QUESTION 1: The Long-Term Impact of the Slave Trade [25 Points]
This part uses data from the following paper, available in the assessment folder on Moodle:
(2008). “The Long-Term Effects of Africa’s Slave Trades.” Quarterly Journal
of Economics 123 (1): 139-176.
Copyright By PowCoder代写 加微信 powcoder
To help answer this question, first read the paper. Part of your task is to replicate and extend some
of Nunn’s results, which he produces using instrumental variables. If you are unable to exactly
reproduce Nunn’s results, report your best effort to do so. Whether or not you can exactly replicate
the paper’s findings, ensure that both your write-up and your R script clearly indicate how you
obtained your results. The dataset for this question is nunn.da. It contains these variables:
Variable name
Variable description
Country name
In realgdp2000
Log real per capita GDP in 2000, also called “In y” in the paper
In export area
Log total number of slaves exported, divided by land area
atlantic dist
Sailing distance to nearest destination of Atlantic slave trade
indian dist
Sailing distance to nearest destination of Indian slave trade
saharan dist
Overland distance to nearest port of export for Saharan slave trade
redsea dist
Overland distance to nearest port of export for Red Sea slave trade
colonial power
Name of colonizer, if any, prior to independence
equator dist
Distance from equator
Minimum monthly rainfall
Average maximum humidity
Average minimum temperature
In coastline area
Log coastline divided by land area
low distance
=1 if situated at a low distance from a major slave destination
high slavery
=1 if country had a high level of slave exports
Note that low distance and high slavery do not feature in Nunn’s paper: they have been created for
this question. The other variables are identical to those used in the paper. You will also need to load
the AER package for this question.
Answer the following questions:
a) Run a simplified version of the instrumental variables analysis that Nunn uses in his paper,
using the Wald estimator and binary variables. Use the single binary instrumental variable
low distance, the binary treatment variable high slavery, and the outcome variable
In realgdp2000. You should:
Explain, in this case, what type of country is a complier and what type of country is
an always-taker.
Calculate and report the proportion of compliers and the intent-to-treat effect.
Use your answers from (ii) to calculate and report the Complier Average Causal
Effect (CACE) of high slavery on GDP.
Use an appropriate method to calculate and report the p-value for this CACE
Briefly interpret your results.
b) Turning now to the analysis that Nunn conducts in his paper, replicate the first-stage results
from the first column of Table IV on p.162. Report your results.
Note: You only need to produce the four coefficients and four standard errors.
c) Are the instruments in this paper subject to the weak instrument problem? What consequences
does this have, if any, for our interpretation of the results? Explain your answer, providing
evidence from the data.
d) Do you think that the instruments in this example satisfy the exclusion restriction assumption?
Briefly explain your answer.
) Replicate the second-stage coefficients and standard errors for In(exports/area) in columns (1),
(2) and (3) of Table IV on p.162 of the paper. Report your results and briefly interpret each of
the three estimated LATEs.
Why do you think that Nunn estimated the additional models in columns (2) and (3) of Table
IV that include coloniser fixed effects and geographic controls?
QUESTION 2: A Simulated Experiment |25 Points|
This question analyses a simulated experimental dataset contained in the file “2022essay_q2.Rda.”
It includes 100 units and the following six variables:
Variable name
Variable description
Potential outcome under treatment
Potential outcome under control
Reporting status under treatment and control (=1 if reports data, 0
otherwise)
A baseline covariate
Treatment assignment (=1 if in treatment group, 0 if in control group)
Observed outcome (assuming no attrition occurred)
For parts (a) to (d), we will assume that no attrition occurred. That means using all 100 units for our
calculations. Answer the following questions:
a) Assuming no attrition, is it likely that randomisation failed in this experiment? Provide evidence
from the dataset for your answer.
b) Again assuming no attrition, calculate and report:
The true average treatment effect in terms of potential outcomes
The observed average treatment effect from the experiment
Then use your answers to explain whether selection bias is negative, zero, or positive in this
experiment.
c) Explain the direction of the selection bias in (b): why is it negative, zero, or positive? Provide
evidence from the dataset for your answer.
d) Still assuming no attrition, use an appropriate technique to adjust your calculation from (b) (il)
to come closer to recovering the true ATE from the experiment. How close is your new estimate
to the true ATE?
e) Now we’ll examine attrition, assuming that it occurs as described by the variable R. Are units
in this experiment missing at random? Provide evidence from the dataset for your answer.
Evaluate the following two statements about this dataset:
“You cannot estimate an ATE for always-reporters with this data”
*Within values of y, units are missing-at-random”
Are these statements correct? Provide evidence from the dataset for your answers.
程序代写 CS代考 加微信: powcoder QQ: 1823890830 Email: powcoder@163.com