ECON0004: APPLIED ECONOMICS
STATA EMPIRICAL GROUP PROJECT
DEADLINE: MONDAY 11TH MARCH, 2024, 10AM
INSTRUCTIONS
The mark for this project is worth 20% of your total mark for the module.
Group work
Group formation. This is a group project with maximum 4 members per group. Please follow the instructions on Moodle to form groups. You have the option to form a group yourselves or be randomly assigned to one.
Co-operative learning. We encourage co-operative learning in this group project which emphasizes positive interdependence. Each group member should be assigned specific roles, which are essential for the group to function effectively. Make sure to complete the submission form on Moodle to indicate the roles of members.
Free-riding issue. Free-riding is prohibited. Should any member engage in free-riding, report this to the lecturer along with your group number. An investigation may lead to mark deductions for those who fail to contribute.
Marks. All group members will receive the same mark unless a free-riding issue is reported and verified.
Submission. Submit your project with the submission cover sheet as the first page. All work must be submitted anonymously. Do not put your name on any file name or inside any document. Name your submission 'Stata project-xxx', replacing 'xxx' with your group number. Please elect one group member to submit the project for the group. ONLY one submission per group is allowed.
Your work should not exceed 800 words. This includes everything except the figures, mathematical formulae, data tables. references, appendix and the submission cover sheet. Please include your STATA commands and output in the appendix. You must state your word count in the provided submission cover sheet.If your submitted work exceeds the word count, Faculty Word Limit Penalties will apply as follows.
a. For work that exceeds the word count by less than 10%, the mark will be reduced by five percentage points, but the penalised mark will not be reduced below the pass mark: marks already at or below the pass mark will not be reduced.
b. For work that exceeds the word count by 10% or more, the mark will be reduced by ten percentage points, but the penalised mark will not be reduced below the pass mark: marks already at or below the pass mark will not be reduced.
Prepare a Word or PDF file to submit your work and allow enough time to submit your work. Waiting until the deadline for submission risks facing technical problems when submitting your work, due to limited network or systems capacity.
You will be awarded a mark of 0% in any assessment component where you: (1) are absent from the summative assessment component or, (2) do not attempt the summative assessment component or, (3) attempt so little of the summative assessment component that it cannot be assessed. Please check the UCL Academic Manual (Section 3.11) for information on the consequences of not submitting or engaging with any of your assessment components.
If you have extenuating circumstances that affect your ability to engage with any of the module assessment components within the required deadlines, please apply for alternative arrangements to the Economics Department as soon as possible. Please contact the BSc Year 1 Teaching and Learning Administrator Michelle Ming Chih Wu (Email: [email protected]) on extenuating circumstances related questions.
If you have a disability or long-term medical condition, you may be entitled to adjustments for assessments. Please see Section 5 of the Academic Manual for information on how to apply for adjustments. Note that the application must be made well in advance of the assessment.
The assessment submission area has been set up to allow work to be submitted late. This is in place for those with permitted extensions due to SORAs or Extenuating Circumstances. If you submit your work after the deadline and do not have a permitted extension due to a SORA or Extenuating Circumstance your work will be subject to late penalties as set out in UCL’s Academic Manual (Section 3.12 - https://www.ucl.ac.uk/academic-manual/chapters/chapter-4-assessment-framework-taught-programmes/section-3-module-assessment#3.12).These penalties will not be applied in provisional marks but will be applied later by the Departmental Tutor as appropriate.
The Economics Department follows UCL’s guidance on academic assessment irregularities, as set out in Chapter 6 (Section 9) of the Academic Manual: https://www.ucl.ac.uk/academic-manual/chapters/chapter-6-student-casework-framework/section-9-student-academic-misconduct-procedure. Assessment irregularities include (but are not limited to) plagiarism, self-plagiarism, unauthorised collaboration between students, access another student’s assessment, falsification, contract cheating, and falsification of extenuating circumstances. If a possible assessment irregularity is discovered related to your assessed work or your extenuating circumstances, it will be notified to the Chair of the Board of Examiners immediately and you will be informed of any steps that are going to be taken, in line with the UCL procedures. Penalties for assessment irregularities range from an adjustment to your provisional marks to exclusion from UCL. All students should make themselves familiar with what is considered a breach of assessment regulations and what the potential penalties are as detailed in the UCL regulations https://www.ucl.ac.uk/academic-manual/chapters/chapter-6-student-casework-framework/section-9-student-academic-misconduct-procedure. UCL has produced a guide to on Academic Integrity. Check https://www.ucl.ac.uk/students/exams-and-assessments/academic-integrity on what Academic Integrity is, why it is important, and what happens if you breach it.
Artificial Intelligence (AI)
According to UCL’s AI guidelines, this assignment falls under Category 2: AI tools can be used in an assistive role. For this assignment, this means that you can use AI for:
Giving feedback on your draft
Proofreading your draft
Please note that Generative AI can be a useful starting point to gather background information on a topic, but be aware that:
Generative AI produces information that may be inaccurate, biased, or outdated.
Generative AI is not an original source of information: it reproduces information from unidentified sources.
Generative AI may fabricate quotations and citations.
It is always best to refer to original and credible sources of information.
If you do choose to use generative AI tools, you must always:
Critically evaluate any output it produces.
Carefully check any quotations or citations it creates.
Correctly document your use of the tools so that it can be appropriately acknowledged, according to UCL’s acknowledging and referencing AI guideline.
The use of AI tools that exceeds that permitted in the assessment brief constitutes Academic Misconduct.
TASK
COVID-19 has affected almost all the countries in the world, although the extent of its effects varied across different countries. While some countries have effectively managed to curb the spread of the virus, others have faced more significant challenges. The varying severity of the pandemic's impact among countries can be attributed to several factors, including differences in policy responses by governments.
The Coronavirus Pandemic (COVID-19) - Our World in Data is a very comprehensive dataset on COVID and includes variables in the following categories across 207 countries:
Deaths
Cases
Tests
Hospitalizations
Vaccinations
Mortality risk
Excess mortality
Policy responses
Please use this dataset to design a research project to assess the effectiveness of governmental measures taken to mitigate the COVID-19 pandemic in a country of your choice.
Submit a report (word limit: 800 words) that includes the following sections:
Introduction: Specify the country you have chosen for this project, provide the context of the research, and discuss why the research question is both important and relevant.
Data Analysis:
o Explain the setup of your regression analysis, detailing how you measure the dependent and independent variables, which control variables to include based on your judgment, and the functional forms to use.
o Discuss the results of your regression analysis, including the magnitude and significance of the coefficients, and provide both statistical and economic interpretations of the results. Additionally, reflect on the limitations of your analysis if any.
o Use data visualization such as charts/figures, where appropriate, to enhance the presentation and comprehension of your findings.
Conclusion: draw your main conclusion.
Reference: See the UCL guide on how to cite your references properly (Harvard referencing style is recommended).
Note that:
You can download the dataset from the website in either XLSX or CSV format. Then, use the 'import' function in Stata to load the dataset for analysis. We have demonstrated how to import an Excel dataset into Stata during the practical lecture.
Ensure that you use the most up-to-date data available.
As this is a Stata empirical project, you are required to use Stata exclusively for data analysis, although you can use Excel to prepare or clean the dataset before importing it into Stata.
As indicated on the module's Moodle page, a sample project from 2021 investigated the effectiveness of measures implemented by the UK government to mitigate the COVID-19 pandemic. You may use this as a reference. However, your project must be original. You cannot simply replicate or make minor changes to the sample project. If you are unsure what constitutes minor changes, please contact the module leader.
You might find the following writing guides useful:
o UCL IOE Writing Centre’s ‘Organise, Structure and Edit’ guide
o Pomona College’s The Young Economist’s Short Guide to Writing Economic Research
Please submit your report via the submission link on Moodle by MONDAY 11 MARCH, 2024, 10AM. In the report, include your STATA commands and output in the appendix.