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

Author

Listed:
  • Benoit Decerf

    (Development Finance and Public Policies, University of Namur)

  • Merry Ferrando

    (Tilburg School of Economics and Management, Tilburg University)

  • Balint Menyhert

    (University of Turku)

Abstract

Poverty is inherently multidimensional, encompassing both monetary and nonmonetary dimensions. However, these outcomes are often collected in separate surveys, leaving the joint distribution partially unobserved. To improve social poverty comparisons, we propose 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, we show that our method systematically outperforms traditional single-survey measures and “mash-up” measures. Monte Carlo simulations further confirm the robustness of our results across a wide range of datagenerating scenarios. Our findings highlight the value of our proposed method for monitoring multidimensional poverty and suggest it may also benefit other social indicators facing similar data limitations.

Suggested Citation

  • Benoit Decerf & Merry Ferrando & Balint Menyhert, 2025. "Multidimensional Poverty: Why not Make Up the Missing Joint Distribution Data?," DeFiPP Working Papers 2504, University of Namur, Development Finance and Public Policies.
    • Handle: RePEc:nam:defipp:2504
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