Creative Projects
08 Creative Projects — Building a Public Economics Research Portfolio
Selective liberal arts colleges and research universities increasingly value applicants who demonstrate how they use economics as a tool, not just a subject they study. One of the strongest ways to do this during junior year is by producing a student-led research project with real data and publishing it in a format that others can read, cite, and discuss.
The committee noted the opportunity to develop a rigorous project around the microfinance dataset you referenced. Instead of treating this dataset as a classroom exercise, the goal should be to transform it into a policy-oriented working paper that shows you asking your own economic questions, applying statistical analysis, and drawing policy implications.
This approach mirrors the type of work undergraduate economics students do in econometrics courses or research assistantships. If executed well, it can become a central intellectual artifact in your application — something you can reference in essays, interviews, and supplemental materials.
Project Concept: Microfinance Outcomes Policy Analysis
The core idea is to analyze the microfinance dataset as if you were an economist evaluating whether a policy intervention actually works.
Rather than summarizing existing research about microfinance, your project should investigate a specific economic question using the dataset itself.
Examples of research questions you could explore include:
- Do microfinance loans improve household income outcomes relative to comparable non-participants?
- Are repayment rates associated with borrower characteristics such as loan size, gender, or region?
- Does access to microfinance correlate with business formation or income stability?
- Are there diminishing returns for larger loans?
The goal is not to prove a predetermined conclusion. The goal is to demonstrate that you can formulate a question, test it with data, and interpret the results like an economist.
Analytical Methods to Demonstrate Econometric Thinking
Your analysis should go beyond descriptive statistics and show that you understand how economists test relationships between variables.
| Technique | Purpose | Example Application |
|---|---|---|
| Linear Regression | Estimate relationships between variables | Loan size predicting business income growth |
| Comparative Outcome Analysis | Compare outcomes across groups | Borrowers vs. non-borrowers |
| Correlation Analysis | Identify potential associations | Repayment rates vs. borrower characteristics |
| Data Visualization | Communicate findings clearly | Loan distribution charts or income trend graphs |
You do not need advanced graduate-level econometrics. What matters is demonstrating that you understand how economists use data to evaluate policies.
Recommended Technical Stack
You have not provided information about your programming background. If you already code, use the language you know. If not, this project is very manageable with beginner-friendly data tools.
| Tool | Purpose | Why It Works Well for Student Research |
|---|---|---|
| Python (Pandas, Statsmodels) | Data cleaning and regression analysis | Widely used in economics and easy to document |
| R (tidyverse, ggplot2) | Statistical analysis and visualizations | Standard tool in academic economics |
| Jupyter Notebook | Combine code, explanation, and charts | Creates a transparent research workflow |
| GitHub | Host the project publicly | Allows admissions readers to see methodology |
If you are new to programming, Python with Pandas is typically the fastest learning curve for dataset analysis.
Deliverable Structure (Working Paper Format)
Your final product should resemble a simplified undergraduate economics research paper. The goal is clarity and logical structure.
| Section | Content |
|---|---|
| Abstract | Short summary of the research question and main findings |
| Research Question | Explain the economic problem you are investigating |
| Dataset Overview | Describe the microfinance dataset and variables |
| Methodology | Explain regression or comparison methods used |
| Results | Charts, tables, and interpretation |
| Policy Implications | What the findings might mean for microfinance programs |
| Limitations | Discuss what the dataset cannot prove |
Including a limitations section is important. It shows intellectual maturity and prevents the project from sounding like advocacy rather than analysis.
Publishing the Research Publicly
The value of the project increases significantly if it becomes publicly accessible.
Consider publishing it in at least one of these formats:
- A working-paper style PDF hosted on a personal website
- A public GitHub repository containing the dataset analysis
- A blog post explaining the results for a general audience
- A submission to a student research journal that accepts high school work
The key objective is that your application can include a link where admissions readers can see the project directly.
This turns your research from a private assignment into a public intellectual artifact.
GitHub Portfolio Structure
If you publish the project on GitHub, structure the repository clearly so reviewers can navigate it quickly.
| Folder | Purpose |
|---|---|
| /data | Cleaned dataset used for analysis |
| /analysis | Python or R notebooks showing regression and statistics |
| /figures | Charts and visualizations |
| /paper | Final policy brief or research paper |
| README.md | Summary of research question and findings |
The README should clearly explain:
- The economic question
- The dataset used
- The analytical methods
- Your main conclusion
This allows someone unfamiliar with the project to understand it in under two minutes.
Optional Extension: Policy Brief Version
In addition to the full research paper, consider writing a short policy brief (2–3 pages) translating your results into a form that policymakers could read quickly.
The structure would look like:
- Problem statement
- Key data findings
- Policy interpretation
- Suggested reforms or considerations
This dual format — technical paper plus policy brief — demonstrates both quantitative reasoning and policy thinking, which aligns well with economics programs at schools like Amherst, Berkeley, and Pomona.
Research Portfolio Timeline (Junior Year)
| Month | Actions |
|---|---|
| March |
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| April |
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| May |
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| June |
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| July |
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| August |
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Why This Project Strengthens Your Application
For an economics applicant, original research demonstrates several qualities that colleges value:
- Quantitative reasoning
- Ability to interpret real-world data
- Intellectual curiosity about economic policy
- Initiative to pursue independent research
Most applicants interested in economics talk about markets or policy in abstract terms. A publicly published research project shows that you have already begun thinking and working like an economist.
Priyanka, if you execute this project carefully — asking a clear question, applying thoughtful statistical analysis, and publishing the results — it can become one of the most distinctive intellectual components of your application.