Link to Facebook PageLink to RSS Feed Link to Twitter

MPI Frequently Asked Questions

Methodology

Income vs MPI

Policy & international adoption

Limitations

Methodology

What is the Global MPI? The Global Multidimensional Poverty Index (MPI) is a measure designed to capture the severe deprivations that each person faces at the same time. The MPI reflects both the incidence of multidimensional deprivation, and its intensity – how many deprivations people experience at the same time. It can be used to create a comprehensive picture of people living in poverty, and permits comparisons both across countries, regions and the world, and within countries by ethnic group, urban/rural location, as well as other key household and community characteristics. The MPI builds on recent advances in theory and data to present the first global measure of its kind, and offers a valuable complement to traditional income-based poverty measures.


Why is this better than the Human Poverty Index (HPI) previously used in the Human Development Rerport?
The MPI replaced the HPI, which appeared in the HDR from 1997-2009. Pioneering in its day, the HPI used country averages to reflect aggregate deprivations in health, education, and standards of living. It could not identify specific individuals, households or larger groups of people as jointly deprived. The Global MPI addresses this shortcoming by capturing how many people experience overlapping deprivations (incidence) and how many deprivations they face on average (intensity). The MPI can be broken down by indicator to show how the composition of multidimensional poverty changes for different regions, ethnic groups and so on—with useful implications for policy.

What does the MPI measure? The MPI identifies overlapping deprivations at the household level across the same three dimensions as the Human Development Index (living standards, health, and education) and shows the average number of poor people and deprivations with which poor households contend. For details see Alkire and Santos 2010. Read more about the MPI methodology here.

What makes a household “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 weighted indicators (see below for definitions of ‘severe’ poverty and ‘vulnerable’ to poverty).

Why is income not included?
We could not include income due to data constraints. Income poverty data come 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 income poor and also deprived in all the MPI indicators so could not include income.


What data are used in the MPI?
The MPI relies on three main datasets that are publicly available and comparable for most developing countries: the Demographic and Health Survey (DHS), the Multiple Indicators Cluster Survey (MICS), and the World Health Survey (WHS). Certain countries use special datasets. The MPI data pages list in full the surveys used.

The MPI is described as a measure of acute poverty. How does this differ from extreme poverty? The MPI reflects the severe deprivations that people face at the same time. Because it was designed to internationally compare across developing nations, it is most relevant to lesser developed countries. We have described the MPI as a measure of ‘acute’ poverty to avoid confusion with the World Bank’s measure of ‘extreme’ poverty that captures those living on less than $1.25 a day.

How do I interpret the various values presented with the Global MPI results? The MPI constitutes a family or set of poverty measures. 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, 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 the most prevailing deprivations. The MPI measures are explained below:

Incidence of poverty: the proportion of people who are poor according to the MPI (those who are deprived in at least one third of the weighted indicators).

Average intensity of poverty: the average number of deprivations people experience at the same time.

MPI value: The MPI value summarises information on multiple deprivations into a single number. It is calculated by multiplying the incidence of poverty by the average intensity of poverty.

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 HDR 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. The population in severe poverty, meanwhile, measures the percentage of the population in severe multidimensional poverty—that is those with a deprivation score of 50 percent or more.


How does the MPI respond to changes over time?
We estimated changes to the MPI over time for 34 countries in 2014 where suitable data was available. For details see the research brief ‘Reducing Multidimensional Poverty and Destitution: Pace and Patterns‘, and the MPI Data Tables for 2014.

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, including video guides, 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 OPHI Short Courses.

Income vs MPI


Why are there such wide discrepancies between MPI poverty estimates and $1.25/day poverty estimates in so many countries?
The MPI complements income poverty measures. It measures various deprivations directly. In practice, although there is a clear overall relationship between MPI and $1.25/day poverty, the estimates do differ for many countries. This is a topic for further research, but some possibilities can include public services, as well as different abilities to convert income into outcomes such as good nutrition. For more information, see materials from the workshop ‘Dynamic Comparison between Multidimensional Poverty and Monetary Poverty‘.

Why is the MPI headcount (much) higher than national poverty estimates in some countries? The MPI, like the $1.25/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 poverty number, or the MPI headcount is wrong – these simply measure different conceptions of poverty. At the same time, just as national poverty measures, in contrast, are designed to reflect the national situation more accurately and often differ in very useful ways from the $1.25 measure, some countries may wish to build a national multidimensional poverty index that is tailored to their context, to complement the global MPI (see ‘National policy‘ for details of countries who are doing this).

Is the MPI intended to replace the standard $1.25 a day measure of poverty used for the MDGs and other international purposes? No. The MPI is intended to complement monetary measures of poverty, including $1.25 a day estimates. The relationship between these measures, as well as their policy implications and methodological improvement, are priorities for further research.

Policies & international adoption

What are the policy implications? The MPI methodology shows aspects in which the poor are deprived and help to reveal the interconnections among those deprivations. This enables policymakers to target resources and design policies more effectively. This is especially useful where the MPI reveals areas or groups characterized by severe deprivation. Examples where this has been done in practice already include Mexico’s poverty targeting programme and Colombia’s poverty reduction strategy, tied to their nationally adapted MPIs.

How does the MPI relate to the Millennium Development Goals (MDGs)? The MPI indicators are drawn from the MDGs as far as the available internationally comparable data allow. The ten indicators of the MPI are identical, or relate, to MDG indicators: nutrition (MDG 1), child mortality (MDG 4), access to drinking water (MDG 7), access to sanitation facility (MDG 7) and use of an improved source of cooking fuel (MDG 9). The overall MPI can be broken down into its constituent parts, revealing the overlapping needs of families and communities across a range of indicators which so often have been presented in isolation. This helps policymakers to see where challenges lie and what needs to be addressed. OPHI has suggested that a global MPI 2015+ should be considered for the post-2015 MDGs; you can read the briefing here.

How is the MPI approach useful at the country level? The multidimensional poverty approach can be adapted using indicators and weights that make sense at the country level to create tailored national poverty measures. The MPI can be useful as a guide to helping governments tailor a poverty measure that reflects multiple local indicators and data. In 2009 Mexico, became the first country to adopt a multidimensional poverty measure reflecting multiple deprivations on the household level. In 2011, Colombia introduced the first poverty reduction plan to use an adaptation of OPHI’s measure. Colombia’s binding “multidimensional” poverty-reduction targets are tied to a new national Multidimensional Poverty Index Colombia (MPI-Colombia) which assesses broader social and health-related aspects of poverty: education, employment, the condition of children and young people, health, access to public services and housing conditions.

Can the indicators be adapted at the country level? Yes. The global MPI estimates are constrained by need for comparability. National teams should use the indicators and weights that make sense. The multidimensional poverty approach to assessing deprivations at the household level can be tailored using country-specific data and indicators to provide a richer picture of poverty at the country level.

Can the MPI be adopted for national poverty eradication programs? Yes. The MPI methodology can and should be modified to generate national multidimensional poverty measures that reflect local cultural, economic, climatic and other factors. The global MPI was devised as an analytical tool to compare acute poverty across nations. Colombia is a powerful example of how the MPI can be used to inform national poverty eradication programmes.

How does the MPI respond to the effects of shocks? The effects of shocks are difficult to capture in any poverty measure. Because the standard survey data used to estimate the global measure are collected only every three years, the ability to detect changes is limited by the available data. The MPI will reflect the impacts of shocks as far as these cause children to leave primary education or to become malnourished, for example. If more frequent data are available at the country or local level, this can be used to seek to capture the effects of larger scale economic and other shocks.

The MPI only covers 108 developing countries. Will an MPI be created for developed nations? This is still under investigation. The list of all 108 countries that MPI estimates are available for and country-specific summaries are available on the MPI country briefing page and through the MPI data pages.

Limitations

What are the main limitations of the MPI? The MPI has some drawbacks, due mainly to data constraints. First, the indicators include both outputs (such as years of schooling) and inputs (such as cooking fuel) as well as one stock indicator (child mortality, which could reflect a death that was recent or long ago), because flow data are not available for all dimensions. Second, the health data are relatively weak and overlook some groups’ deprivations especially for nutrition, though the patterns that emerge are plausible and familiar. Third, in some cases careful judgments were needed to address missing data. But to be considered multidimensionally poor, households must be deprived in at least six standard of living indicators or in three standard of living indicators and one health or education indicator. This requirement makes the MPI less sensitive to minor inaccuracies. Fourth, as is well known, intra-household inequalities may be severe, but these could not be reflected. Fifth, while the MPI goes well beyond a headcount to include the intensity of poverty experienced, it does not measure inequality among the poor, although decompositions by group can be used to reveal group-based inequalities. Finally, the estimates presented here are based on publicly available data and cover various years between 2002 and 2011, which limits direct cross-country comparability.

Why is empowerment not included? We could not include empowerment due to data constraints. The DHS surveys collect data on womens’ 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 are 2014 MPI estimates only available for 108 countries? We could not include other countries due to data constraints. Comparable data on each of the indicators were not available for other developing nations.


Why does national data for the MPI date from so many different years?
Isn’t it unfair to compare countries if the statistics in one case are five years older than in another? The MPI relies on the most recent and reliable data available since 2002. However surveys are taken in different years, and some countries do not have recent data. In order to facilitate the 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 government to collect new surveys that better reflect more recent progress. A significant number of DHS and MICS household surveys are currently underway and it is expected that more recent data will be available soon for a number of countries.

Join OPHI Mailing List

Email address:     

Latest

OPHI annual Summer School on Multidimensional Poverty in Oxford, UK OPHI Summer School 2014 concludes today. You can now view the materials online. Read more

OPHI’s John Hammock visits Accion Joven Foundation in Costa Rica John Hammock visits Accion Joven Foundation, an organization that seeks to rescue at-risk youth from poverty. Read more

Dominican Republic to establish a national MPI Government to develop a new multidimensional poverty measure to capture information useful for strategic decision-making Read more

Measuring Multidimensional Poverty in Latin America: Previous Experience and the Way Forward This paper states the need to design a multidimensional poverty index for the Latin America region (LA-MPI) that can monitor poverty trends in a cross-country comparable way, yet is also relevant to the particular regional context. Read more

Measuring Conjoint Vulnerabilities in Italy: An Asset-Based Approach This paper uses an asset-based approach, focusing on the resources that individuals and households can draw upon to reduce economic vulnerability and strengthen their resilience. Read more

Measuring and Decomposing Inequality among the Multidimensionally Poor Using Ordinal Data: A Counting Approach A paper by Suman Seth and Sabina Alkire proposes the use of a separate decomposable inequality measure using Demographic Health Survey datasets. Read more