Global Comparisons

The global Multidimensional Poverty Index (MPI) was created using the multidimensional measurement method of Alkire and Foster (AF). The global MPI is an index of acute multidimensional poverty that covers over 100 countries. It is computed using data from the most recent Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Pan Arab Project for Family Health (PAPFAM) and national surveys. The MPI has three dimensions and 10 indicators. Each dimension is equally weighted, and each indicator within a dimension is also equally weighted. Any person who fails to meet the deprivation cutoff is identified as deprived in that indicator. So the core information the MPI uses is the profile of deprivations each person experiences.

On this page, data can be browsed at the national level and on the Urban/Rural level where disaggregations are available.

To explore data of a specific nation and their sub-national levels, please visit the page of Country Level Analysis .

AGGREGATE MEASURES: MPI, H, and A, Vulnerability, and Severe Poverty

In the global MPI, a person is identified as multidimensionally poor or MPI poor if they are deprived in at least one third of the weighted MPI indicators. In other words, a person is MPI poor if the person’s weighted deprivation score is equal to or higher than the poverty cutoff of 33.33%. Following the AF methodology, the MPI is calculated by multiplying the incidence of poverty (H) and the average intensity of poverty (A). More specifically, H is the proportion of the population that is multidimensionally poor, while A is the average proportion of dimensions in which poor people are deprived. So, MPI = H × A, reflecting both the share of people in poverty and the degree to which they are deprived.

A headcount ratio is also estimated for two other ranges of poverty cutoffs. A person is identified as vulnerable to poverty if they are deprived in 20–33.33% of the weighted indicators. Concurrently, a person is identified as living in severe poverty if they are deprived in 50–100% of the weighted indicators. A summary of the global MPI statistics are presented in table 1 for national, rural and urban areas.
A brief methodological note is published following each round of global MPI update. For example, for the global MPI September 2018 update, please refer to Alkire et al. (2018). The note explains the methodological adjustments that were made while revising and standardizing indicators for over 100 countries.

Headcount of Poverty: MPI and $1.90/day

The following visualisation shows the percentage of people who are MPI poor and severely poor in the countries analyzed. The percentage of people who are MPI poor is shown in beige. The height at each dot denotes the percentage of people who are monetary poor according to the $1.90 a day poverty line in each country. The monetary poverty statistics are taken from the year closest to the year of the survey used to calculate the MPI. In cases where a survey was conducted over two calendar years, the later period is taken as the reference year.


The AF methodology has a property that makes the global MPI even more useful: dimensional breakdown. This property makes it possible to compute the percentage of the population who are multidimensionally poor and simultaneously deprived in each indicator. This is known as the censored headcount ratio of an indicator. The following visualisation shows the censored headcount ratio of each indicator at the national level. Poverty information, however, becomes even more valuable when it is disaggregated by urban and rural areas. It also allows the breakdown of indicators by country, and urban and rural areas. This analysis shows the headcount ratios of different indicators of MPI at different levels, which can reveal structural differences in urban and rural poverty.


The censored headcount ratio shows the extent of deprivations among the poor but does not reflect the relative value of the indicators to overall poverty. Two indicators may have the same censored headcount ratios but different contributions to overall poverty, because the contribution depends both on the censored headcount ratio and on the weight assigned to each indicator. As such, a complementary analysis to the censored headcount ratio is the percentage contribution of each indicator to overall multidimensional poverty.

The next visualisation allows two options: absolute contribution and percentage contribution of each indicator to national, rural and urban poverty. For percentage contributions, colors inside each bar denote the relative contribution of each indicator to the overall MPI, and all bars add up to 100%. For absolute contributions, the height of each bar adds up to the value of MPI. This enables an immediate visual comparison of the composition of poverty across areas.