J-nkumbatir/IHS5-Poverty-Analysis

This project investigates the key determinants of poverty in Malawi using data from the Fifth Integrated Household Survey (IHS5). It combines survey-weighted econometric analysis with reproducible workflows in both Stata and Python # Analyzing Determinants of Poverty in Malawi: Evidence from IHS5 A full end-to-end data analysis project using Stata and Python. **Author**: James Nkumbatira **Qualification**: BA (Public Administration) **Date**: July 2026 --- ## 📌 Project Description This project examines the **socio-economic determinants of poverty** in Malawi using the **Fifth Integrated Household Survey (IHS5)** conducted by the National Statistical Office (NSO). The analysis combines **survey-weighted econometric techniques** with **reproducible data science workflows** to understand why some households are more likely to fall into poverty. The study focuses on key household characteristics such as education, household size, dependency ratio, gender of household head, residence (urban/rural), and regional location. Two complementary models were estimated: - **Logistic Regression** → Predicts the probability of a household being poor - **OLS Regression** → Explains variation in log per capita consumption The project demonstrates a full end-to-end data analysis pipeline — from data wrangling and cleaning to exploratory analysis, modeling, diagnostics, and policy interpretation. --- ## 📁 Project Structure IHS5_Poverty_Analysis/ ├── Data/ │ ├── Raw/ # ← Not uploaded (contains official IHS5 modules) │ └── Processed/ # ← Uploaded (cleaned datasets) ├── Output/ │ ├── Figures/ │ ├── Tables/ │ └── Reports/ ├── Scripts/ # Stata .do files ├── Notebooks/ # Jupyter notebooks ├── Logs/ └── README.md > **Note**: The raw IHS5 datasets are **not included** in this repository due to size and data licensing. Users who wish to replicate this analysis can request access to the official IHS5 datasets from the **National Statistical Office (NSO) of Malawi**. --- ## 🚀 Key Findings - **Education** is the strongest protective factor against poverty. - Female-head …