Poverty Prediction and Targeting Over Time and Space : Evidence from Nigeria

Understanding poverty dynamics is crucial to target and tailor economic policies in developing countries like Nigeria – a country at the risk of hosting about a quarter of all people living in poverty worldwide. To facilitate the targeting of poverty-reducing interventions, we build a nationally representative panel dataset with more than a hundred covariates and apply econometric and machine learning tools to predict and examine factors associated with static, transient, and persistent poverty status of Nigerian households. Results show that demographic, asset holdings, access to infrastructure, and housing indicators can predict poverty accurately in 80% of cases