Multidimensional well-being indicators have the potential to reduce the “bias” associated to monetary indicators. However, they face stringent data constraints. This paper studies the construction of indicators that strike a balance between (i) reliability in approximating conceptually sound well-being comparisons and (ii) simplicity of application and communication. The recommendations focus on globalmultidimensional poverty measures. The paper identifies three potential sources of improvements: “wasting” less data, better filtering the data, and further developing multidimensional analysis. Less information would be “wasted” by avoiding needlessly dichotomizing all the variables, using the available mortality data, and combining variables from separate surveys. To filter the data better, “equal weights” could be replaced by weights selected from external information on preferences. When the data permit, the unit of analysis should be switched from household level to individual level. Finally, multidimensional indicators should be used to help move beyond a suboptimal “dimension-by-dimension” approach to policy making.

Multidimensional Well-Being Measurement Practices: A Review Focused on Improving Global Multidimensional Poverty Indicators

Resource Key: DNLUNJZI

Document Type: Report

Creator:

Author:

  • Benoit Decerf

Creators Name: {mb_resource_zotero_creatorsname}

Place: Washington D.C.

Institution: World Bank

Date: June 2024

Language: en

Multidimensional well-being indicators have the potential to reduce the “bias” associated to monetary indicators. However, they face stringent data constraints. This paper studies the construction of indicators that strike a balance between (i) reliability in approximating conceptually sound well-being comparisons and (ii) simplicity of application and communication. The recommendations focus on globalmultidimensional poverty measures. The paper identifies three potential sources of improvements: “wasting” less data, better filtering the data, and further developing multidimensional analysis. Less information would be “wasted” by avoiding needlessly dichotomizing all the variables, using the available mortality data, and combining variables from separate surveys. To filter the data better, “equal weights” could be replaced by weights selected from external information on preferences. When the data permit, the unit of analysis should be switched from household level to individual level. Finally, multidimensional indicators should be used to help move beyond a suboptimal “dimension-by-dimension” approach to policy making.

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