Deregit2025/ethiopia-financial-inclusion-forecast

Forecasting Ethiopia’s financial inclusion trajectory using sparse time-series data, event-based impact modeling, and scenario-driven projections aligned with the Global Findex framework. # Forecasting Financial Inclusion in Ethiopia ## Project Overview This project develops a transparent, policy-oriented forecasting system to analyze and project Ethiopia’s financial inclusion trajectory using the World Bank Global Findex framework. The analysis integrates historical data, event-driven modeling, and scenario-based projections to provide actionable insights for policymakers and financial institutions. ### Core Dimensions - **Access** — Account Ownership Rate (`ACC_OWNERSHIP`) - **Usage** — Digital Payment Adoption Rate (`ACC_MM_ACCOUNT`, `USG_P2P_COUNT`) ### Key Highlights - Historical trend analysis (2011–2024) - Scenario-based forecasts (2025–2027) under **Base**, **Optimistic**, and **Pessimistic** conditions - Inclusion projections vs. 60% policy target benchmark - Interactive dashboards for exploration and policy simulation --- ## Business Context Ethiopia is undergoing rapid digital financial transformation driven by: - Expansion of mobile money platforms such as **Telebirr** and **Fayda** - Market liberalization and emergence of new fintech entrants - National digital infrastructure initiatives including 4G coverage expansion Despite progress, financial inclusion growth has slowed, making it critical to understand historical patterns, project future trends, and evaluate the effectiveness of policy interventions. --- ## Project Objectives 1. Understand and enrich Ethiopia’s financial inclusion data 2. Analyze historical trends and structural gaps 3. Model the impact of major events and policy interventions on financial inclusion 4. Forecast key indicators for 2025–2027 using scenario-based methods 5. Communicate insights via an interactive dashboard for policymakers and stakeholders --- ## Data ### Primary Datasets - `ethiopia_fi_unified_data.csv` — unified dataset combining observations, events, and modeled impact links - `reference_codes.csv` — mapping of indicator and event codes to human-readable names ### Additional Resources - ` …