Over 50% of the population in Bihar classified as multidimensionally poor: NITI Aayog

Bihar, Jharkhand, Uttar Pradesh, Madhya Pradesh and Meghalaya have emerged as the poorest states in India with over 50% of the population in Bihar classified as multidimensionally poor, as per NITI Aayog’s Multidimensional Poverty Index.

The index, unveiled by the Aayog recently, shows 51.9% of Bihar’s population is poor followed by 42.16% in Jharkhand, 37.79% in Uttar Pradesh, 36.65% in Madhya Pradesh and 32.67% each in Meghalaya and Assam.

Kerala, Goa, Sikkim, Tamil Nadu and Punjab have registered the lowest poverty across India and are at the bottom of the index. Kerala has just 0.71% of its population as poor followed by 3.76% in Goa, 3.82% in Sikkim, 4.89% in Tamil Nadu and 5.59% in Punjab.


With smaller populations, the union territories have fared better with Puducherry having 1.72% of its population as poor, Lakshadweep (1.82%), Andaman & Nicobar Islands (4.30%), Delhi (4.79%) and Chandigarh (5.97%).

The multidimensionality of poverty, developed on the basis of the National Family Health Survey 2015-16 will facilitate formulation of sectoral policies and targeted interventions which contribute towards ensuring that “no one is left behind”, the Aayog said in its report.

Under the sustainable development goals, India aims to reduce “at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions”.

The Aayog has retained the 10 indicators of the global MPI while computing the national, essentially to be closely aligned to the global methodology and rankings, it said in its report.

India’s MPI has three equally weighted dimensions, health, education and standard of living - which are represented by twelve indicators namely nutrition, child and adolescent mortality, antenatal care, years of schooling, school attendance, cooking fuel, sanitation, drinking water, electricity, housing, assets and bank accounts.

According to the Aayog, the national MPI provides a high-level view of the level of multidimensional poverty and its change with time. “This presents an overall picture of poverty in the country, while also enabling closer and more in-depth analyses of areas of interest such as regions – state or districts, and specific sectors –under the dimensions of health and nutrition, education and living standards and complements the existing monetary poverty statistics,” it said.

“While this report is an indispensable first step in mainstreaming MPI, it is based on a dataset which is five years old. The success of numerous development interventions in the recent past have resulted in progress in key parameters on health, education, and standard of living,” it added.