National Multidimensional Poverty Index (MPI)
Information
Full name
Population living in multidimensional poverty according to national definitions using the Multidimensional Poverty Index
Short name
National Multidimensional Poverty Index (MPI)
Code
MPINational
Definition
The MPI uses the Alkire-Foster (AF) method of multidimensional measurement developed by Sabina Alkire and James Foster at the University of Oxford, which ordinarily takes the individual or household as the starting point and counts the different types of deprivations experienced, capturing both the percentage of people who are poor (headcount ratio or incidence of poverty) and the percentage of deprivations that poor people face (intensity of poverty) (Alkire & Foster, 2011a). It is a flexible approach that can be tailored to different contexts and disaggregated according to different subgroups.
Countries’ national MPIs are released as official permanent poverty statistics. National MPIs are tailored to their national priorities, so countries choose their own set of dimensions, indicators, weights and cut-offs, according to their national priorities, plans and contexts. National MPIs are disaggregated by region, urban/rural areas, age and other factors in order to leave no one behind.
Source: UNDP / Oxford Poverty and Human Development Initiative (OPHI), Handbook: How to Build a National Multidimensional Poverty Index (MPI), 2019, Available from: https://ophi.org.uk/sites/default/files/2023-08/How_to_Build_Handbook_2019_PDF.pdf
Rationale
Poverty has traditionally been defined as the lack of money. However, the poor themselves consider their experience of poverty much more broadly. A person who is poor can suffer multiple disadvantages at the same time – for example, they may have poor health or malnutrition, a lack of clean water or electricity, poor quality of work or little schooling. Focusing on one factor alone, such as income, is not enough to capture the true reality of poverty. Therefore, multidimensional poverty measures have been developed to create a more comprehensive picture by looking at multiple dimensions such as health, education, living standards.
Source: UN SDG indicator metadata, version 2025 March 28, available at: https://unstats.un.org/sdgs/metadata/
Method of computation
See full details on how to construct the index on the Oxford Poverty and Human Development Initiative (OPHI) website, available from: https://ophi.org.uk/national-mpi/designing-mpi. [assessed June 2026]
Comparability
International definition and standards with contextualization
Relationships
| Framework | Indicator | Comparability |
|---|---|---|
| Sustainible Development Goals Indicators | Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions | Comparable |
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Limitations
The MPI has some drawbacks, mainly due to data constraints. The indicators include both outputs (such as years of schooling) and inputs (such as cooking fuel). Data on outcomes like people’s functionings and capabilities are not available for all dimensions.
There are many aspects of health that the two included indicators do not cover. Furthermore, the nutrition indicator is the least comparable of all 10 indicators, because in countries with MICS surveys, data are only available for children under 5 years of age, whereas in DHS surveys women’s nutritional data are usually available and sometimes some data for men. National surveys may cover a larger age range. Despite these limitations, the patterns that emerge are plausible and familiar.
In some cases, careful judgments are needed to address missing data. To be considered multidimensionally poor, households must be deprived in at least six living standard indicators or in three living standard indicators and one health or education indicator. This requirement makes the MPI less sensitive to minor inaccuracies.
Source: Oxford Poverty and Human Development Initiative (OPHI) website, available at: https://ophi.org.uk/national-mpi-and-policy. [assessed June 2026]
Quality assurance
Quality assurance follows the practices of the standardized survey series or data source from which the data is collected.
Data sources
The preferred data source for this series is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of robust household survey with an appropriate employment module may also be used to obtain the required data. When official sources do not include forcibly displaced or stateless people or they cannot be disaggregated in the dataset, other potential data sources include UNHCR standardized surveys such as the Forced Displacement Survey.
Panama: Census - 2023
GOVT_PAN_2023_CENSUS
2023
Panama
Census or national survey
Gov't. of Panama
Pakistan: Forced displacement survey (FDS) - 2024
UNHCR_PAK_2024_FDS
2025
Pakistan
Probability survey
United Nations High Commissioner for Refugees