Methodology and Usage
The data for the MPM is derived from harmonized surveys in the World Bank’s Global Monitoring Database. The latest estimates for the world are available for circa 2018, using household survey data collected within a three-year window between 2015 to 2021.
The MPM is composed of six indicators: consumption or income, educational attainment, educational enrollment, drinking water, sanitation, and electricity. These are mapped into three dimensions of well-being: monetary, education, and basic infrastructure services.
The three MPM dimensions are weighted equally, and within each dimension each indicator is also weighted equally. Individuals are considered multidimensionally deprived if they fall short of the threshold in at least one dimension or in a combination of indicators equivalent in weight to a full dimension. (See Table1). In other words, households will be considered poor if they are deprived in indicators whose weight adds up to 1/3 or more. Because the monetary dimension is measured using only one indicator, anyone who is income poor is automatically also poor under the multidimensional poverty measure.
Table 1. Multidimensional Poverty Measure Dimensions, Weights, and Parameters
| Dimension | Parameter | Weight |
|---|
| Monetary | Daily consumption or income is less than $ 2.15 per person. | 1/3 |
| Education | At least one school-age child up to the age of grade 8 is not enrolled in school. | 1/6 |
| | No adult in the household (age of grade 9 or above) has completed primary education. | 1/6 |
| Access to basic infrastructure | The household lacks access to limited-standard drinking water. | 1/9 |
| | The household lacks access to limited-standard sanitation. | 1/9 |
| | The household has no access to electricity. | 1/9 |
Source: World Bank, 2020
Summarizing the information on the different deprivations into a single index proves useful in making comparisons across populations and across time. However, any aggregation of indicators into a single index always involves a decision on how each indicator is to be weighted.
Not all countries have current and comparable data on all the above dimensions, making it challenging to construct a multidimensional poverty measure, especially at the global level. More details on the methodology of the MPM are available here.
What does multidimensional poverty look like around the world?
The global multidimensional poverty headcount ratio for circa 2018 was 14.5 percent, nearly double the monetary poverty measure of 8.8 percent. In line with past MPM editions, Sub-Saharan Africa and South Asia remain hotspots for multidimensional and monetary poverty. In Sub-Saharan Africa, half of the population (52 percent) experiences multidimensional poverty, significantly exceeding the global average, and nearly double the monetary poverty in the region. In South-Asia, 17 percent of the population classify as multidimensionally poor, however these results should be interpreted with caution given the low population coverage in the region.
Table 2 displays the share of population deprived in each MPM indicator. Compared to the 5th edition of the MPM, there are slight changes (less than one percent on average) in the share of population experiencing deprivations by indicator, reflecting updates to underlying survey data in some countries.[1] Globally, inadequate access to sanitation stands out as the most common deprivation, affecting nearly a quarter (23.2 percent) of the population. This is particularly acute in Sub-Saharan Africa, where two thirds of the population lack basic sanitation access (65.6 percent). Educational attainment (12.6 percent) and access to electricity (12.1 percent) are the most common deprivations globally after sanitation.
Table 2. Monetary and Multidimensional Poverty Headcount, by Region and the World, circa 2018
Region | Monetary poverty, headcount ratio (%) | Multidimensional poverty, headcount ratio (%) | Number of economies | Population coverage (%)a |
East Asia and Pacific | 3.2 | 4.7 | 13 | 30.5 |
Europe and Central Asia | 0.3 | 2.1 | 25 | 89.2 |
Latin America and the Caribbean | 3.8 | 4.6 | 15 | 87.1 |
Middle East and North Africa | 1.2 | 1.8 | 5 | 51.3 |
South Asia | 8.1 | 17.3 | 5 | 22.1 |
Sub-Saharan Africa | 32.4 | 51.8 | 34 | 71.1 |
Rest of the World | 0.7 | 1.4 | 24 | 72.1 |
All regions | 8.8 | 14.5 | 121 | 50b |
Source: Global Monitoring Database, November 2023.
Note: The monetary headcount is based on the international poverty line of $2.15. Regional and total estimates are population-weighted averages of survey-year estimates for 121 economies and are not comparable to the monetary poverty measures presented in PIP. The multidimensional poverty measure headcount indicates the share of the population in each region defined as multidimensionally poor. Number of economies is the number of economies in each region for which information is available in the window between 2015 and 2021, for a circa 2018 reporting year. Regions without sufficient population coverage are shown in light grey.
a. Data coverage differs across regions. The data cover as much as 89 percent of the population in Europe & Central Asia and as little as 22 percent of the population in South Asia. The coverage for South Asia is low because no multidimensional poverty data is available for India between 2014 and 2021. Due to the absence of data on China and India, the regional coverage of South Asia, and East Asia and Pacific is insufficient.
b. The table conforms to both coverage criteria used for the global poverty estimate. The global population coverage for low-income and lower-middle-income countries is 50.24 percent (also see annex 1A of World Bank, 2020).
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[1] Countries with updated data include Australia, Benin, Burkina Faso, Côte d'Ivoire (Ivory Coast), The Gambia, Iran
Israel, South Korea, Mali, Niger, Senegal, Togo, United States. These changes are due to survey revisions and updates in West African Economic and Monetary Union (WAEMU) countries to improve the welfare measurements, and to updates in the Luxembourg Income Study (LIS), which is used to draw welfare data in several high-income countries. See Aron et al. 2023 for more information on these changes.
