Socio-Economic%20Antecedents%20of%20Transnational%20Terrorism:%20Causal%20Graphs

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Socio-Economic Antecedents of Transnational
Terrorism Causal Graphs
AGEC689 Terrorism and Homeland Security
David A. Bessler
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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.

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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).

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Research Question

• Do social, economic, and political factors
  influence the environment conducive to terrorist
  activities?
• If so, how?

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Counter-Terrorism Strategies

• Strategies and terrorism formats
• 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.

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Does Poverty Breed Terrorism?

• Some say Yes
• 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
• 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.

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Do Other Economic Conditions Matter?

• Political freedom?
• 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?
• Stern (2000) attributes involvement in terrorist
  acts to lack of adequate education.
• Others like trade, income equality?
• 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.

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What is Our Specific Mission

• Separate the impacts of socio-economic and
  political factors on
• Likelihood of terrorism participation
• Number of terrorism participation at country
  level
• Investigate the relationships of terrorism
  participation and
• Income
• Education
• Poverty
• Freedom
• Openness to trade
• Concentration of Wealth

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Data Sources

• The chronological data on transnational terrorism
  events was obtained from Dr. Edward Mickolus
  (Vinyard Software Inc.), including
• incident date, incidents country of origin,
  location of incident, up to three nationalities
  of victims, and up to three nationalities of
  perpetrators.
• Socio-economic and political variables
• World Bank data base
• CIA world factbook
• Various National Statistics Services
• Heritage Foundation

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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

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Independent Variables
Name Name Definition Mean SD
Income/Wealth measures Income/Wealth measures Income/Wealth measures
GDP GDP per capita (1,000) 6,737 8,880
GINI GINI index (0perfectly inequity 1perfectly 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
Education Trade and Economic freedom measures Education Trade and Economic freedom measures Education Trade and Economic freedom measures
Literacy population ratio who can read and write 0.82 0.20
Trade (ImportsExports)/GDP 0.67 0.52
Freedom economic freedom (1highest 5lowest) 3.01 0.64

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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.

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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) an
d thus the ceteris paribus condition may
not hold. We may not ever know if it holds
because of unknown variables operating on our
system.
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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.

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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) an
d thus the ceteris paribus condition may
not hold. We may not ever know if it holds
because of unknown variables operating on our
system.
16
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) an
d thus the ceteris paribus condition may
not hold. We may not ever know if it holds
because of unknown variables operating on our
system.
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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.

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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).

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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)
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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.

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A Causal Fork

• For three variables X, Y, and Z, we illustrate
• X causes Y and Z as

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.
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An Example of a Causal Fork

• X is the event, the student doesnt 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) gt P
(Z)
If we add the information on whether he/she
understands the material, the contribution of
grade disappears (we do not know candidates name
when we mark his prelim) P (Z Y, X) P (Z X)
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An Inverted Fork

• Illustrate X and Z cause Y as

• 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.
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The Causal Inverted Fork An Example

• Let Y be the event that my daughters cell-phone
  wont
• 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(XZ) 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(XY,Z) lt P (XY).
• (although I dont know her bill status for sure).
• X ? Y ? Z

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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

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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.
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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

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Causal Sufficiency

• No two included variables are caused by a
• common omitted variable.

No hidden variables that cause two included
variables.
Z
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Causal Markov Condition

• The data on our variables are
• generated by a Markov property,
• which says we need only condition
• on parents

P(W, X, Y, Z) P(W) P(XW) P(Y) P(ZX,Y)
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Faithfulness

• There are no cancellations of parameters.

A b1 B b3 C C b2 B
It is not the case that -b2 b3 b1
Deep parameters b1, b2 and b3 do not form
combinations that cancel each other.
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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.
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Cautions on Interpretations of the Results

• The dataset was constructed using a limited
  amount of best available information and involved
  extrapolation of socio-economic 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.