petermose-max/kenya-financial-inclusion-analysis

# Kenya Financial Inclusion Analysis 2024 ## FinAccess Household Survey Analysis in SQL and Python **Date:** 2024 to 2025 **Dataset:** FinAccess 2024 Household Survey, FSD Kenya **Tools:** PostgreSQL 18, Python 3.13, pandas, SQLAlchemy, Jupyter Notebook This project analyses the FSD Kenya FinAccess 2024 Household Survey to understand financial inclusion patterns across Kenya's 47 counties. The work demonstrates a professional data analysis workflow using PostgreSQL for database analysis, reusable SQL views, county scorecards, and export-ready outputs for further Python exploratory data analysis. The SQL results show that Kenya has broad financial access driven by mobile money, but formal banking, insurance, financial health, and financial resilience remain uneven across counties and income groups. ## Key Findings - National mobile money use is high at 78.69%, confirming mobile money as Kenya's strongest financial access channel. - Bank use is much lower at 45.52%, while insurance use is 19.25%, showing that deeper formal financial inclusion remains more limited. - Overall access is 81.43% under the working SQL definition, but exclusion is concentrated in counties such as West Pokot, Turkana, Tana River, Narok, and Migori. - Income inequality is substantial: overall access rises from 68.57% in the lowest income quintile to 92.83% in the highest income quintile. - Gender gaps are nuanced. The national overall access gap is small, but the national bank-use gender gap is 10.59 percentage points and county-level gaps vary. ## Project Structure ```text FinancialInclusion2024/ |-- .gitignore |-- README.md |-- data_dictionary.md |-- requirements.txt |-- data/ | |-- README.md | `-- finaccess2024.csv |-- notebooks/ | |-- 00_setup.ipynb | |-- 01_sql_basic.ipynb | |-- 02_sql_advanced.ipynb | |-- 03_eda.ipynb | `-- 04_modelling.ipynb `-- outputs/ |-- report_summary.md |-- figures/ `-- finaccess_county_scorecard_2024.csv (generated by 02_sql_advanced.ipynb) `` …