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Multidimensional Poverty Index

Also known as: MPI, Alkire-Foster Method, Adjusted Headcount Ratio, Dual-Cutoff Multidimensional Poverty

OriginatorSabina Alkire & James FosterYear2011Sources1Related methods12

The Multidimensional Poverty Index applies the Alkire-Foster method, introduced by Sabina Alkire and James Foster in 2011, to measure poverty as the joint deprivation of individuals across several dimensions such as health, education, and living standards. Its signature is a dual-cutoff identification: a person is deprived in an indicator if they fall below that indicator's cutoff, and they are counted as multidimensionally poor only if their weighted count of deprivations crosses a cross-dimensional cutoff k. The headline measure is the adjusted headcount ratio M0 = H times A, the product of the share of people who are poor (incidence) and the average breadth of their deprivations (intensity).

Key highlights

  • Captures the joint distribution of deprivations across dimensions, which separate single-dimension indices and dashboards miss.
  • The adjusted headcount M0 satisfies dimensional monotonicity, falling when any poor person escapes a deprivation, unlike the raw multidimensional headcount.
  • Decomposable by population subgroup and breakable down by indicator, enabling targeted policy and clear poverty profiles.
  • Works with ordinal data (the common case for health and education indicators), where many alternative measures require cardinality.

Intuition

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How it works

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When to use it

Use the Alkire-Foster MPI when poverty is genuinely multidimensional and you have individual- or household-level data on several deprivation indicators with defensible cutoffs and weights. It is the standard tool behind the global MPI published by UNDP and OPHI and behind many national multidimensional poverty measures, and it is appropriate whenever income data alone would misrepresent deprivation. Report incidence H, intensity A, and the adjusted headcount M0 together, and use the indicator decomposition to identify which deprivations drive poverty in each group. The method requires explicit normative choices — which dimensions, what cutoffs, what weights, and the poverty cutoff k — so transparency and robustness checks over these choices are essential. When indicators are cardinal, the related M1 and M2 measures add depth and severity.

Strengths & limitations

Strengths
  • Captures the joint distribution of deprivations across dimensions, which separate single-dimension indices and dashboards miss.
  • The adjusted headcount M0 satisfies dimensional monotonicity, falling when any poor person escapes a deprivation, unlike the raw multidimensional headcount.
  • Decomposable by population subgroup and breakable down by indicator, enabling targeted policy and clear poverty profiles.
  • Works with ordinal data (the common case for health and education indicators), where many alternative measures require cardinality.
Limitations
  • Requires explicit, contestable normative choices — dimensions, indicator cutoffs, weights, and the cross-dimensional cutoff k — that drive the results.
  • The headline M0 uses only binary deprivation status, discarding information on how far below each indicator cutoff a person falls (depth within indicators).
  • Equal or expert-set weights are hard to justify and the index is sensitive to them, especially for the identification cutoff k.
  • Comparability across countries and over time is fragile when indicators, cutoffs, or data sources differ.

Common pitfalls

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Applications

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Frequently asked

What are the two cutoffs in the dual-cutoff method?

The first cutoff is within each indicator: a person is deprived in indicator j if their achievement falls below that indicator's threshold z_j (for example, no household member completed primary school). The second cutoff is across dimensions: a person counts as multidimensionally poor only if their weighted deprivation score reaches the poverty cutoff k (for example, deprived in at least one third of weighted indicators). The first defines deprivation indicator by indicator; the second defines who is poor overall, ensuring the measure reflects joint deprivation.

Why use the adjusted headcount M0 instead of the simple headcount H?

The simple multidimensional headcount H counts who is poor but is insensitive to how broadly each poor person is deprived — if a poor person escapes one deprivation but remains above the cutoff k, H does not move. The adjusted headcount M0 = H times A multiplies incidence by intensity, so it falls whenever any poor person reduces their deprivations. This dimensional monotonicity is the key axiomatic advantage and the reason M0, not H, is the headline measure of the Alkire-Foster method.

How are the weights and the poverty cutoff k chosen?

Both are normative choices made by the analyst or institution. The global MPI, for example, uses three equally weighted dimensions (health, education, living standards) with equal weights within each, and sets k at one third of weighted indicators. These choices are defensible but not unique, and the index can be sensitive to them, so good practice is to justify the choices transparently and report robustness over alternative weights and a range of k values rather than presenting a single configuration as definitive.

Sources

  1. 1.
    Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487.

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ScholarGate. (2026, June 22). Multidimensional Poverty Index. ScholarGate. https://scholargate.app/economics/multidimensional-poverty-index