Multidimensional poverty measures have become a standard feature in poverty assessments. A large and growing body of work uses endogenous (data driven) weights to compute multidimensional poverty. We demonstrate that broad classes of endogenous weights violate key properties of poverty indices such as monotonicity and subgroup consistency, without which poverty evaluation and policy targeting are seriously compromised. Using data from Ecuador and Uganda we show that these violations are widespread. Our results can be extended to other composite welfare measures such as the widely used asset indices.

中文翻译：

---

内生的权重和多维贫困：一个警示故事

多维贫困测度已成为贫困评估中的标准特征。大量且不断增长的工作使用内在的（数据驱动的）权重来计算多维贫困。我们证明，宽泛的内生权重违反了贫困指数的关键特性，例如单调性和亚组一致性，如果没有这些特性，贫困评估和政策目标将受到严重损害。使用来自厄瓜多尔和乌干达的数据，我们表明这些违法行为很普遍。我们的结果可以扩展到其他综合福利措施，例如广泛使用的资产指数。