Multidimensional Poverty : Why Not Make Up the Missing Joint Distribution Data?

Poverty is inherently multidimensional, encompassing both monetary and non-monetary dimensions. However, these outcomes are often collected in separate surveys, leaving the joint distribution partially unobserved. To improve social poverty comparisons, this paper proposes a new, simple method to address this data constraint: assume a fixed value for the missing part of the joint distribution. This approach allows the integration of outcomes collected from different surveys, unlike the mainstream method currently in use. Drawing on household surveys from six developing countries where both dimensions are observed, the paper shows that the method systematically outperforms traditional single-survey measures and “mash-up” measures. Monte Carlo simulations further confirm the robustness of the results across a wide range of data-generating scenarios. The findings highlight the value of the proposed method for monitoring multidimensional poverty and suggest that it may also benefit other social indicators facing similar data limitations