The majority of the world’s poor live in rural areas, but the amount of reliable and harmonised information on the nature of rural poverty is scarce.
To help meet this gap in understanding the challenges of the rural poor, the Food and Agriculture Organization of the United Nations (FAO) has collaborated with the Oxford Poverty and Human Development Initiative (OPHI), to create an innovative Rural Multidimensional Poverty Index (R-MPI).
The R-MPI broadens existing methods for measuring rural poverty by taking a closer look at rural people’s capabilities: It measures food security, nutrition quality, education, living standards, agricultural assets, and exposure to environmental and other risks. The R-MPI builds on the notion that a single dimension, such as household income, does not accurately capture poverty in rural areas.
That notion is already reflected in the Global Multidimensional Poverty Index (MPI), which was launched in 2010 by the United Nations Development Programme and OPHI and covered 109 countries and 5.9 billion people in 2021.
The R-MPI, which expands the scope of the global MPI, also includes an innovative combination of geospatial and survey data that quantifies rural dwellers’ risks of exposure to drought, floods or heat waves.
The usefulness of this new tool is illustrated in the joint FAO-OPHI report, which tested the index using recent household surveys in Ethiopia, Malawi, the Niger and Nigeria.
The R-MPI was tested in the field by the University of Malawi at Zomba, specifically in 64 rural areas of Malawi. Community members were asked to review the dimensions included in the R-MPI, based on their life experience, and define, in their own words, rural hardship and poverty. While most dimensions turned out to be considered crucial, others – such as state of mind or physical appearance – also surfaced. While not all of these can easily be elicited in large-scale surveys, important lessons were learned about the limitation of money metrics and the importance of tailoring the measurement to rural contexts.
All this is not just about producing more data. A more precise identification of who the extreme poor are, where they live, and what specific constraints prevent them from escaping poverty in rural areas, can play a crucial role in shaping more accurate policies to tackle rural poverty and hunger.
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