terrorists14 . This line of argument is addressed
in Krueger and Maleckova (2003). The authors find that terrorists in the Middle East are
actually more educated than the rest of the population in their home countries. However,
they have no way of coding whether or not individuals go to radical religious schools. Therefore, there is no information on the type of education the terrorists are receiving. Using the
coding from Janda, I can determine in what years countries favor the establishment, and
even funding, of religious schools. Then, I can examine how support for religious schools in
a country correlates with both the number of terror organizations active in the country and
the number of active terrorists from that country. While this research would not perfectly
address the concerns raised in Kruger and Maleckova, it could help determine the role of
religious education in fostering future terrorists.
14
US News and World Report article title ”An Education on Muslim Integration”
20
6
Conclusion
The goal of this paper was to answer whether the political orientation of the leadership of
a country determines its likelihood of being targeted by terrorist attacks. The motivation
for this project was threefold: to build on previous literature discussing which countries are
targeted most often, to determine which policies are important to terrorist organizations,
and to understand the mechanism through which terrorists choose their targets. First, the
previous literature in the field found that terrorist organizations are more likely to target
democracies with suicide bombings and less likely to target governments with strong social
welfare policies. This paper built on that work examining how terrorist attacks were related
to 12 different policy issues. Additionally, this study encompassed a larger geographical
area than most previous studies. The countries involved in the analysis were chosen from
a random sample stratified by geography with countries from each continent represented.
Finally, in addition to a cross country sample, the observations also cover a time period from
1968–2004, with the end result being a rich panel dataset from which to draw conclusions.
The second goal of this paper was to determine which policies are important to
terrorist organizations in picking their targets. Since Janda’s sample covered political party
stances on 12 different issues, countries in this sample received orientation scores for each of
the twelve issues in each year of the sample. Since the regressions were run on each issue
separately, we were able to determine how each issue affects the likelihood of an event. Two
issues were determined to be significant in explaining the number of terrorist attacks. These
issues were: government ownership of production and secularization of society. Additionally,
small changes in policy scores on these issues lead to large changes in the number of incidents
a country faces. A 10 percent change in any of these policy scores could influence the number
of terror attacks in a country by anywhere between 0.3 and 0.76 attacks in a given year.
Finally, the third goal was to uncover information about the mechanism through
which terrorist organizations choose their targets. A robustness check on the full dataset
revealed that countries with multiple religions represented by at least 10 percent of the population are the target of fewer terrorist attacks. Additionally, further analysis of the data
revealed that the relationship between a country’s policy score and the number of terror
incidents that country faces is not continuous. Future robostness checks will be performed
to unveil the mechanism through which policy and terror attacks are related.
21
References
Alberto Abadie and Javier Gardeazabal. The economic costs of conflict: A case study of the
basque country. American Economic Review, 93(1):113–132, March 2003.
Daniel Arce and Todd Sandler. Counterterrorism: A game theoretic analysis. Journal of
Conflict Resolution, pages 183–200, April 2005.
Daniel Arce and Todd Sandler. An evolutionary game approach to fundamentalism and
conflict. Journal of Institutional and Theoretical Economics, March 2003.
JoAnna Birnir. The paradox of political terrorism in democracy and institutional alternatives. 2006.
Brian Burgoon. On welfare and terror. Journal of Conflict Resolution, 50(2):176–203, 2006.
Donald C. Cell. Policy influence without policy choice. Journal of Political Economy, 82(5):
1017–1026, 1974.
Konstantinos Drakos and Ali M. Kutan. Regional effects of terrorism on tourism in three
mediterranean countries. Journal of Conflict Resolution, 47(5):621–641, October 2003.
Bruno S. Frey and Simon Luechinger.
Terrorism:
Deterrence may backfire.
URL
http://search.ssrn.com/sol3/papers.cfm?abstracti d = 359823. 2002.
Lawrence C. Hamilton and James D. Hamilton. Dynamics of terrorism. International Studies
Quarterly, 27(1):299–305, March 1983.
Matthew Hanson and Martin Schmidt. The impact of coalition offensive operations on the
iraqi insurgency. 2007.
James J. Heckman. Sample selection bias as a specification error. Econometrica, 47(1):
153–162, January 1979.
Kenneth Janda. Political Parties: A Cross National Survery. The Free Press, New York,
1980.
Alan Krueger and Jitka Maleckova. Education, poverty, and terrorism: Is there a causal
connection? Journal of Economic Perspectives, 17(4):119–144, 2003.
Harvey E. Lapan and Todd Sandler. To bargain or not to bargain: That is the question.
American Economic Review, 78(2):16–21, May 1988.
22
Edward F. Mickolus, Todd Sandler, Jean M. Murdock, and Peter A. Flemming. International
Terrorism: Atrributes of Terrorist Events Data Codebook, July 2002.
Robert Pape. The strategic logic of suicide terrorism. American Political Science Review,
97:343–361, 2003.
Daniele Paserman and David A. Jeager. Israel, the palestinian factions and the cycle of
violence. American Economic Review, 96(2):45–49, May 2006.
Todd Sandler and Walter Enders. The Political Economy of Terrorism. Cambridge University
Press, Boston, 2005.
Todd Sandler and Kevin Siqueira.
Global terrorism: Deterrence versus preemption.
URL http://gemini.econ.umd.edu/cgi-bin/conference/download.cgi?dbn ame =
lasm2003paperi d = 64. 2003.
Alex Schmid and Albert Jongman. Political Terrorism: A New Guide to Actors, Authors,
Concepts, Data Bases, Theories, and Literature. Transaction Books, Amsterdam, 1988.
D. Thistlethwaite and D. Campbell. Regression-discontinuity analysis: An alternative to the
ex post facto experiment. Journal of Educational Psychology, 51:309–317, 1960.
Jeffre M. Wooldridge. Econometric Analysis of Cross Section and Panel Data. The MIT
Press, cambridge, Massachusetts, 2002.
23
A
Missing Data
Problems with missing data occured in both the terrorism and political data. Below I will
discuss how I dealt with these issues. First, there were about 200 (or less than two percent
of the sample) attack observations that had an unknown country as the location of attack.
These observations were dropped from the dataset. In the future I hope to be able to add
some of these observations back into my dataset by utilizing news stories from the day of the
attack. Once these observations are dropped, I have a full dataset of event counts in each
country in each year because each entry is now an observation of a terrorist event. However,
I was also interested in measuring how the political orientation can affect the intensity of
attacks, so a measure of fatalities as a dependent variable was also necessary. For about
100 observations the number killed in the attack was unknown. While this represents a very
small portion of my final sample, dropping these observations is not advisable in case the
reason they were missing data was not random. In some cases, dropping these observations
would mean dropping the only terrorist attack that occured in that location in that year.
For some of the observations, the number killed could be gleaned from news reports15 . For
the remaining observations with missing data, I examined what was known about each of
these attacks. For each of these datapoints, full information on the year, country, and type
of attack was available16 . Therefore, using full dataset to run a Tobit model where number
killed was a function of these three characteristics, I was able to impute the number killed
in each of the attacks with missing data.
Missing data was a larger problem with the political party data. Janda’s
sample covers almost 200 political parties and 12 different issues. About 1/3 of the parties
in his sample have a score for every issue. Additionally, 1/2 of the parties have a score
for at least 11 issues. For analysis, it will be favorable for each party to have a score for
each issue. However, it is not prudent to give a party missing a score on one issue the
average score of that issue. For instance, assume that the issue is government ownership of
production and the score is missing for a communist party. The average score for this issue
is approximately 0.50, implying that the country is moderate, with only a slight disposition
towards government ownership of production. This assumption would likely be incorrect.
Therefore a new system for imputing scores must be developed. Using the countries with
scores for every issue, we can determine how the issues are related to each other. In this
15
The events of 9/11/2001 had missing values for number killed. I pulled the total number killed used in
this study from the US State Department
16
Type of attack refers to whether it was a bombing, suicide attack, kidnapping, etc.
24
system, we run a regression where Issue i is a function of the other 11 issues such that
Issuei = f (Issue−i ). The equations had R2 ranging from 0.15 to 0.77 and usually at least
four of the other issues were statistically significant at the five percent level. These equations
were then used to impute the missing scores.
However, since approximately half of the parties were missing scores for at
least two issues, these equations will need to be tweaked. For instance, assume that two
issues (A and B) are missing the issue orientation score. For a party missing these two
scores, in order to compute the value of the missing score for issue A I used the average of
score B as a proxy for score B in the equation. Thus, the equation now looks like:
n
1X
IssueA = α + β
IssueB + f (Issuec−l )
n i=1
(9)
In the future, I am interested in calculating these missing values more precisely17 . In order
to do so, I plan on repeating the process described above to solve for score A and B, but
using the values of scores A and B calculated from the above equations in the previous
period instead of using an average. This process would be repeated until the missing scores
converged to their ”true” value. For the scores to converge to a true value, the parameters
in the equation must be on the interval (-1,1), which they all are. While missing data is an
issue in this project, imputing the scores is a better solution than dropping the observations
completely, because it is possible that there is a systematic reason for why some of the data
is missing.
B
Check for Underreporting
Even though the results show that countries with lower civil liberties experience fewer terrorist attacks, there are reasons to doubt the validity of that assessment. Since the terrorism
data comes from news reports, it is possible that countries with low civil liberties are better
able to keep terrorist attacks out of the media. In The Political Economy of Terrorism, Sandler and Enders argue that utilizing event counts is the best way to overcome the bias from
17
Likewise, in order to compute the value of the missing score for Issue B, I used the average of score A
as a proxy for score A in the equation in the following manner:
n
IssueB = αb + βb
1X
IssueA + g(Issuec−l )
n i=1
25
(10)
underreporting in countries with low civil liberties. Their argument is that event counts
work better than studies that look at active terrorist groups because it is harder for the
media to hide an attack than it is for the government to hide a group. However, I was still
interested in determining if underreporting was a large issue in this dataset. In order to test
for underreporting, I extend the argument made by Sandler and Enders. Countries with low
civil liberties will not be able to hide an event that was actually carried out as planned as
easily as they would be able to hide an attack that was thwarted, either in the planning or
execution stage. The ITERATE dataset has a variable that codes each attack as a tactical
success (6) or failure (0) from the point of view of the terrorist organization. According
to my hypothesis, if there is underreporting of terrorist attacks in countries with low civil
liberties, than the relationship between logistic success and civil liberties should be positive
and significant.
Since ITERATE codes tactical success on a 0–6 scale, it is practical to use an
ordered probit to estimate the relationship between success and civil liberties. However, to
use an ordered probit, the underlying distribution of the tactical success variables must be
uniformly distributed. The vast majority of the events were tactical successes, thus skewing
the underlying distribution. However, if we look only at events that were not complete
tactical successes (on the 0–5 range) then the distribution more closely fits. Since it is difficult
not to report an attack that takes place, it is reasonable to drop those observations when
determining if underreporting bias exists. In other words, underreporting probably does not
exist on events that actually took place. The results show that the civil liberties parameter
is positive and significant at the 10 percent level. Hence, our positive coefficient implies that
countries with more censorship are likely to have underreporting of terrorist attacks that
were not carried out successfully; however, the precision attached to this estimate is low.
Therefore, to test the robustness of these results, I ran two more specifications
of this equation. First, instead of running an ordered probit, I ran a simple logit where 1
represents a completely successful attack and 0 represents any attack that is not carried out
as the terrorists’ planned. Second, I ran an ordered logit after combining some of ITERATE’s
specifications. I assigned a 0 to any attack that was stopped in the planning stages according
to their system; a 1 to any attack that was carried out with errors or thwarted in the execution
stage; and a 2 to any attack that was carried out successfully. These two specifications both
yielded no statistically significant relationship between civil liberties and tactical success,
implying that there is not underreporting bias in the dataset. From analyzing the results of
these three specifications, we can assume that while underreporting might be a problem, it
26
is likely not a large problem.
It is possible to find rankings of countries civil liberties. Freedom house is
an agency that measures the level of civil liberties in a country on a 1–7 scale each year.
Unfortunately, these measures do not cover the same time period as the terrorism sample.
If these freedom scores could be ascertained for the time period of the sample, then the
regressions can be run separately for each category of civil liberties. Then, any changes in the
parameters of interest between regressions can be tied to a possible difference in reporting
in these countries. Additionally, a nested regression can yeild similar results. These are
options to investigate in the future to further discover how prevelant underreporting is in
this dataset.
27
Albania
Table 1: Countries in Janda
Burkina Faso, Upper Volta
Australia
Burma
Austria
Congo-Brazzaville
Bulgaria
East Germany
Cambodia
Ghana
Canada
Indonesia
Central African Republic Iran
Chad
Lebanon
Cuba
Malaysia
Denmark
Portugal
Dominican Republic
Sudan
Ecuador
Togo
El Salvador
France
Greece
Guatemala
Guinea
Hungary
Iceland
India
Ireland
Kenya
Luxembourg
Netherlands
New Zealand
Nicaragua
Paraguay
Peru
Sweden
Tunisia
Turkey
Uganda
United Kingdom
United States
Uruguay
USSR (Russia)
Venezuela
28
Table 2: Descriptive Statistics
Mean Mean |Incident
Number of Incidents
1.379
5.48
Total Killed
1.813
7.203
Total Wounded
3.674
14.596
Country
Incidents
Table 3: Top 10 Countries
Country
Killed Country
Lebanon
847
United States
3100
United States
4565
United States
701
Lebanon
1310
Israel
3726
(West) Germany
666
Iraq
938
Iraq
2457
United Kingdom
665
Israel
836
United Kingdom
2317
France
639
USSR (Russia)
678
Lebanon
1895
Columbia
467
Angola
477
USSR (Russia)
1285
Greece
413
Columbia
473
France
1270
Argentina
390
Pakistan
376
Saudi Arabia
1191
Italy
383
Kenya
278
Pakistan
1137
Israel
348
Cambodia
276
(West) Germany
1015
29
Wounded
Table 4: Hypotheses - Incidents will Increase with Changes in Policy of Predicted Sign
Policy
Positive Estimate
Negative Estimate
Govt Prod
With many economic actors, economic
destabalization is more easily achieved.
Govt
Econ
With many economic actors, economic
Plan
destabalization is more easily achieved.
Redistribute
High income inequality could lead to a dis-
Wealth
affected portion of the population turning
to terrorism.
Social Welfare
Secular Soci-
Religious terror organizations from a mi-
Those in the majority religion could rebel
ety
nority party could rebel against state con-
against allowing for a separation of church
trol of the majority religion
and state.
Electoral Par-
Attacks against democracies are more suc-
Former political groups might turn to ter-
ticipation
cessful because voters are afraid of fu-
ror as a way for their voices to be heard.
ture attacks and have the ability to enact
changes in policies.
Support Mili-
Countries with strong militaries have
Countries with strong militaries are better
tary
more bases abroad, which might upset ter-
able to deter against attacks, and would
ror organizations.
be better prepared to retaliate.
Anticolonialism Subordinate countries employ terrorism Rogue elements of subordinate countries
to gain independence.
could turn to terrorism to undermine the
acceptance of the colonial power.
Suprantional
Joining these unions can be seen as either
Integration
”westernizing” or trying to spread one’s
influence, which could in turn upset terror
organizations.
National Inte-
Minority groups could be left out fo the
gration
political process, thus inducing them to
resort to terror.
Protect Civil
Minority groups who feel discriminated
Rights
against could resort to terror.
Interfere Lib-
People want to rebel against governments
Terror attacks are harder to carry about
erties
that infringe upon their civil liberties.
because there is less free exchange of information.
30
Govt Prod
Table 5: Results - Number of Incidents
Tobit Estimates
IV Estimates
2
Estimate Constant Adj R
Estimate Constant
-1.553∗∗ 13.660
0.1458
-15.509∗∗ -19.370
(0.546)
Govt Econ Plan
-0.650
(5.007)
16.905
0.1444
16.425
0.1470
17.677
0.1447
(0.716)
Redistribute Wealth
-2.079∗∗
-1.053
-0.319
0.368
16.138
0.1443
-0.252
14.850
0.1443
-1.585∗
17.644
0.1443
15.896
0.1450
19.419
0.1456
-0.818
-0.628
16.419
0.1444
0.487
-10.099
10.263
-46.465
-10.369
-0.083
-0.589
3.951
-36.810
10.810
(35.801)
18.629
0.1446
17.588
0.1405
(0.437)
Interfere Liberties
3.135
(3.951)
(0.774)
Protect Civil Rights
-25.489
(12.190)
(0.512)
National Integration
-30.457∗∗
(8.564)
(0.825)
Suprantional Integration 1.325∗∗
9.949
(9.839)
(0.458)
Anticolonialism
-3.299
(6.806)
(0.408)
Support Military
4.618
(4.956)
(0.561)
Electoral Participation
-0.854
(3.185)
(0.708)
Secular Society
12.425
(4.678)
(0.560)
Social Welfare
-7.316
-7.215
29.515
(4.965)
(0.463)
-11.616
(10.504)
31
-23.095
Govt Prod
Table 6: Results - Number of Incidents US Dropped
Tobit Estimates
IV Estimates
Estimate Constant Adj R2
Estimate
Constant
-1.471∗∗ 15.242
0.1474
-6.070∗∗
2.079
(0.512)
Govt Econ Plan
-1.210∗
(1.872)
16.083
0.1463
17.027
0.1489
(0.659)
Redistribute Wealth
-2.002∗∗
-1.510∗∗
-0.505
21.112
0.1447
0.368
14.332
0.1456
12.019
0.1458
-0.338
15.411
0.1457
-1.597∗∗
17.795
0.1466
0.363
15.979
0.1467
-.354
13.824
0.1456
15.578
0.1458
0.318
-28.164
Not Concave —
-1.844
5.567
-.684
3.414
15.527
0.1416
-5.569
10.545
(3.573)
(0.399)
Interfere Liberties
6.496
(3.539)
(0.709)
Protect Civil Rights
-26.007
(1.883)
(0.469)
National Integration
-30.157∗∗
(—)
(0.751)
Suprantional Integration 1.057∗∗
7.230
(16.999)
(0.417)
Anticolonialism
-0.916
(8.213)
(0.408)
Support Military
4.684
(1.159)
(0.522)
Electoral Participation
-0.777
(2.304)
(0.645)
Secular Society
13.364
(4.076)
(0.509)
Social Welfare
-6.563
-7.615
8.870
(4.965)
(0.422)
-5.888
(5.663)
32
-12.321
Govt Prod
Table 7: Results - First Stage Estimation
Instrument Constant Country Year
0.0179∗ ∗ ∗ -1.624
Yes
Yes
Adj R2
0.9563
(0.0034)
Govt Econ Plan
0.0634∗ ∗ ∗
0.805
Yes
Yes
0.9563
-0.104
Yes
Yes
0.9090
1.176
Yes
Yes
0.9298
-1.131
Yes
Yes
0.9635
4.022
Yes
Yes
0.9146
5.010
Yes
Yes
0.9605
-0.329
Yes
Yes
0.9470
-2.251
Yes
Yes
0.8762
-0.216
Yes
Yes
0.9551
3.303
Yes
Yes
0.9721
-1.872
Yes
Yes
0.9559
(0.0030)
Redistribute Wealth
0.0308∗ ∗ ∗
(0.0041)
Social Welfare
0.0122∗ ∗ ∗
(0.0026)
Secular Society
0.0151∗ ∗ ∗
(0.0027)
Electoral Participation
-0.0045∗∗
(0.0022)
Support Military
-0.0091∗∗
(0.0032)
Anticolonialism
0.0066∗∗
(0.0027)
Suprantional Integration 0.0184
(0.0041)
National Integration
0.0028
(0.0020)
Protect Civil Rights
0.0098∗ ∗ ∗
(0.0022)
Interfere Liberties
0.0067
(0.0023)
33
Govt Prod
Govt Econ Plan
Constant Above Median Issue Estimate Country-Year Adj R2
-0.674
-0.656
-1.464∗∗
Yes
0.1458
-2.982
Redistribute Wealth
-62.559
Social Welfare
1.387
Secular Society
-6.221
Electoral Participation
Support Military
Anticolonialism
-5.077
-6.094
-0.935
Suprantional Integration -22.710
National Integration
Protect Civil Rights
Interfere Liberties
-9.027
-7.311
-64.210
(2.467)
(0.655)
1.286
-0.820
(3.262)
(0.836)
0.730
-2.178
(—)
(—)
5.444∗∗
-1.712∗∗
(2.751)
(0.776)
-1.766
-0.083
(2.712)
(0.670)
-1.346
0.698
(2.471)
(0.834)
-3.199
0.311
(2.211)
(0.601)
-0.094
-1.574∗
(2.631)
(0.871)
-1.697
1.749∗∗
(2.348)
(0.779)
1.237
0.404
(2.499)
(1.138)
5.629∗∗
-1.261∗∗
(2.720)
(0.532)
1.879
0.201
(—)
(—)
34
Yes
0.1444
Yes
0.1470
Yes
0.1455
Yes
0.1444
Yes
0.1444
Yes
0.1447
Yes
0.1450
Yes
0.1457
Yes
0.1445
Yes
0.1455
Yes
0.1406
Constant Above Median Issue Estimate Country-Year
-3.147
32.991∗∗
-22.195∗∗
Yes
Govt Prod
Govt Econ Plan
12.513
Redistribute Wealth
4.437
Social Welfare
6.505
Secular Society
Electoral Participation
-44.868
-2.307
Support Military
-5.488
Anticolonialism
-5.632
Suprantional Integration 2.824
National Integration
6.886
Protect Civil Rights
21.652
Interfere Liberties
195.643
(11.750)
(7.594)
-17.041∗∗
-7.511
(7.165)
(4.614)
0.879
-1.026
(7.709)
(4.673)
13.161
7.984
(11.490)
(7.984)
66.883∗∗
-49.161∗∗
(19.210)
(14.232)
-4.389
2.454
(15.780)
(7.299)
-11.922
4.486
(7.915)
(0.4.326)
35.184
-20.628
(41.112)
(24.631)
2.977
-1.042
(8.456)
(5.189)
16.808
-18.760
(12.331)
(14.201)
8.746
-7.477
(7.811)
(5.151)
-185.636
82.843
(364.840)
(162.232)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Table 8: Robustness Check - Secularization of Society
Num over 10 Percent Total Religions Nonreligious New Religions Constant
Estimate -0.811∗∗
3.284
0.0209
(0.383)
Estimate
0.365∗∗
0.450
0.0408
(0.139)
Estimate
0.013
1.962
0.009
(0.034)
Estimate
-0.047
2.060
0.0016
(0.092)
Estimate -0.938∗∗
(0.377)
R2
0.404∗∗
1.732
0.0763
(0.138)
35