ECON0004: APPLIED ECONOMICS
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ECON0004: APPLIED ECONOMICS
STATA EMPIRICAL GROUP PROJECT
DEADLINE: MONDAY 16TH MARCH, 2026, 2PM
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 'Python 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 1000 words. This includes everything except the figures, mathematical formulae, data tables. references, appendix, Jupter notebook and the submission cover sheet. 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-integrityon 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:
• assisting with coding
• providing feedback
• 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
Income inequality is one of the most pressing issues economists should address today. It affects economic growth, social mobility, political stability, and overall well‑being. Understanding what drives inequality is therefore essential for informing public policy and designing effective interventions.
In this project, you will investigate how a specific factor—selected from Our World in Data — relates to income inequality. You may choose either:
• a cross-sectional sample of countries for a given year, or
• a time series dataset for a single country
as long as your dataset contains an adequate number ofobservations. You should select relevant control variables from Our World in Data or other reputable data sources (ensure to reference them properly) to account for other factors influencing income inequality.
Submit a report (word limit: 1000 words) that includes the following sections:
Title: The Effect of [choose a factor from Our World in Data] on Income Inequality: Evidence from [your chosen sample].
1. Introduction
• Research Context: Explain the significance, relevance, and importance of the topic. Provide background on why the relationship you are studying matters for understanding income inequality.
• Sample Selection: Specify the sample chosen for this project, whether it is a cross-sectional sample of countries for a given year, or a time series dataset for a single country.
2. Data
• Dependent and Independent Variables: Explain how you measure the
dependent variable (income inequality) and your chosen independent variable.
• Control Variables: List and justify your chosen control variables. These should be factors that may also influence inequality.
• Summary Statistics: Present a summary statistics table for all the key variables.
• Scatter Plot: Present a scatter plot showing the relationship between income inequality and your chosen independent variable.
3. Regression analysis
• Regression Setup: Clearly state your econometric model and justify the functional forms used (e.g., linear model, log‑transformations).
• Results Discussion:
• Sign, Magnitude and Statistical Significance: Discuss the sign and magnitude of the coefficients and assess their statistical significance.
• R-squared: Offer an interpretation ofthe R2 value of the model.
• Economic Interpretation: Provide economic interpretations of your findings.
4. Limitations: Reflect on any limitations of your analysis.
5. Conclusion: Summarise your main findings and discuss their implications for
understanding income inequality.
At the end of the report, include:
• References: Follow the UCL guide on how to cite your references properly (Harvard referencing style is recommended).
• Appendix: Include details of your data source and your hypothesis testing. If you use AI, you should include your acknowledgement here as well.
Note that:
• Use the most up-to-date data available, wherever possible, to ensure the relevance of your analysis.
• As this is a Python empirical project, all data analysis must be conducted entirely in Python. You may use Excel or similar tools for preliminary data preparation or cleaning prior to importing the data into Python. Such preparation is considered an integral part of the research process.
• A sample project and the accompanying Jupyter notebook are provided.
• 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
• Submit your report and the accompanying Jupyter notebook as two separate documents via the submission link on Moodle by the deadline.
2026-02-28