Context: India aims to reduce poverty in all its forms by at least half by 2030 (SDG target 1.2). To measure India’s progress in this respect, NITI Aayog has developed an indigenised index to monitor and address multidimensional poverty. The baseline edition of national multidimensional poverty index was launched in 2021. This present second edition of National MPI has been developed based on National Family Health Survey-5 (NFHS-5).
Indicators and weights used in MPI
MPI is measured based on three broad indicators of deprivations in health, education and standard of living each having equal weights:
Indicators and theirs weights
- Headcount Ratio: Proportion of multidimensionally poor in the population, which is arrived at by dividing number of multidimensionally poor reasons by total population.
- Intensity of Poverty: Average proportion of deprivations which is experienced by multidimensionally poor individuals. To compute intensity, the weighted deprivation scores of all poor people are summed and then divided by the total number of poor people.
- MPI value is arrived at by multiplying the headcount ratio (H) and intensity of poverty (A), reflecting both the share of people in poverty and the degree to which they are deprived.
MPI = H X A
Methodology of India’s National MPI: Alkire Foster Methodology
MPI is computed using the Alkire-Foster Methodology which is a globally accepted general framework for measuring multidimensional poverty that poor people on a dual-cutoff counting method.
Significance of multidimensional poverty
- MPI has been developed by UNDP & Oxford Poverty & Human Development Initiative (OPHI). The statistics from this metric is used by UNDP to measure levels of poverty across the globe in its Human Development Report.
- Multidimensional Poverty is qualitative measure of poverty and it used non-monetary metrics to measure poverty in the world by measuring overlapping deprivations in access to health, education and living standards.
- MPI captures additional information not measured by monetary measures of poverty such as broader qualitative aspects of life, child mortality, housing conditions and other basic services such as water and sanitation.
- Monetary measures of poverty based on poverty lines only give headcount ratios i.e., number of people who are poor. However, these measures fail to measure depth of poverty. It is possible that while the overall number of poor individuals reduce, while at the same time the poorest get more poor. Also, gains in quality of life may be completely missed unless the poor cross the poverty line or exit poverty.
- Thus, MPI provides insights not just into the distribution of poverty within a country but also indicates contribution of each indicator to multidimensional poverty.
- Using MPI, it has been possible to device schemes which target specific deprivations.
MPI Progress Report 2023
Steep decline in Poverty Headcount Ratio from 24.85% in 2015-15 to 14.96% in 2019-21. This indicates that India is well on course to achieve the SDG target 1.2 much ahead of 2030. At the same time, the Intensity of Poverty, which measures the average deprivation among the people living in multidimensional poverty also reduced from 47.14% to 44.39%.
Regional/State-wise distribution of Multidimensional Poverty Index
Analysis: Bihar, Jharkhand and Meghalaya fare the worst on the Multi-dimensional poverty index. Southern states of Maharashtra, Telangana, Andhra Pradesh, Tamil Nadu, Kerala, Karnataka, Punjab, Haryana & Himachal Pradesh.
Bihar, the state with the highest MPI value in NFHS-4 (2015-16), saw the fastest reduction in MPI value in absolute terms with the proportion of multidimensional poor reducing from 51.89% to 33.76% in 2019-21. The next fastest reduction in the MPI value was seen in Madhya Pradesh and Uttar Pradesh
