Your final assignment consists of two parts:
First, analysing two datasets using the statistical software STATA (represents 20% of your total
grade). As mentioned in the Module Outline, the guidelines for this data assignment will be
emailed by this 20 March 2020, along the stata files. This data assignment can be done
individually or in a group of up to four people. Students will receive instructions by 20 March on
how to specify whether will do this data assessment individually or in groups. Please patiently
await for further communication on this.
Second, writing an essay a maximum and strict upper limit of 2,500 words excluding references,
appendices, tables (any words written beyond that will not be read). This document provides the
guidelines for your essay.
Deadline for both assignments: 17 April 2020 15.00 pm. Please upload your assignment in
QMPLUS. Both assignments must be submitted in word version.
Essay Assignment Guidelines
Choose one topic that interests you from the following six topics:
1. Geography 2 Institutions 3. Conflict 4. Democracy 5. Foreign Aid 6. Corruption
You also have the choice to write:
A.Empirical essay or a
B. Critical literature review essay
The great majority of our students (90%) in recent years have written empirical essays. But this
is by no means obligatory. Critical literature reviews can also get top marks if well written. Both have
same word limit, roughly 2,500 words excluding table results, references, appendixes and footnotes.
Regardless of which type of essay you choose you are required to review at least four different
sources (e.g. journal articles or book chapters). Among the required four sources you can
include articles listed as compulsory readings for the module. Note that those doing a
literature review essays might well have to read and assess more than just four sources to be
comprehensive enough.
If you plan to do an empirical essay, it is vital to find whether the data you require exist and are
accessible. Beware essay ideas can be thwarted by infeasible or over-ambitious data-collection plans.
