Country Level Analysis

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, you can view and interact with global MPI data for each country covered in the MPI database at the subnational level.

To explore Global Comparisons, please follow this link. Alternatively, you can click here to download Country Briefings.

Summary Table: MPI, H, and A

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.

SUB-NATIONAL AGGREGATES: MPI, H, and A, Vulnerability, and Severe Poverty

In addition to providing data on multidimensional poverty at the national and urban-rural level (as shown in Global Comparisons), the MPI can also be computed by subnational regions to show disparities in poverty within countries. Subnational disaggregations are published when the survey used for the global MPI is representative at the subnational level.
The next visulations shows a summary of the global MPI statistics by subnational region. The last column of the table also presents the population share of each region. The population share was obtained by applying the sampling weight in the respective survey dataset to the final sample used for the computation of the reported poverty statistics in this country profile. The population-weighted regional figures on MPI , H and A add up to the national figures.


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 sub-national level. Poverty information, however, becomes even more valuable when it is disaggregated by sub-national regions. It also allows the breakdown of indicators at the subnational levels of the country of choice. This analysis shows the headcount ratios of different indicators of MPI at different levels, which can reveal structural and spatial differences in 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 poverty of each sub-national region. 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.


Recall that the intensity of poverty (A) is the average proportion of weighted indicators in which poor people are deprived. A person who is deprived in 90% of the weighted indicators has a greater intensity of deprivation than someone deprived in 40% of the weighted indicators. The following pie chart shows the percentage of MPI poor people who experience different intensities of deprivation. For example, the first slice of the pie chart shows deprivation intensities of greater than 33.33% but strictly less than 40%.


The following visualisation illustrates the breakdown by indicators by country, and urban and rural areas. This analysis shows the contribution of different indicators to poverty in different areas, which can reveal structural differences in urban and rural poverty. This in turn could mean different policy responses in different areas, making the MPI useful for monitoring the effects of policy shifts and program changes.