Poverty Reduction

Poverty reduction research increasingly focuses on refining poverty measurement and understanding the societal factors hindering its alleviation. Current efforts utilize advanced statistical modeling, including machine learning algorithms and agent-based models, to analyze diverse data sources like satellite imagery, mobile phone records, and social media to improve poverty mapping and program targeting. This work highlights the importance of addressing not only economic inequality but also discriminatory biases and systemic factors that perpetuate poverty, aiming to inform more effective and equitable anti-poverty policies. The resulting insights have significant implications for policy design and implementation, promoting more targeted and impactful interventions.