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<p>This study explores the potential of adapting a WOE (Logit) method, a combination of techniques such as weight of evidence, information value, logit regression, and a score-scaling approach based on doubling the odds, for poverty targeting. To verify the effectiveness and accuracy of this method, the study applies it to develop targeting tools based on international and Vietnamese national poverty lines, and compares their accuracy to commonly used poverty-targeting tools such as the Simple Poverty Scorecard, Proxy Means Test, and Poverty Assessment Tool. The study uses coverage rate as a criterion to compare the accuracy of targeting tools in identifying the poor at household and individual levels. The results indicate that targeting tools constructed using the WOE (Logit) method outperform those developed using other methods. Depending on the poverty line, this method improves poverty identification accuracy by 1.9–5.9 percentage points for households and 1.5–3.3 percentage points for individuals compared to other methods. Therefore, this study contributes an additional method for constructing targeting tools with high accuracy alongside existing approaches.</p>

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Can Weight of Evidence Method Improve Poverty Targeting?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月6日

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  • Be Thanh Duong,
  • Orkhan Sariyev,
  • Manfred Zeller

摘要

This study explores the potential of adapting a WOE (Logit) method, a combination of techniques such as weight of evidence, information value, logit regression, and a score-scaling approach based on doubling the odds, for poverty targeting. To verify the effectiveness and accuracy of this method, the study applies it to develop targeting tools based on international and Vietnamese national poverty lines, and compares their accuracy to commonly used poverty-targeting tools such as the Simple Poverty Scorecard, Proxy Means Test, and Poverty Assessment Tool. The study uses coverage rate as a criterion to compare the accuracy of targeting tools in identifying the poor at household and individual levels. The results indicate that targeting tools constructed using the WOE (Logit) method outperform those developed using other methods. Depending on the poverty line, this method improves poverty identification accuracy by 1.9–5.9 percentage points for households and 1.5–3.3 percentage points for individuals compared to other methods. Therefore, this study contributes an additional method for constructing targeting tools with high accuracy alongside existing approaches.