In a discussion about the intersectional experiences of marginalized communities, the concept of Multidimensional Poverty (MDP) is an interesting study of the developed discourse. The concept captures a complex phenomenon that clarifies that poverty doesn’t simply mean the lack of money.
This article is the first in a two-part series where we delve deep into this concept, trace its development and later discuss its relevance and its effect on policymaking in India.
MDP gives a more comprehensive picture of the kind of poverty people suffer—the range of various disadvantages they experience. For example, a person who is poor can also have poor health or lack access to clean water and electricity.
MDP can be broken down into multiple areas to reveal the extent of poverty in different areas of a country among different sub-groups of people.
These areas have different indicators along which MDP can be measured. These are as follows:
A Multidimensional Poverty Index (MPI) measures MDP by reflecting multiple deprivations that poor people face in these three areas. There’s a global framework for MPI, which works as an international resource to measure acute MDP across more than 100 developed countries.
But MPI is not the first of its kind—it has had predecessors too.
HDI is a summary measure of average achievements in key dimensions of human development: life expectancy at birth, expected years of schooling, mean years of schooling, and gross national income per capita. Created in 1990 by economist Mahbub ul Haq, this index was used by United Nations Development Program (UNDP) to measure a country’s development. However, this index is GDP-focused, posing income as the sole indicator of welfare, which has received criticism in recent times.
Human development is much more nuanced than just the amount of money one person makes. It involves being able to exercise human rights and freedoms, for which there can be no simple quantitative measure. HDI also does not emphasize quality-of-life factors, such as feelings of security or happiness.
Moreover, this index is limited to the socio-economic sphere of life, thereby separating political and civil spheres from the discourse. This means that inequalities that fall outside the socio-economic spheres are not taken into account.
This is why the next index adopted by UNDP was the Inequality-adjusted Human Development Index (IHDI).
Introduced in 2010, IHDI accounts for the inequality that HDI wouldn’t compute by “discounting” each dimension’s average value according to its level of inequality. This means that IDHI would remain the same as HDI if there were no inequality across people, but it will fall below the HDI value if inequality exists.
Putting it simply will mean that if ‘a’ is the HDI value for a specific region, the IHDI value will be ‘b’ subtracted from ‘a’, where b accounts for the inequality in the region for specific people.
But there are limitations to IHDI too. This index is not association sensitive, which means that it does not take into account overlapping inequalities. Simply put, IHDI will not capture whether the same person is at the lower end of distributions in all three dimensions.
This is where MPI comes in handy. Developed to measure context-specific indicators, the MPI gives a more comprehensive and nuanced understanding of poverty.
By virtue of having been developed based on its predecessors, the MPI has certain advantages that help capture both the nature and intensity of poverty. Since it considers the interactions between all its dimensions, namely health, education, and standard of living, MPI allows for a more accurate picture of an individual’s level of poverty, which, in turn, helps policymakers develop comprehensive solutions to such issues.
For example, MPI will help identify how many people live without electricity and suffer malnutrition in a particular region, helping decision-makers in allocating the required resources to overcome such deprivation.
Currently, India measures MPI and uses this data to estimate multiple and simultaneous deprivations at a household level across all dimensions. In fact, MPI uses data from the National Family Health Surveys to measure poverty across districts, making it the first large-scale index disaggregated to both state and district levels by rural and urban areas, age groups, schedules, castes and tribes, and religious groups.
While this kind of index has helped us understand poverty as more than just an income-based deprivation, it has also initiated a discourse about the future of poverty indices.
Is MPI the best index to measure the constantly evolving deprivations people experience?
Despite being contextually relevant, can the MPI capture the nuances of a highly diverse nation such as India?
Stay tuned for the second part of this two-part article series!
Aishwarya Bhatia, Sambodhi