Global MPI Frequently Asked Questions

This FAQ section addresses general questions about the global MPI as an Index. For year-specific information please visit each year's release.

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  • Introduction

    What is the global MPI?

    The global MPI is an internationally comparable measure of acute multidimensional poverty in over 100 countries. It complements traditional monetary poverty measures, such as the World Bank’s extreme poverty line, by capturing the acute deprivations in health, education, and living standards that a person experiences simultaneously.

    The global MPI covers some 6 billion people living in over 100 countries across the developing regions of the world. This represents over 90 percent of the population in lower- and middle-income countries and over three-quarters of the world’s population.

    On this website, the global MPI results are accessible through Excel data tables.  These data tables include results at the country level, by age group, rural/urban area, subnational regions, and gender of the household head using the most recent survey. As a measure of robustness, we also publish results for multiple poverty-cutoffs. 

    We also publish poverty trends over time for over 80 countries. Finally, auxiliary statistics such as standard errors and sample sizes are part of all our data tables.

    The global MPI results are presented in country-specific briefings for all the countries covered. The results are visualised through an interactive databank where users can view the data through different visualisations. Our MPI Methodological Notes provide the methodology and technical decisions for calculating the global MPI results namely national, age groups, rural and urban areas, subnational regions, and gender of household head.

    The global MPI is produced annually by OPHI in the University of Oxford in collaboration with United Nations Development Programme’s Human Development Report Office (UNDP HDRO). However, the poverty trends over time are exclusively produced by OPHI and are licensed under the Oxford Academic license. 

    The global MPI was originally co-designed and launched in 2010 by both institutions and was jointly revised in 2018 to align insofar as is possible with the Sustainable Development Goals as well as with recommendations of the World Bank’s Atkinson Commission on Monitoring Global Poverty.

    How often is the global MPI updated?

    The global MPI is updated once a year to include the newly available datasets. 

    How was the global MPI created?

    The global MPI was developed by OPHI’s Alkire and Santos in 2010 in collaboration with the United Nations Development Program’s Human Development Report Office (HDRO) – see Alkire and Santos 2010 and Alkire and Santos 2014. It was first published in HDRO’s 20th anniversary flagship report in 2010 to replace the Human Poverty Index for the Human Development Report of UNDP. In 2018, the first major revision of the global MPI since its inception was undertaken, in order to take into account improvements in survey microdata, and to better align with the 2030 development agenda – see Alkire and Jahan (2018); Alkire, Kanagaratnam and Suppa (2018); Alkire and Kanagaratnam (2021); Alkire, Kanagaratnam, Nogales and Suppa (2022) and Vollmer and Alkire (2022). The global MPI is a leading practical application of the Alkire–Foster method pioneered by Sabina Alkire and James Foster.

    How is the global MPI aligned with the Sustainable Development Goals (SDGs)?

    The global MPI is aligned with the SDGs in several important respects: 

    • SDG 1: The preamble to the 2030 Agenda for Sustainable Development, which defined the SDGs, states that ‘eradicating poverty in all its forms and dimensions… is the greatest global challenge and an indispensable requirement for sustainable development.’ SDG Target 1.2 calls for countries to halve the proportion of men, women, and children living in poverty in all its dimensions according to national definitions. Poverty is understood to be both multidimensional and measurable. National MPIs developed by countries to reflect their particular context, and the global MPI, both assess progress in poverty reduction: one with respect to national priorities and the other from a comparative perspective.
    • Leave no one behind: The 2030 Agenda pledges that ‘no one will be left behind’. The global MPI considers the depth or intensity of an individual’s poverty, going beyond the overall number of poor people (headcount ratio) and providing measurement incentives to reduce the deprivations of the poorest – even if they do not yet exit poverty. Disaggregation of the MPI by age group, rural/urban area, subnational region and female/male headship identifies specific pockets of poverty. The 2021 global MPI report included results that disaggregated by the ethnicity and caste of household head. Disaggregation enables more targeted policies and actions and helps ensure that particular areas and groups are not left behind. In analysing MPI trends, it is important to note if the poorest groups made the fastest progress – because if they progressed slowly, they are being left behind.
    • Interlinkages: Rather than viewing challenges in silos, the MPI shows how deprivations related to the following SDGs are interlinked in the lives of poor people: No Poverty (SDG 1), Zero Hunger (SDG 2), Health & Well-being (SDG 3), Quality Education (SDG 4), Clean Water & Sanitation (SDG 6), Affordable & Clean Energy (SDG 7), Sustainable Cities & Communities (SDG 11). The MPI brings many concerns together into one headline measure. Given that people are MPI poor if they are deprived in one third or more of the weighted indicators, the MPI focuses on people who are being left behind in multiple SDGs at the same time. The 2022 global MPI report probed interlinkages further with the first analysis of the common deprivation bundles within the global MPI to see whether and how some patterns of poverty are more frequent than others to inform potential multisectoral policies to reduce poverty efficiently.

    What can the global MPI be used for?

    The global MPI measures acute multidimensional data using a comparable methodology for over 100 countries. For global poverty data across developing regions, it is the multidimensional complement of the World Bank’s $2.15-a-day measure.

    The global MPI dataset has many potential applications particularly for researchers; INGOs and international agencies; regional, national and local governments; and journalists. These include:

    • Seeing where the poorest people live – by country, by rural/urban areas, by subnational regions, by age cohorts, and by gender of the household head – from a multidimensional perspective to cross-check whether programmes are reaching the poorest.
    • Using the global MPI to report multidimensional poverty levels for SDG Indicator 1.2.2. in the Global SDG Indicators Database in cases where national governments do not yet have their own tailored national MPI, or where they have officially adopted the global MPI as their national MPI (e.g. Nepal).
    • Analysing the composition of poverty by indicator, both at the national level and disaggregated by age, rural/urban, subnational, female/male headship, and occasionally by other variables such as ethnicity or those living with disabilities.
    • Comparing absolute and relative trends (and their significance) over time of the MPI, incidence, intensity, and indicators of MPI for countries, and comparing absolute and relative trends for all of these variables disaggregated by subnational regions, rural-urban areas, and age groups.
  • How can I use and replicate the global MPI Database?

    What do we mean by Database? 

    The Global Multidimensional Poverty Index (MPI): Harmonised Level Estimates and their Changes over Time database (“the Database”) are open access under the Oxford Academic Licence. 

    You have the permission to use and replicate (see replication steps below) the results presented in the Database in any medium, provided the source and authors are credited and not-for-profit use such as non-commercial research, education purposes or other scholarly use. 

    However, to republish and distribute all or any portion of the Database, please email us for permission at

    The Copyright Notice “©2018 University of Oxford” and citation must appear prominently wherever results from the Database are used such as in Tables and Figures; or when the Database is described in any research publication or on any documents or other material created.

    If you are interested in using the Database commercially, please contact Oxford University Innovation Limited to negotiate a licence. Contact details are quoting reference OPHI.

    How can I replicate the Database?

    The Database published in excel data tables are subject to an Oxford Academic licence. You may replicate the Database in these three general steps. 

    Step 1: Download survey microdata

    The underlying data that was used to estimate the Database was produced by DHS, MICS, PAPFAM and national surveys and as such the underlying data is subject to the survey provider’s licence or terms of use. If you want to download the underlying data and use it, e.g., to replicate the Database, your use would be subject to the terms of the survey provider’s licence or terms of use. 

    To access the DHS and MICS microdata, you must obtain permission from the survey providers through their website. Access to the microdata by PAPFAM and national offices are restricted. We have indicated in our Stata do-files how you may access the national surveys. OPHI will not facilitate or assist in any request for microdata. 

    We will always indicate the original source of the underlying microdata in our documentation, so you should always check the license of any such survey provider before replication of the Database. 

    Step 2: OPHI’s script files (Stata do-files)

    Our data preparation do-files allow you to generate the harmonised global MPI indicators from the microdata. Microdata are household surveys produced by specific survey providers. The do-files used for the purpose of replicating the Database are open access under the Oxford Academic Licence.

    Step 3: MPI estimation using -mpitb-

    Once you have produced the cleaned microdata using our data preparation do-files, the next step is to estimate the global MPI and its associate statistics following the Alkire-Foster method. This estimation may be carried out using the MPI toolbox (-mpitb-), a user-written open access Stata package (it is distributed under the MIT licence). -mpitb- is authored by Dr Nicolai Suppa and available at the Statistical Software Component (SSC) and under

  • Methodology

    What are the main limitations of the global MPI?

    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 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.

    The estimates presented for the 2023 global MPI are based on publicly available data and cover various years between 2011 and 2021/22, which limits direct cross-country comparability.

    Why are these indicators used in the global MPI? Why not indicators for environment, employment, or empowerment for instance?

    To create the global MPI an initial list of dimensions and indicators was prepared following a long process of consultation. This list was then compared against available data for most countries included in the study, and we found that there were some data constraints. To build a meaningful index we are limited to data readily available in most surveys.

    Environment: The Human Development Report in 2020 released research probing the interrelationship between climate change, the environment and poverty and continues to explore the technical challenges that this raises.

    Employment: The data sources used, unfortunately, are not able to identify unemployed or underemployed persons, nor unsafe work or informal work. Given its importance, many national MPIs add this dimension.

    Empowerment: The DHS surveys collect data on women’s empowerment for some countries, but not every DHS survey includes empowerment, and the other surveys do not have these data. Data on men’s empowerment or political freedom are missing.

    Why does national data for the MPI date from so many different years? Is it unfair to compare countries if the statistics in one case are five years older than in another?

    The 2023 MPI relies on the most recent and reliable data available since 2011. As in the case of all poverty measures including income and social statistics, surveys are taken in different years, and some countries do not have recent data. In order to facilitate clear analysis, the year of the survey is reported in the MPI tables. The difference in dates limits direct cross-country comparisons, as circumstances may have improved, or deteriorated, in the intervening years. Naturally, this is a stimulus for country governments to collect up-to-date surveys that reflect recent progress. The SDGs’ focus on data should, we hope, give rise to more frequent data for MPI estimations.

    The 2023 report issues a call to action to conduct frequent and up-to-date household surveys in order to measure poverty and launch strategic tools to eliminate abject poverty, even as new challenges arise. Recent data are vital for planning, designing policies, and incentivizing and recognizing change. Regular multitopic household surveys, while not perfect, are the best instrument for estimating multidimensional poverty. But advances in data collection have not improved the frequency or breadth of these surveys.

    What are the main limitations to measuring MPI trends over time?

    Analysis of trends in multidimensional poverty rely on harmonised versions of the global MPI. As such, all the limitations of the global MPI, also apply to our work on trends over time.

    What are the main technical limitations to analysing MPI by gender of the household head?

    Across all surveys, gender is a binary variable (male or female), and household head is a self-reported category. Household members typically acknowledge the household head on the basis of age (older), gender (male) or economic status (main provider). The analysis provides a global account of multidimensional poverty by headship but is constrained by the mixed definition of headship used in the surveys.

  • Findings and Report

    What is new in the global MPI 2023 compared to 2022?

    The global MPI 2023 covers 110 countries. Of these, six have updated surveys. The countries with updated estimates are Cambodia, Madagascar, Mexico, Mozambique, Nigeria and Peru. The new countries with estimates are Fiji and Uzbekistan.

    The 2023 global MPI retains the updates made in 2018, which changed five of the ten indicators from the original MPI: nutrition, child mortality, years of schooling, housing and assets to respond to the priorities of the Sustainable Development Goals (SDGs). For more info on the 2018 revision, please see the MPI Methodological Note 46OPHI Working Paper 121 and OPHI Research in Progress 56a.

    In terms of the 2023 global MPI trends over time results, we published in Table 6 harmonised estimates for 84 countries. Forty-five of the 84 countries have trends for two points in time, 35 countries have trends for three points in time, while four countries have trends for four points in time.

    This year’s global Multidimensional Poverty Index 2023 report Unstacking global poverty: data for high impact action presents a compact update on the state of multidimensional poverty in the world. It tells an important and persistent story about how prevalent poverty is in the world and provides insights into the lives of poor people, their deprivations and how intense their poverty is—to inform and accelerate efforts to end poverty in all its forms. As still only a few countries have data from after the COVID-19 pandemic, the report urgently calls for updated multidimensional poverty data. While providing a sobering annual stock take of global poverty, the report also highlights examples of success in every region.

    What are the data sources used in the global MPI 2023?

    The global MPI is updated when new data become available from the following sources: Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and national surveys. We also explore whether there are new national surveys in the public domain that have indicators comparable to those included in the global MPI. National surveys are considered in the absence of surveys produced by DHS and MICS, or if DHS and MICS data are more than three years older than the national surveys.

    The 2023 global MPI covering 110 countries relies on using 43 Demographic and Health Surveys (DHS), 54 Multiple Indicator Cluster Surveys (MICS), and 13 national surveys. The estimates produced for the 110 countries included in the global MPI 2023 are built on the most recent data available for each country. The estimates use the latest survey data available from 2011–2021/2022. A total of 87 countries—home to 85.4 percent of multidimensionally poor people—have data fielded in 2016 or later and 41 of those countries have data fielded in 2019 or later.

    The 84 global MPI countries with trends over time have data that span from 2000–2020. In 27 countries, we harmonised DHS data sets across all time points, and used only MICS data sets in 22 countries. For four countries (China, Ecuador, Mexico and Morocco) the harmonisation work is exclusively based on national data sets. For Bolivia and Peru, the harmonised datasets include a combination of DHS surveys and national surveys. For 29 countries we have used a mix of DHS and MICS across time points.

    All population aggregates (e.g. numbers of people living in multidimensional poverty) which are presented in the data tables and the 2023 global MPI report use 2021 population data from the World Population Prospects 2022 by the Population Division of the United Nations’ Department of Economic and Social Affairs (UNDESA). Data Tables available online also provide the results using population data for the year of the survey.

    Why were these 110 countries selected for the 2023 global MPI?

    The 110 countries in this year’s global MPI have been selected on the basis that they all have internationally comparable survey data, they have at least one indicator in both the health and the education dimension, and they are nationally representative.

    Of the 110 countries included in global MPI 2023, data for 102 were published in earlier rounds of the global MPI. We continue to include these surveys in the global MPI 2023 because we consider surveys released in the last decade, that is, since 2013. In the 2023 round, new or updated survey data were available for 8 countries.

    Is it possible to compare the global MPI 2023 to previous years?

    The multidimensional poverty trends results, as published in OPHI’s Data Table 6, harmonise the indicators to allow for comparability over time. We would caution against using the published numbers of the global MPI for strict comparisons because the indicators may vary between two years. Data Table 6 permits strict comparisons.

    Why does the report feature 81 countries for trends over time while Data Table 6 on the OPHI website has 84 countries?

    The harmonised over time estimates of the 2023 global MPI cover 84 countries. Data Table 6 on OPHI's website covers all 84 countries. The 2023 global MPI report published in partnership with UNDP’s Human Development Report Office profiled 81 countries. The countries excluded from the joint publication but included in OPHI Data Tables are Afghanistan, Trinidad and Tobago, and Yemen. These countries are excluded from the joint publication because of data constraints in health indicators over the time periods. The complexity of the harmonised estimates of these three countries is detailed in MPI Methodological Note 50.

  • Income vs MPI

    Why are there wide discrepancies between MPI poverty estimates and extreme poverty line estimates in many countries?

    The fact that there are discrepancies between MPI poverty estimates and $2.15/day poverty estimates is one of the reasons why we compute the global MPI. The World Bank updated the $1.90 a day to $2.15/day in September 2022. Although there is a clear overall relationship between MPI and the extreme poverty line of $2.15, particularly in low poverty countries, the estimates differ for many countries. The MPI therefore complements monetary poverty measures, revealing poverty that would otherwise be overlooked. It measures various deprivations directly.

    The mismatch between income and other deprivations is well-documented, including in Europe. Possible explanations include infrastructure; public services such as health, education, water, power, and transportation; market access, spending habits, household size and composition; pro-poorest or discriminatory local institutions; the presence of a large employment industry (e.g. a mine); remoteness, and so on.

    Why is the global MPI headcount ratio much higher than national monetary poverty estimates in some countries?

    The MPI, like the World Bank’s extreme poverty line, is a globally comparable measure of poverty. It measures acute multidimensional poverty, and only includes indicators that are available for many countries. National poverty measures are typically monetary measures, and thus capture something different. The fact that there are differences does not mean that the national monetary poverty number, or the MPI headcount ratio is wrong – these simply measure different conceptions of poverty.

    At the same time, just as national poverty measures are designed to reflect the national situation more accurately and often differ from the World Bank’s $2.15 a day measure, some countries may wish to build a national Multidimensional Poverty Index that is tailored to their context, to complement the global MPI.

    Is the global MPI intended to replace the standard $2.15 a day measure of poverty used for the SDGs and other international purposes?

    No, the global MPI is intended to complement monetary measures of poverty, including $2.15/day estimates. The relationship between these measures, as well as their policy implications and methodological improvement, are priorities for further research.

  • Policies and international adoption

    How can I find out more about a country’s multidimensional poverty?

    Country briefings for the 110 countries included in the global MPI 2023 are available which explain the global MPI results for each country.

    What are the policy implications of the global MPI results?

    The MPI shows where and how people are poor and trends show the rate of reduction or increase in multidimensional poverty. It helps policymakers analyse the interconnections among deprivations and identify priority areas for interventions, enabling policymakers to target resources and design policies more effectively. The global MPI estimates act as a springboard for countries, who go on to develop national MPIs using the same methodology. The indicators and weights in national MPIs are tailored to the national context to identify key policy priorities. Some countries, such as Nepal, use the global MPI directly as their national MPI. For more information on uses please see the OPHI-UNDP book ‘How to Build a National Multidimensional Poverty Index (MPI): Using the MPI to inform the SDGs’.

    Can the global MPI be adopted for national poverty eradication programmes?

    The global MPI was devised as an analytical tool to compare acute poverty across nations. The MPI indicators and dimensions can be modified to generate national MPIs that reflect local cultural, economic, political, climatic and other factors. An MPI will immediately reflect changes in deprivations in any of its indicators, such as school attendance, so can be used to monitor progress.

    Countries select data sources and the most relevant indicators and weights that make sense in their context to create tailored national poverty measures. Over 40 governments have released official national MPIsColombia is a powerful example of how the MPI can be used to coordinate national poverty eradication programmes. Costa Rica shows how it is used for budgeting and Bhutan shows how it can be used for targeting.

    How does the MPI respond to the effects of shocks?

    The survey data used to estimate the global measure are usually collected every three to five years. The MPI will reflect the impacts of shocks as far as these, for example, cause children to leave primary education, or become malnourished, or experience deteriorated housing conditions or services etc in the next survey. For example, if a flood occurred between two survey periods and many people lost their homes and are still living in substandard housing, this will be captured by the global MPI in the next survey. If more frequent data are available at the country or local level, this can be used to capture the effects of larger scale economic and other shocks. However, there are other ways the MPI is being used to provide information on shocks, particularly in light of the COVID-19 pandemic.

    First, if certain deprivations in the global MPI signal high vulnerability (for example, people who are undernourished, lack clean water, and are at risk of acute respiratory infections due to solid cooking fuel), then additional elementary analysis can identify which people have one, two, or all three of these deprivations (see OPHI Briefing 53a and OPHI Briefing 54a).

    Second, there may be additional indicators in the survey that can be linked to the risk, so disaggregating by this information (data permitting) or adding it in as an additional indicator or dimension to the MPI will clarify risks from the shock. For example, in the case of COVID-19, one might incorporate a dimension for each household that also includes data on handwashing, overcrowding, or the presence of older person(s) in the household, or underlying health conditions such as diabetes and heart conditions, or domestic violence.

    Third, it is possible to explore different scenarios by randomly assigning additional deprivations to some identified vulnerable groups and using these microsimulations to re-assess the MPI. For example, if undernutrition among poor and vulnerable people rose by 25%, what would be the effect on multidimensional poverty? Simulations in the 2020 edition of the global MPI report suggested that the pandemic set progress in reducing MPI values back by 3–10 years; emerging post pandemic data indicate that the worst of these scenarios may become a reality. Please also see: Social Science and Medicine vol. 291 (December 2021), paper No. 114457.

    The global MPI covers more than 100 developing countries. Will an MPI be created for developed countries?

    The constraint is not the methodology, which can be easily extended to reflect different thresholds and aspects of multidimensional poverty, but rather the data.

    There are no publicly available equivalently comparable data across high-income countries. The list of the 110 countries that the global MPI 2023 covers and country-specific summaries are available on the Country briefings page and through the MPI data tables page.

    For examples of discussions of MPI in developed countries see the following discussions for Germany and the United States.