Abstract
The Multidimensional Poverty Index is used increasingly to measure poverty in developing countries. The index is constructed using selected indicators that cover health, education, and living standards dimensions. The accuracy of this tool, however, depends on how each indicator is measured. This study explores the effect of accounting for water quality in multidimensional poverty measurement. Access to drinking water is traditionally measured by water source types. The study uses a more comprehensive measure, access to safely managed drinking water services, which are free from E. coli contamination, available when needed and accessible on premises in line with Sustainable Development Goal target 6.1. The study finds that the new measure increases national multidimensional headcount poverty by 5-13 percentage points, which would mean that 5-13 million more people are multidimensionally poor. It also increases the poverty level in urban areas to a greater extent than in rural areas. The finding is robust to changes in water contamination risk levels and Multidimensional Poverty Index aggregation approaches and weighting structures.
Highlights
Measuring poverty is a contentious issue in the development literature
Depending on the estimated model and scope of analysis, we find that replacing the traditional measure of water indicator with a more objective measure of quality pushes up multidimensional poverty (MDP) headcount by 5–13 percentage points, which implies 5–13 million more MPIpoor individuals
MDP decreases as urbanity increases because the most of the Multidimensional Poverty Index (MPI) indicators are based on amenities that are more available in urban than in rural areas
Summary
Using income or consumption spending as a proxy for household economic welfare is a common way to estimate poverty in developing countries [1], but the approach has practical, empirical, and theoretical limitations, which Alkire and Santos [2] have summarized. These two indicators often fail to capture other crucial dimensions of poverty especially in developing countries. Data availability can be a practical constraint because income and consumption surveys are costly, time-consuming, and complex; and it is difficult to find quality data at adequate frequency for every country. Lack of comparable consumption and spending data limits international comparisons because different countries conduct such surveys differently and in different frequencies
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