Microlevel structural poverty estimates for southern and eastern Africa

• 成果类型: Article
• 署名作者: Tennant, Elizabeth; Ru, Yating; Sheng, Peizan; Matteson, David S.; Barrett, Christopher B.
• 署名单位: Cornell University; Asian Development Bank; Cornell University; University of Chicago; Cornell University; Cornell University; Cornell University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12483
• DOI: 10.1073/pnas.2410350122
• 发表日期: 2025-02-11
• 关键词: small-area estimation; persistent poverty; ECONOMICS; DYNAMICS; income; traps

摘要

For many countries in the Global South traditional poverty estimates are available only infrequently and at coarse spatial resolutions, if at all. This limits decisionmakers' and analysts' ability to target humanitarian and development interventions and makes it difficult to study relationships between poverty and other natural and human phenomena at finer spatial scales. Advances in Earth observation and machine learningbased methods have proven capable of generating more granular estimates of relative asset wealth indices. They have been less successful in predicting the consumptionbased poverty measures most commonly used by decision-makers, those tied to national and international poverty lines. For a study area including four countries in southern accessible machine learning methods, and asset-based structural poverty measurement to address this gap. This structural poverty approach to machine learning-based poverty estimation preserves the interpretability and policy-relevance of consumption-based poverty measures, while allowing us to explain 72 to 78% of cluster-level variation in a pooled model and 40 to 54% even when predicting out-of-country.