Suicide Bomber Instructor Discussion Guide We Want To Use

Suicide Bomber Instructor Discussion Guide We Want To Use

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Suicide Bomber Instructor Discussion Guide
We want to use this paper as an example for you to follow in your own
work.
Put on board the parts of an empirical paper—just the bolded parts and
have them say what goes in each one.
I.
Introduction
A.
Statement of the topic and question to be analyzed
B.
Rationale for choice of the topic (or why you find this
interesting)
C.
Explanation of the organization of the remainder of the paper
II. Literature Review
Choose some form (e.g., chronological or thematic) to organize the
literature review. Mere listing and summary of several sources is not
acceptable. A good literature review interweaves the various articles
in a seamless way.
III. Theoretical Analysis
Present a brief version of a model or highlight the theoretical source
of the hypothesis to be tested. In many cases, you may wish to combine
the literature review and theoretical analysis into a single section.
For example, a paper you review may contain a version of the model you
wish to adapt for your own analysis.
I
All data and analyses must be completely documented and available for
inspection.
V. Empirical Analysis (the main and longest part of the empirical
paper)
A.
The Data
i.
Provide sources on all variables
ii.
Provide summary statistics on all variables in a
well-organized table
B.
Presentation and Interpretation of Results
V. Conclusion
A.
Restate the topic or question that was analyzed
B.
Provide your answer or conclusion, and compare to previous results
in the literature
C.
Point out the best areas for further research
VI. References
Stylistic Issues
Explain the JEP journal—more talky than usual econ journals
Notice the catchy intro.
How is the paper structured? Get students to list elements:
Intro
Where is the Lit Rev? Included Lit Rev in the Intro
Citation style
Author name (Year) and references at the end
Highlight p. 225 – “Our argument fits within the growing body of lit”
Summary Stats
Fig 1
See SuicideBomber.xls for discussion of correlation
Notice how they discuss specific numbers from figures and tables.
Discuss box model (see Excel file)
Table 1
Excel file reproduces parts of this table
Data Description
How the data were collected?
Augmented with ISA data.
I emailed them and they refused to give me the data because it’s
secret.
Table 2 Target Importance – size of city and military
What are they trying to say with this table?
Big city attacks kill and injure more people so they are more
important
Plus, they are trying to figure out how to measure the importance of a
target and have two competing measures, large/small city and
military/civilian
Content: Bottom Line
What is this paper’s main point?
Suicide bombing involves an optimization problem.
Suicide bombers are educated.
Are older, better educated suicide bombers more productive?
Content: Theory
What is human capital?
People’s education and training. Capital the stock of machinery and
equipment that a firm uses to produce. When it buys capital, it
invests.
Human capital is the stock of skills and knowledge in a person. When
you go to college, you are investing in yourself so you are said to be
acquiring human capital.
What is the optimization problem involved for the head of a terrorist
organization?
Who to send on a mission and how to do it. “Attack assignment”
What is the suicide bomber’s optimization problem? On a bus, what is
the choice variable?
When to blow yourself up. Too soon is bad. Too late is bad.
Content: Metrics
Start with anecdotal evidence from Table 3. What does this show?
That educated people are bombers.
Table 4 is a probit regression. The dependent variable is a dummy
variable, 0/1. We will study this later.
Coefficient on Education is not positive for large city, but they
report it anyway.
Conclusion: older bombers assigned to large cities, less educated to
military targets
Table 5 is the key table. What is the table trying to determine?
If older, more educated bombers are more productive. It’s the title of
the paper.
If you are unsuccessful, what is your value for number of people
killed?
Zero
What does the scatter plot with age look like?
Bunch of zeroes (42 to be exact) sprinkled in for the 148 dots. Y axis
has #killed, X axis has age
This is why they separate out the All and Only successful regressions.
Notice that they report both. You should report different estimates
from different models in your paper. Use this table style to report
the coefficients and other pertinent information.
HUGE QUESTION
If Education coefficient is negative, doesn’t this mean that education
lowers the number of people killed? Doesn’t this disprove the entire
argument of the article?
No, because of the interaction term.
As education goes from 0 to 1, both the Education coefficient and the
interaction term kick in.
How to construct an interaction?
Here are some hypothetical observations. Fill in the table.
Age
Education
Education Dummy
Size of City
Target Dummy
Education x Target
19
In High School
Tamra—26,000
24
Masters Candidate
Haifa—300,000
21
High School
Tira—21,000
29
Law School Graduate
Jerusalem—800,000
How to interpret the coefficients? What is the effect of Education on
the number killed?
See Excel file, Table5 sheet.
You can set up a sheet like this when you are reading an empirical
paper. Reading empirical papers is NOT like reading a novel. You have
to dig deep to figure out what is going on.
Explain how the sheet works: you enter values in the yellow cells and
the sheet computes the Predicted Y (number killed) for that case.
Compare the cases cited in the article and entered in the Excel file.
Explain how the case approach gives the same value as that computed in
the sheet as the change in target for an uneducated, 25 year old.
Predicted Y = b0 + b1Age + b2Education + b3Target + b4Age x Target +
b5Education x Target
They use a formula that can be derived like this.
If an uneducated, 25 year old, small city target (initial value)
changes to an uneducated, 25 year old, big city target (new value), we
would compute
Change in Predicted Y = b0 + b1[25] + b2[0] + + b3[1] + b4[25] x [1] +
b5[0] x [0]
- b0 - b1[25] - b2[0] – b3[0] – b4[25] x [0] – b5[0] x [0]
The b0, b1[25], b2[0], and b5[0] x [0] terms drop out and all you have
left is
Change in Predicted Y = b3[1] + b4[25] x [1] – b3[0] – b4[25] x [0]
This is the formula that they use.
You get the same answer if you compute Predicted Y for an uneducated,
25 year old, small city and then compute Predicted Y for an
uneducated, 25 year old, big city and subtract one from the other.
That is what the Excel file does.
The regression says that more people are killed when a bigger target
is chosen for uneducated, 25 year old bombers.
The effect depends on the age. What happens if an 18 year old bomber
moves from a small to a large city?
Compare the cases. -0.24 is the answer (replicating the 0.2 in the
article).
What about education? (which is what the article is supposed to be
about)
Compare cases
Assigning a 25 year old, uneducated bomber to a big city target gets
you 17.5 people killed. What’s the RMSE? They don’t tell us. The R^2
is pretty low, though, so there’s probably a lot of dispersion.
Assigning a 25 year old, educated bomber to a big city target gets you
19.15 people killed. That’s an increase of 1.65 people killed. More
educated bombers kill more people when the target is a big city.
Does Education improve productivity for a small city?
Compare cases. The answer is no.
Table 6—what is the point?
That successful bombers are older and more educated.
Excel file has the box model for this.
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