NITI Aayog National Multidimensional Poverty Index

The National Multidimensional Poverty Index (MPI) is India's official non-monetary measure of poverty, constructed and published by NITI Aayog, the Government of India's apex public policy think tank that replaced the Planning Commission on 1 January 2015. The index draws its methodological foundation from the global MPI developed jointly by the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford and the United Nations Development Programme (UNDP), first released in 2010, which itself applies the Alkire-Foster method—a dual-cutoff counting approach formalised by Sabina Alkire and James Foster in 2007–2011. NITI Aayog's mandate to produce the national index arose from its role as the nodal agency for monitoring India's progress on the UN Sustainable Development Goals, particularly SDG Target 1.2, which commits states to halving multidimensional poverty by 2030. The baseline National MPI report was released in November 2021, drawing on the National Family Health Survey-4 (NFHS-4, 2015–16), with a progress review published in July 2023 using NFHS-5 (2019–21) data.

The index aggregates twelve indicators grouped into three equally weighted dimensions—health, education, and standard of living—broadly mirroring the global MPI but adding two India-specific indicators: maternal health and bank accounts. Health carries the indicators of nutrition, child and adolescent mortality, and maternal health; education comprises years of schooling and school attendance; standard of living covers cooking fuel, sanitation, drinking water, electricity, housing, assets, and bank accounts. Each of the three dimensions receives a one-third weight, and indicators are weighted equally within their dimension. The Alkire-Foster method proceeds in two steps: a household is identified as multidimensionally poor if it is deprived in indicators whose combined weights sum to at least one-third (the poverty cutoff k = 0.33). The headcount ratio (H) records the proportion of people so identified, while the intensity (A) measures the average share of weighted deprivations they experience. The MPI value is the product H × A, capturing both incidence and depth in a single figure.

A defining property of the Alkire-Foster framework is dimensional decomposability: the aggregate MPI can be broken down by indicator, by population subgroup, and by geography, allowing analysts to identify which deprivations contribute most to poverty in a given district or state. This permits the index to inform targeted policy—for instance revealing that cooking fuel, housing, and nutrition consistently drive the largest contributions to India's MPI. The national index aligns its indicators with flagship government schemes such as Poshan Abhiyaan (nutrition), Swachh Bharat Mission (sanitation), Pradhan Mantri Ujjwala Yojana (cooking fuel), Saubhagya (electricity), Pradhan Mantri Awas Yojana (housing), and Jan Dhan Yojana (bank accounts), so that movement in indicator deprivation maps onto programme delivery.

NITI Aayog's July 2023 discussion paper reported that 24.85 crore Indians escaped multidimensional poverty between 2013–14 and 2022–23, with the national headcount ratio falling from 29.17 per cent (2013–14) to 11.28 per cent (2022–23) on its projected estimates. The 2021 baseline placed Bihar, Jharkhand, and Uttar Pradesh as the states with the highest MPI values, while Kerala, Goa, Sikkim, and Tamil Nadu recorded the lowest. The work is coordinated within NITI Aayog through a Multidimensional Poverty Index Coordination Committee, with technical partnership from OPHI and UNDP, and feeds into India's Voluntary National Reviews presented to the UN High-Level Political Forum.

The National MPI is distinct from the older income- or consumption-based poverty lines associated with the Tendulkar Committee (2009) and the Rangarajan Committee (2014), which counted a person poor if monthly per-capita expenditure fell below a calorie-anchored threshold. Whereas those monetary measures capture purchasing power, the MPI captures concurrent deprivations in capabilities and basic services that money metrics may miss. It should also be distinguished from the Human Development Index (HDI), a country-level composite of life expectancy, education, and income that does not identify the poor at household level, and from the global MPI, which uses a slightly different indicator set and weighting and is not tailored to Indian schemes. The MPI is a deprivation count, not a welfare ranking of the entire population.

Controversy surrounds the index's reliance on NFHS rounds conducted years apart, which complicates real-time tracking, and on the 2023 paper's use of statistical projection beyond the 2019–21 survey to reach 2022–23 figures—a method critics argue overstates recent gains. Scholars including Jean Drèze have questioned whether equal weighting of indicators, and the choice of indicators themselves, adequately reflects severity; deprivation in housing is weighted identically to deprivation in child mortality. The prolonged absence of an updated Consumption Expenditure Survey until 2022–23 left the MPI as one of the few systematic poverty measures available, amplifying both its policy weight and the scrutiny of its assumptions. Debate also continues over whether bank account ownership reliably signals financial inclusion or merely scheme-driven account opening.

For the working practitioner—the civil services aspirant, the development economist, or the desk officer monitoring SDG progress—the National MPI is essential as India's principal multidimensional poverty instrument and a recurring subject in UPSC General Studies Paper III on inclusive growth and government schemes. It exemplifies the global shift from money-metric to capability-based poverty measurement championed by Amartya Sen, and its scheme-linked indicators make it a diagnostic dashboard for evaluating delivery of welfare programmes. Analysts should cite it alongside its data source and survey round, note the distinction between the national and global indices, and treat its projected estimates with the methodological caution the underlying surveys warrant.