Socio-Economic Antecedents of Transnational
Terrorism: Causal Graphs
AGEC689: Terrorism and Homeland
Security
David A. Bessler
Bio-Security and Terrorism
All of the events studied, thus far in this
course have been introduced (we think) by
chance. FMD, BSE, and next lecture AI are
not intentionally introduced into our food
chain.
However, it may be the case that such
maladies could be intentionally introduced
to “cause” harm to the local population.
What is Transnational Terrorism
Transnational terrorism is a “premeditated
threatened or actual use of force or violence
to attain a political goal through fear,
coercion, or intimidation” and when its
ramifications transcend national boundaries
through the nationality of the perpetrators
and/or human or institutional victims,
location of the incident, or mechanics of its
resolution (Mickolus et al. 1989).
Research Question
Do social, economic, and political factors
influence the environment conducive to
terrorist activities?
If so, how?
Counter-Terrorism Strategies
Strategies and terrorism formats:

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Strategies focusing on terrorism of certain format
(mental detection)
Strategies less sensitive to terrorism format
(international sanctions, financial aid, education
assistance)
Protective vs. responsive strategies

If protective strategies is more desirable to thwart
terrorism, then we need to think about factors
encouraging/discouraging terrorism participation.
Does Poverty Breed Terrorism?
Some say Yes
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Alesina et al. (1996) suggest that poor economic conditions increase the probability of
political coups.
Hess and Orphandies (2001) show that the frequency of war is greater following
recessions than economic growth.
Blomberg and Hess (2002) suggest that economic recessions increase the prob. of
internal/external conflicts
Blomberg, Hess and Weerapana (2004) find that economic recessions increases the
probability of terrorist activities in democratic high-income countries.
Li and Schaub (2004) find that economic development decreases the number of
international terrorist incidents.
Fearon and Laitin (2003) conclude that poverty has a significant positive effect on violent
domestic conflicts.
Some say No
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Piazza (2006) finds no significant relationship between terrorism and income equity, per
capita GDP growth, unemployment, and so forth
Krueger and Maleckova (2003) finds no statistical relationship between involvement in
terrorism events and economic activity.
Abadie (2006) finds no significant relationship between risk of terrorism and economic
variables.
Krueger and Laitin (2003) suggest that poor countries do not generate more terrorism than
rich countries with similar levels of civil liberties.
Do Other Economic Conditions Matter?
Political freedom?
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Abadie (2006) finds a countries with intermediate levels of political
freedom are more prone to terrorism than countries with high or low levels
of political freedom
Li (2005) shows that democratic participation reduces transnational
terrorism.
Education?
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Stern (2000) attributes involvement in terrorist acts to lack of adequate
education.
Others like trade, income equality?
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Li and Schaub (2004) find that trade, foreign direct investment, and
portfolio of investment have no direct positive effect on the number of
terrorist events.
Muller and Seligson (1990) and London and Robinson (1989) show that
income inequality is a significant predictor of political violence.
What is Our Specific Mission
Separate the impacts of socio-economic and political factors on
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Likelihood of terrorism participation
Number of terrorism participation at country level
Investigate the relationships of terrorism participation and
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Income
Education
Poverty
Freedom
Openness to trade
Concentration of Wealth
Data Sources
The chronological data on transnational terrorism
events was obtained from Dr. Edward Mickolus
(Vinyard Software Inc.), including

incident date, incident’s country of origin, location of
incident, up to three nationalities of victims, and up to
three nationalities of perpetrators.
Socio-economic and political variables
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World Bank data base
CIA world factbook
Various National Statistics Services
Heritage Foundation
Dependent Variable
The dependent variable is annual counts of terrorism events
by country and year in which citizens of a particular country
were documented as perpetrators.

The annual count of terrorism events for Philippines in 2000 is seven,
which means that the Philippine nationals were documented as
perpetrators for seven transnational terrorism incidents in 2000.
The original chronological data documents up to three
nationalities of perpetrators for each terrorism incident.

On March 15, 1982 three Salvadorans, two Nicaraguans, one Chilean
and others were arrested for intending to kidnap an unidentified
American diplomat. This incident increases the annual count of
terrorism events by one for Salvador, Nicaragua, and Chile each.
Documented with known nationalities vs. unknown
nationalities

Unknown: either the perpetrators or their nationalities were not
traceable.
Counts vs. Incidents
Independent Variables
Name
Definition
Mean
SD
Income/Wealth measures
GDP
GDP per capita ($1,000)
6,737
8,880
GINI
GINI index (0=perfectly inequity;
1=perfectly equity)
0.40
0.11
PV1
population ratio living under $1/day
0.11
0.15
PV12
population ratio living on $1 to $2/day
0.25
0.15
0.82
0.20
0.67
0.52
3.01
0.64
Education Trade and Economic freedom measures
Literacy
population ratio who can read and write
Trade
(Imports+Exports)/GDP
Freedom
economic freedom (1=highest; 5=lowest)
Inference on Causal Flows
 Oftentimes we are uncertain about which
variables are causal in a modeling effort.
 Theory may tell us what our fundamental
causal variables are in a controlled system.
 It is common that our data may not be
collected in a controlled environment.
Use of Subject Matter Theory
Theory may be a good source of information about
direction of causal flow among variables. However,
theory usually invokes the ceteris paribus condition
to achieve results.
Data are often observational (non-experimental)
and thus the ceteris paribus condition may not
hold. We may not ever know if it holds because
of unknown variables operating on our system.
Inference on Causal Flows
 Oftentimes we are uncertain about which
variables are causal in a modeling effort.
 Theory may tell us what our fundamental
causal variables are in a controlled system.
 It is common that our data may not be
collected in a controlled environment.
Use of Subject Matter Theory
Theory may be a good source of information about
direction of causal flow among variables. However,
theory usually invokes the ceteris paribus condition
to achieve results.
Data are often observational (non-experimental)
and thus the ceteris paribus condition may not
hold. We may not ever know if it holds because
of unknown variables operating on our system.
Use of Subject Matter Theory
Theory may be a good source of information about
direction of causal flow among variables. However,
theory usually invokes the ceteris paribus condition
to achieve results.
Data are often observational (non-experimental)
and thus the ceteris paribus condition may not
hold. We may not ever know if it holds because
of unknown variables operating on our system.
Experimental Methods
If we do not know the "true" system, but have an
idea that one or more variables operate on that
system, then experimental methods can yield
appropriate results.
 Experimental methods work because they use
randomization, random assignment of subjects to
alternative treatments, to account for any additional
variation associated with the unknown variables on
the system.
Observational Data
In the case where no experimental control is used in the
generation of our data, such data are said to be observational
(non-experimental).
Causal Models Are Well-Represented
By Directed Graphs
One reason for studying causal models,
represented here as X  Y, is to predict the
consequences of changing the effect
variable (Y) by changing the cause variable
(X). The possibility of manipulating Y by
way of manipulating X is at the heart of
causation.
“Causation seems connected to intervention and manipulation:
one can use causes to ‘wiggle’ their effects.”
-- Hausman (1998, page 7)
Directed Acyclic Graphs

Pictures summarizing the causal flow
among variables -- there are no cycles.
 Inference on causation is informed by
asymmetries among causal chains,
causal forks, and causal inverted forks.
A Causal Fork
For three variables X, Y, and Z, we illustrate
X causes Y and Z as:
Y
X
Z
Here the unconditional association between
Y and Z is non-zero, but the conditional
association between Y and Z, given
knowledge of the common cause X, is zero.
Knowledge of a common cause screens off
association between its joint effects.
An Example of a Causal Fork
 X is the event, the student doesn’t learn the material
in Econ 629.
 Y is the event, the student receives a grade of “D” in
Econ 629.
 Z is the event, the student fails the PhD prelim in
Economic Theory.
Grades are helpful in forecasting whether a student
passes his/her prelims: P (Z | Y) > P (Z)
If we add the information on whether he/she
understands the material, the contribution of grade
disappears (we do not know candidate’s name
when we mark his prelim): P (Z | Y, X) = P (Z | X)
An Inverted Fork
 Illustrate X and Z cause Y as:
X
Y
Z
 Here the unconditional association between X
and Z is zero, but the conditional association
between X and Z, given the common effect Y is
non-zero:
Knowledge of a common effect does not
screen off the association between its
joint causes.
The Causal Inverted Fork: An
Example
 Let Y be the event that my daughter’s cell-phone won’t
work
 Let X be the event that she did not pay her phone bill
 Let Z be the event that her battery is dead
Paying the phone bill and the battery being dead are
independent: P(X|Z) = P(X).
Given I know her battery is dead (she remembers that she
did not charge it for a week) gives some information
about bill status: P(X|Y,Z) < P (X|Y).
(although I don’t know her bill status for sure).
XYZ
The Literature on Such Causal Structures Has
Been Advanced in the Last Decade Under the
Label of Artificial Intelligence
 Pearl , Biometrika, 1995
 Pearl, Causality, Cambridge Press, 2000
 Spirtes, Glymour and Scheines, Causation,
Prediction and Search, MIT Press, 2000
 Glymour and Cooper, editors, Computation,
Causation and Discovery, MIT Press, 1999
Causal Inference Engine
- PC Algorithm
1. Form a complete undirected graph
connecting every variable with all other
variables.
2. Remove edges through tests of zero
correlation and partial correlation.
3. Direct edges which remain after all possible
tests of conditional correlation.
4. Use screening-off characteristics
to accomplish edge direction.
Assumptions
(for PC algorithm on observational data to give
same causal model as a random assignment
experiment)
1. Causal Sufficiency
2. Causal Markov Condition
3. Faithfulness
4. Normality
Causal Sufficiency
No two included variables are caused by a
common omitted variable.
No hidden variables that cause two included
variables.
Z
Z
X
Y
Causal Markov Condition
The data on our variables are
generated by a Markov property,
which says we need only condition
on parents:
W
X
Z
Y
P(W, X, Y, Z) = P(W) • P(X|W) • P(Y) • P(Z|X,Y)
Faithfulness
There are no cancellations of parameters.
A
A = b 1 B + b3 C
C = b2 B
b1
b3
B
b2
C
It is not the case that: -b2 b3 = b1
Deep parameters b1, b2 and b3 do not form
combinations that cancel each other.
Figure 1. Graphical Representation on Eight Variables Related to Terrorism Events in Countries and Economic and Political
Characteristics.
Note; the numerical values embedded in the lower right hand corner of each variable rectangle are the mean values of that variable over the sample. The numerical
values embedded in each arrow represent the estimated coefficient from a regression of the effect variable on the cause variable, taking into account the backdoor
paths in that regression.
Cautions on Interpretations of the Results
The dataset was constructed using a limited amount of best
available information and involved extrapolation of socioeconomic estimates over the periods for which data was not
available. Caution is warranted for interpretation of the
results.
The findings should be interpreted as no more than a
preliminary support of the idea that socioeconomic factors
may play a role in encouraging/discouraging terrorist
behavior.
Further studies based on either more complete records or on
alternative approaches, which would avoid reliance on
observational data, are necessary to fully understand the
linkage between socioeconomic factors and participation in
transnational terrorism acts.