What This Document Is
This is a problem set designed for an advanced undergraduate economics course focused on the analytics of economic development. Specifically, it’s the first assignment for Econ 270C at the University of California, Berkeley, centered around a real-world case study investigating the impact of educational interventions. The assignment utilizes a rich dataset collected from a field experiment in rural Kenya, examining the long-term effects of merit-based scholarships on student achievement. It requires students to apply econometric techniques to analyze the data and draw meaningful conclusions.
Why This Document Matters
This problem set is crucial for students enrolled in Econ 270C, or similar courses in development economics and econometrics. It’s best used as a practical application of the theoretical concepts discussed in lectures. Students preparing for exams, or those seeking to solidify their understanding of applied econometrics, will find this assignment particularly valuable. Successfully completing this assignment demonstrates proficiency in data analysis and interpretation within the context of economic development.
Topics Covered
* Non-parametric Regression Techniques
* Kernel Density Estimation
* Econometric Analysis of Educational Interventions
* Impact Evaluation in Developing Countries
* Data Analysis using STATA
* Treatment Effect Estimation
* Distributional Analysis of Test Scores
* Long-Term Educational Outcomes
What This Document Provides
* A detailed description of a research project investigating the effects of scholarships on student performance in Kenya.
* Instructions for utilizing a STATA dataset (“PSET1-2007.DTA”) containing individual-level data on student test scores, program participation, and demographic characteristics.
* A series of analytical tasks designed to assess the impact of the scholarship program on various educational outcomes.
* Guidance on employing statistical methods to explore potential heterogeneous effects of the program across different student groups.
* Specific prompts for creating visualizations and presenting regression output to support your findings.
