Endogenous weights and multidimensional poverty: A cautionary tale
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 like the widely studied asset indices.
Gaston Yalonetzky is a Lecturer in Economics at the Leeds University Business School and research associate at the Oxford Poverty and Human Development Initiative. Most of his current work is on the statistical operationalisation of concepts and ethical principles pertaining to human development, agency and capabilities, as well as distributional justice. His publications include articles in renowned journals of development studies, socioeconomic statistics and distributional analysis.
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