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Socio-Economic Antecedents of Transnational Terrorism: Causal GraphsPowerPoint Presentation

Socio-Economic Antecedents of Transnational Terrorism: Causal Graphs

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### Socio-Economic Antecedents of Transnational Terrorism: Causal Graphs

AGEC689: Terrorism and Homeland Security

David A. Bessler

Bio-Security and Terrorism Causal Graphs

- 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 Causal Graphs

- 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 Causal Graphs

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

Counter-Terrorism Strategies Causal Graphs

- 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.

Does Poverty Breed Terrorism? Causal Graphs

- 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.

Do Other Economic Conditions Matter? Causal Graphs

- 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.

What is Our Specific Mission Causal Graphs

- 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

Data Sources Causal Graphs

- 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
- World Bank data base
- CIA world factbook
- Various National Statistics Services
- Heritage Foundation

Dependent Variable Causal Graphs

- 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 Causal Graphs

Inference on Causal Flows Causal Graphs

- 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 Causal Graphs

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 Causal Graphs

- 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 Causal Graphs

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 Causal Graphs

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 Causal Graphs

- 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 Causal Graphs

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 Causal 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 Causal 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.

Y Causal Graphs

X

Z

A Causal ForkFor 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.

An Example of a Causal Fork Causal Graphs

- 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)

X Causal Graphs

Y

Z

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.

The Causal Inverted Fork: An Example Causal Graphs

- 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 in the Last Decade Under the Label of Artificial Intelligence

- 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 in the Last Decade Under the Label of Artificial Intelligence(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

Z in the Last Decade Under the Label of Artificial Intelligence

X

Y

Causal SufficiencyNo two included variables are caused by a

common omitted variable.

No hidden variables that cause two included variables.

Z

W in the Last Decade Under the Label of Artificial Intelligence

X

Z

Y

Causal Markov ConditionThe 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(X|W) • P(Y) • P(Z|X,Y)

b in the Last Decade Under the Label of Artificial Intelligence1

A

B

b3

b2

C

FaithfulnessThere 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.

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 Related to Terrorism Events in Countries and Economic and Political Characteristics.

- 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.

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