Global MPI 2020 Frequently Asked Questions
- What is the global MPI?
- What is Changes over Time research, also referred to as trends in poverty reduction?
- How often is the global MPI updated?
- How was the global MPI created?
- How is the global MPI aligned with the Sustainable Development Goals (SDGs)?
- What is new in the global MPI 2020 compared to 2019?
- What are the data sources used in the global MPI 2020?
- Why were these 107 countries selected for the 2020 global MPI?
- Is it possible to compare the global MPI 2020 to previous years?
- Why were these 80 countries selected for the study in Changes over Time?
- Why does the report only feature 75 countries while the OPHI tables and publications have 80 countries?
- What does the global MPI measure?
- What makes a household or person ‘multidimensionally’ poor?
- What do your figures for ‘population vulnerable to poverty’ and ‘population in severe poverty’ mean?
- What is OPHI’s global destitution measure?
- The global MPI is described as a measure of acute poverty. How does this differ from extreme poverty?
- Why is income not included?
- How do I interpret the various values presented with the global MPI results?
- Where can I find out more about how to apply the MPI approach?
- How have the Changes over Time trends been calculated?
- Is it possible to compare (the harmonised) trends across countries?
- Why are the data used to calculate trends for my country from further back in time than the data used in the 2020 global MPI estimates?
- How have the projections on how many countries are on or off track to halve multidimensional poverty by 2030 been calculated?
- How have the simulations of COVID-19 impact been calculated?
- Why are there wide discrepancies between MPI poverty estimates and $1.90/day poverty estimates in many countries?
- Why is the global MPI headcount ratio much higher than national monetary poverty estimates in some countries?
- Is the global MPI intended to replace the standard $1.90 a day measure of poverty used for the SDGs and other international purposes?
- How can I find out more about my country’s multidimensional poverty?
- What are the policy implications of the global MPI and Changes over Time data?
- Can the MPI be adopted for national poverty eradication programmes?
- How does the MPI respond to the effects of shocks?
- The global MPI covers more than 100 developing countries. Will an MPI be created for developed countries?
- What are the main limitations of the global MPI?
- Why are these indicators used in the global MPI? Why not indicators for environment, employment, ethnicity or empowerment for instance?
- 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?
- What are the main limitations to measuring MPI trends over Time?
Global MPI – an introduction
What is the global MPI?
The global MPI is a measure of acute multidimensional poverty in over 100 countries. It complements traditional monetary poverty measures by capturing the acute deprivations in health, education, and living standards that a person experiences simultaneously.
In 2020 the global MPI covers 5.9 billion people, representing over 90 percent of the population in lower- and middle-income countries and over three-quarters of the world’s population.
Originally co-designed and launched in 2010 by the United Nations Development Programme’s (UNDP) Human Development Report Office (HDRO) and the Oxford Poverty and Human Development Initiative (OPHI), the global MPI was jointly revised by both institutions in 2018. The revised global MPI is aligned 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. The MPI and its associated information platform is produced annually by OPHI in collaboration with UNDP’s HDRO.
What is Changes over Time research, also referred to as trends in poverty reduction?
The global MPI compares results across countries by constructing indicators from different datasets to capture poverty at a single point in time. It is also important to analyse how poverty changes over time in a country and across the world, but two datasets for the same country may differ in different periods.
In 2020 we release the first global study of harmonised trends in multidimensional poverty. The analysis tracks changes between harmonised versions of the global MPI, denoted by the subscript MPIT. Indicator definitions are rigorously harmonized to match between time periods. For example, if one survey collected only child nutrition rather than adult nutrition, data for the other survey are restricted to child nutrition as well. If one survey can be disaggregated by 10 subnational regions and the other by 30 (that map onto the other 10), both are disaggregated by 10. This alteration of the original global MPI structure for comparability means that the figures presented might differ from those published in the global MPI 2020 or previous global MPIs. But they can be rigorously compared with each other.
The 2019 global MPI report included a preliminary analysis of trends for ten countries. In 2020 the results span 80 countries covering 5 billion people (including the ten countries named in the 2019 report).
For a detailed description of the methodology and country-specific harmonisation decisions, see MPI Methodological Note 50, and for results for all 80 countries, see Table 6 (2020). An analysis of the results are published in Alkire, Kovesdi et al 2020 (Research in Progress 57a), and the 2020 global MPI report. For previous projects exploring Changes over Time (which uses the pre-2018 global MPI), see Alkire, Roche and Vaz 2017 and Alkire, Jindra, Robles and Vaz 2017.
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 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.
The global MPI is a leading practical application of the multidimensional poverty methodology 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. The official 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 region, age, and urban/rural area identifies specific pockets of poverty. This 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 of the weighted indicators, the MPI focuses on people who are being left behind in multiple SDGs at the same time.
Findings and report
What is new in the global MPI 2020 compared to 2019?
The global MPI 2020 covers 107 countries. Of these, 25 have new surveys: Bangladesh, Botswana, Democratic Republic of the Congo, Cuba, Gambia, Georgia, Guinea, Indonesia, Kiribati, Kyrgyzstan, Lesotho, Madagascar, Mali, Mongolia, Montenegro, Nigeria, Papua New Guinea, Peru, Seychelles, Sri Lanka, Suriname, Togo, Tunisia, Zambia and Zimbabwe.
The 2020 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).
What are the data sources used in the global MPI 2020?
The 2020 global MPI relies on using 47 Demographic and Health Surveys (DHS), 47 Multiple Indicator Cluster Surveys (MICS), 3 Pan Arab Project for Family Health (PAPFAM) and 10 national surveys that provide comparable information to DHS and MICS.
These household surveys are produced at different intervals in time in each country. The standard survey data used to estimate the global measure are collected every three to five years depending on the country. The global MPI is built on the most recent data available and incorporates the new surveys as they become available within the period of computation.
Surveys used for the 2020 MPI computation therefore range from 2008 to 2019. The vast majority of people covered (5.6 of the 5.9 billion, and 1.2 of the 1.3 billion multidimensionally poor people) are captured by surveys fielded in or after 2013.
All population aggregates (numbers of people living in multidimensional poverty) which are presented in the data tables and report use 2018 population data from the World Population Prospects (UNDESA, 2019), unless otherwise indicated. Data tables available online also provide the results using population data for the year of the survey.
Why were these 107 countries selected for the 2020 global MPI?
The 107 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 107 countries included in global MPI 2020, data for 82 were published in earlier rounds of the global MPI. We continue to include these surveys in the global MPI 2020 because we consider surveys released in the last decade, that is, since 2008. In the 2020 round, new or updated survey data were available for 25 countries. Six of these updated surveys were released by Demographic and Health Surveys (DHS), while 14 were released by Multiple Indicator Cluster Surveys (MICS). These surveys was released between May 2019 and March 2020. We also made use of 4 new national surveys that were made available by national statistics offices and one national survey that was available on the public domain. These surveys have indicators comparable to those included in the global MPI. National surveys are considered in the absence of surveys produced by DHS and MICS.
Is it possible to compare the global MPI 2020 to previous years?
We would caution against using the published numbers of the global MPI for strict comparisons. All global MPI estimates published before 2017 refer to the ‘original’ MPI structure, which was updated in 2018 with additional minor revisions in 2019. Comparisons of the global MPI estimates should account for these changes. In contrast, the Changes over Time analysis harmonises the indicators precisely to allow for comparability.
Why were these 80 countries selected for the study in Changes over Time?
For this first global study, countries had to have two datasets with comparable sampling frames, which were fielded with a minimum period of three years between surveys. Countries with no suitable datasets, or suitable data for only one time period were not included in the analysis. For more detail on the countries, data and time periods selected, see the MPI Methodological Note 50.
Why does the report only feature 75 countries while the OPHI tables and publications have 80 countries?
The analysis of Changes over Time has been conducted for 80 selected countries and Table 6 (under Data Tables 2020) and the MPI Methodological Note 50 cover all of these countries. The 2020 global MPI report published in partnership with UNDP’s Human Development Report Office, profiled 75 countries. To be included in the joint report the countries’ harmonised files must meet the following conditions: The absolute value of the relative difference in MPI or H is over 500%; The difference in headcount ratio between the standardized (published in Table 1 of OPHI and of this report) and harmonized MPI has an absolute value that is greater than 15%; The nutrition indicator is dropped, and the child mortality indicator does not include whether or not the death took place within the last five years, and the change between standardized and harmonized value is greater than the absolute value of 0.080 for MPI or 15 percentage points for the headcount ratio, or the relative change is greater than 75%.
For further details, please see the MPI Methodological Note 50.
What does the global MPI measure?
The global MPI is composed of three dimensions (health, education, and living standards) and ten indicators. Each dimension is equally weighted, and each indicator within a dimension is also equally weighted. A person is identified as multidimensionally poor if they are deprived in at least one third of the weighted indicators. The MPI identifies overlapping deprivations for each person. It shows the incidence of poor people in a population and the intensity of deprivations faced within poor households.
What makes a household or person ‘multidimensionally’ poor?
One deprivation alone may not represent poverty. The MPI requires a household to be deprived in multiple indicators at the same time. A person is multidimensionally poor if he or she is deprived in at least one third of the weighted indicators. This is termed acute multidimensional poverty.
A feature of the MPI is that by changing the cutoffs or thresholds of the measure we can analyse different poverty levels and disaggregate from there. The global MPI can therefore also tell us about ‘severe’ poverty (those deprived in half or more of the dimensions) and people who are not yet poor according to the standard cut off, but ‘vulnerable’ to poverty (deprived in 20-33% of the weighted indicators).
What do your figures for ‘population vulnerable to poverty’ and ‘population in severe poverty’ mean?
Since 2011, two additional categories of multidimensional poverty have been reported in the UNDP’s Human Development Report Tables. These are called the ‘population vulnerable to poverty’ and the ‘population in severe poverty’. The population vulnerable to poverty is defined as the percentage of the population at risk of suffering multiple deprivations—that is, those people with a deprivation score of 20–33 percent of the weighted indicators. The incidence of severe poverty, measures the percentage of the population with a deprivation score of 50 percent or more.
What is OPHI’s global destitution measure?
The global MPI destitution measure, described in this year’s Methodological Note, is a poverty measure introduced in 2014 by Alkire, Conconi and Seth. The destitution measure aims to assess the situation of the poorest of the poor within the multidimensional poverty framework.
The destitution measure has precisely the same dimensions, indicators, weights, and poverty cutoff as the global MPI. Only one set of parameters changes: the deprivation cutoffs. Those who are poor according to these deeper deprivation cutoffs are classified as ‘destitute’.
The global MPI is described as a measure of acute poverty. How does this differ from extreme poverty?
We have described the MPI as a measure of ‘acute’ poverty to avoid confusion with the World Bank’s measure of ‘extreme’ monetary poverty that captures those living on less than $1.90 a day.
The MPI reflects the acute deprivations that people face at the same time. Because it was designed to compare acute poverty across developing nations, it is most relevant to low and middle income countries.
Why is income not included?
Income is not included due to data constraints. Monetary poverty data derive from different surveys, and these surveys often do not have information on health and nutrition. For most countries we are not able to identify whether the same people are monetarily poor and also deprived in any of the MPI indicators. There may be additional technical considerations: to be valid in an MPI, the consumption aggregate or household must be a reliable estimate of that particular household’s monetary poverty over the same time period as the MPI indicators.
Nevertheless, monetary poverty is a fundamental perspective on poverty. We consider that the global MPI and the international measure of extreme monetary poverty – $1.90 / day – complement each other by bringing different aspects into view.
How do I interpret the various values presented with the global MPI results?
The MPI is always reported with a set of linked poverty measures that together make up a powerful information platform. These measures can be unpacked to show the composition of poverty both across countries, regions and the world and within countries by ethnic group, urban/rural location, age group, as well as other key household and community characteristics. This is why OPHI describes the MPI as a high-resolution lens on poverty: it can be used as an analytical tool to identify where and in what form poverty is greatest.
The MPI measures are explained below:
Incidence of poverty: the proportion (%) of the population who are multidimensionally poor (those who are deprived in at least one third of the weighted indicators). This is also sometimes referred to as the headcount ratio or the poverty rate.
Intensity of poverty: the average share (percentage) of deprivations across the ten weighted indicators which people experience simultaneously.
MPI: The MPI ‘value’, which ranges from zero to one, is calculated by multiplying the incidence of poverty by the intensity of poverty. It shows the proportion of deprivations that a countries’ poor people experience out of the total possible deprivations that would be experienced if every person in the society were poor and deprived in every indicator. The MPI therefore increases or decreases when either the incidence and/or the intensity of poverty changes.
Number of poor: The number of multidimensionally poor people in a country is important for budgeting and targeting, and reflects both demographic change and population growth. It is computed by multiplying the population of the country by the incidence of MPI.
Where can I find out more about how to apply the MPI approach?
Background materials that provide the technical guidance needed to apply and adapt the MPI approach are available from OPHI’s Online Training Portal. See also ‘How to Apply the Alkire Foster Method’–12 Steps to a Multidimensional Poverty Measure. Our website also advertises periodic short courses on multidimensional poverty – see our recent online course on ‘Designing a Multidimensional Poverty Index’.
How have the Changes over Time trends been calculated?
Trends are estimated using indicators in the global MPI that are adjusted so that precisely the same information is used in both years. This alteration of the original global MPI structure for comparability is called the ‘harmonisation’ process.
The global MPI applies a standard structure of the measure to all the datasets. Harmonization seeks to make two or more MPI estimations rigorously comparable by exactly aligning the indicator definitions across time. In other words, harmonisation, where necessary, adjusts the indicators in the global MPI so that they are using precisely the same information and deprivation cutoffs in both years. Strict harmonisation is necessary to ensure that any differences observed are due to changes in the conditions of poverty in the country rather than changes in the questionnaire.
A description of the harmonisation principles for each indicator and details of the estimation process are available in MPI Methodological Note 50.
Is it possible to compare (the harmonised) trends across countries?
Harmonised estimates can be rigorously compared for the same country. To compare the speed of reduction across countries, we refer to ‘annualised changes’, the rate of absolute or relative change divided by the number of years between the two time points under study for each country. In the case of split survey years (e.g. India 2005/06 and 2015/16), we take the average of the two years for each survey to calculate the annualized changes (in this case, 10 years).
While harmonisation ensures that indicators are comparable across the years for a particular country, it can result in slightly different versions of the indicators across countries. These differences must be considered when comparing harmonized trends across countries.
Why are the data used to calculate trends for my country from further back in time than the data used in the 2020 global MPI estimates?
The project estimating changes in the global MPI started after the launch of the 2018 global MPI and selected countries with two available and comparable datasets at the time. And while the MPI trends analysis has followed the data updates of the global MPI from 2018 to 2019, the list of datasets included in the analysis predates the updates of the 2020 round of the global MPI, with the exception of five countries: Bangladesh, Indonesia, Madagascar, Nigeria, and Peru. The reason is that the global MPI 2020 figures were agreed by OPHI and UNDP after the cutoff date for harmonisation. The next round of MPI Changes over Time will aim to incorporate all possible datasets released as part of the global MPI 2020. For more detail on the countries, data and time periods selected, see the MPI Methodological Note 50.
How have the projections on how many countries are on or off track to halve multidimensional poverty by 2030 been calculated?
Our analysis in the global MPI report 2020 uses the global MPI to project the expected progress in multidimensional poverty that will be achieved in 75 countries from 2015 to 2030 if current trends continue.
Three models were used: linear (continuation of recent absolute changes), proportional (continuation of recent relative changes) and logistic (continuation of changes adjusted for poverty levels). Linear models may overstate progress, and proportional models may understate it. The logistic model reflects the bounded nature of MPI (it cannot be less than 0 or greater than 1) and the empirical observation that ordinarily MPI falls more slowly in the poorest countries because most reduction is in intensity rather than incidence. Reduction tends to accelerate greatly in medium-poor countries, where both incidence and intensity fall. It slows among low-poverty countries, perhaps due to familiar challenges in going “the last mile.”
For further details, see Alkire, S., Nogales, R., Quinn, N. and Suppa, N. 2020 (Research in Progress 58a).
How have the simulations of COVID-19 impact been calculated?
The COVID-19 simulations were applied on 70 countries with a combined population of 4.8 billion people. This is a global simulation which cannot be disaggregated by country. We proceeded as follows:
- First, we projected past trends forward to obtain the level of MPI that was likely in 2020 had there been no pandemic.
- Second, we identified two groups of people in the global MPI micro-datasets: All primary school children who were attending school (Group A) and people who are: poor or vulnerable by the global MPI (see MPI Methodological Note 49), and not undernourished according to the global MPI data (Group B).
- Third, we randomly assigned deprivations in school attendance and nutrition to the relevant populations in Groups A and B. We built our assumptions on the UNESCO and WFP projections about the impact of COVID-19 on school attendance and food insecurity. In Group A, we randomly marked 50% of children as ‘out of school’ due to COVID-19. In Group B, we randomly marked 10%, 25%, or 50% of people as undernourished due to COVID-19.
- Fourth, we re-projected poverty forward to 2020, given different combinations of these assumptions. For each scenario, we looked at three things: What is the new level of poverty? How many years did COVID-19 set back poverty reduction trends? How many more people became poor in this scenario?
For further details, see Research in Progress 58a by Alkire, S., Nogales, R., Quinn, N. and Suppa, N. (2020).
Income vs. MPI
Why are there wide discrepancies between MPI poverty estimates and $1.90/day poverty estimates in many countries?
The MPI complements monetary poverty measures. It measures various deprivations directly. In practice, although there is a clear overall relationship between MPI and $1.90/day poverty, particularly in low poverty countries, the estimates do differ for many countries (these can be viewed in the global MPI Databank). The mis-match 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 (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 $1.90/day 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 in very useful ways from the $1.90 measure, some countries may wish to build a national Multidimensional Poverty Index that is tailored to their context, to complement the global MPI (see the MPPN website for more details).
Is the global MPI intended to replace the standard $1.90 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 $1.90 a 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 my country’s multidimensional poverty?
Country briefings for the 107 countries included in the global MPI 2020 are available which explain the global MPI results for each country.
This year, for the first time, 80 of these countries also have Changes over Time country briefings to unpack trends in multidimensional poverty.
What are the policy implications of the global MPI and Changes over Time data?
The MPI methodology shows where and how people are poor and Changes over Time research shows 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, which 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 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 multidimensional poverty measures 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.
Many governments including Afghanistan, Bhutan, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Honduras, Nepal, Seychelles, Sierra Leone, Pakistan, Thailand and Viet Nam have also released official multidimensional poverty indices – see the National Measures page on MNNP website. Colombia 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; Bhutan 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 cause children to leave primary education, or to become malnourished, or have worsened housing conditions and 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 Policy Briefing 53 and Policy Briefing 54).
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? This is explored in the 2020 global MPI report.
The global MPI covers more than 100 developing countries. Will an MPI be created for developed countries?
In the light of the COVID-19 pandemic, an MPI for developed countries may become more urgent. 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 comparable data across high-income countries. The list of the 107 countries that the global MPI 2020 covers and country-specific summaries are available on the Country briefings page and through the MPI data tables page.
For examples discussion MPI in developed countries see the following discussions for Germany and the United States on the MPPN website.
What are the main limitations of the global MPI?
The MPI has some drawbacks, due mainly 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 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 usually women’s nutritional data are 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.
As is well known, intra-household inequalities may be severe, but these could not be reflected in the MPI. Instead we recommend separate analyses linked to the MPI that uncover gendered and intrahousehold patterns, for example affecting children. See, for example, Working Paper 127.
While the MPI goes well beyond a headcount to include the intensity of poverty experienced, it does not reflect inequality among the poor. Rather, OPHI reports a separate statistic of inequality among the poor based on variance.
The estimates presented here are based on publicly available data and cover various years between 2008 and 2019, which limits direct cross-country comparability.
Why are these indicators used in the global MPI? Why not indicators for environment, employment, ethnicity 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 will release research probing the interrelationship between climate change, the environment and poverty and is currently exploring 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 do add this dimension.
Ethnicity: Not every survey currently includes ethnicity data. For the global MPI 2019 we have analysed ethnicity in the 25 countries for which data was available (see OPHI Briefing 55). Smaller case studies were also undertaken in year 2010 (OPHI Briefing 01) and 2014 (OPHI Briefing 21).
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 2020 MPI relies on the most recent and reliable data available since 2009. 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.
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 analyses of changes over time. Additionally, calculating harmonised trends requires two or more comparable surveys for any particular country with a minimum of three years apart. Countries that do not fulfil this criteria could not be included in 2020 despite being part of the annual global MPI release.
Further limitations arise from the harmonisation process which ensures that indicators are comparable across years within a country. This means that while surveys for a given country are comparable across the years, cross-country comparison is limited due to differences in the indicator definitions and cut offs. For details on how to compare across years, see the question ‘Is it possible to compare trends across countries?’