Actively using the Multidimensional Poverty Index in policy making, along with a complementary Multidimensional Vulnerability Index, can help eliminate residual deprivations and prevent backsliding.
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India’s poverty measurement has evolved significantly, from pre-independence subsistence-based estimates to those including dimensions affecting quality of life. In 2021, Niti Aayog released the Multidimensional Poverty Index (MPI) for India, measuring simultaneous deprivations across health, education, and standards of living, reflecting the multifaceted nature of deprivation. Based on the Alkire-Foster methodology, the index captures both the incidence and intensity of multidimensional poverty. India’s MPI encompasses 12 weighted indicators, reflecting national development priorities. Households are then categorised based on their cumulative deprivation score.
Progress across the indicators remains disproportionate, with health-related deprivations declining by only 0.6-6 percentage points, compared to sharper reductions of 15-25 percentage points for indicators of standards of living.
The MPI has proved valuable in recognising overlapping deprivations, thereby enabling targeted interventions. When comparing the baseline MPI, based on the 2015-16 National Family Health Survey (NFHS) and the 2019-21 NFHS, the proportion of the multidimensionally poor fell from 24.85 percent to 14.96 percent, with approximately 135 million moving out of multidimensional poverty. The progress can be attributed to the granularity of the data, isolating regional deprivation across each indicator, and targeted interventions under the State Support Mission initiative and flagship government schemes, including the Pradhan Mantri Awas Yojana, the National Food Security Act (NFSA), the Jal Jeevan Mission, and the Swachh Bharat Mission, among others. However, progress across the indicators remains disproportionate, with health-related deprivations declining by only 0.6-6 percentage points, compared to sharper reductions of 15-25 percentage points for indicators of standards of living.
Figure 1: Percentage of the Total Population of India who are Deprived in Each Indicator
Source: NITI Aayog
Schemes for healthcare, such as the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) and the NFSA, 2013, use the Socio-Economic Caste Census, 2011, for identifying eligible households based on income proxies. However, decadal data is insufficient for adequate targeting over time. Broad proxies also risk the exclusion of households that may be deprived. For instance, a household with a motor vehicle may be ineligible for AB-PMJAY despite prevalent health deprivations. Moreover, the framework cannot capture the varying intensity of poverty. A government audit found that beneficiary identification by states was not systematic or scientific.
This indicates that despite the existence of an MPI, it remains underutilised in healthcare policy. Additionally, the MPI does not cover the full spectrum of health-related vulnerabilities. Experience from over 40 nations showcases the potential of MPIs, while also highlighting the benefits of a complementary Multidimensional Vulnerability Index (MVI) to build a more responsive system.
Schemes for healthcare, such as the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) and the NFSA, 2013, use the Socio-Economic Caste Census, 2011, for identifying eligible households based on income proxies.
A complementary MVI can help identify populations that are susceptible to deprivation, allowing for proactive targeting efforts to prevent them from falling into the vicious cycle of poverty. As lower levels of poverty are reached, it becomes harder to eradicate, necessitating the use of multidimensional indices – both MPI and MVI – as done by Bangladesh in its attempt to become a high-income nation. A systematic review of 92 reports on multidimensional poverty recommends its use in policy targeting, profiling, and local budget allocations. Actively using the MPI in policy making, along with a complementary MVI, can help eliminate residual deprivations and prevent backsliding.
Given the acknowledgement of the MPI by the Government of India under the Global Indices for Reform and Action initiative as a tool for driving systemic reforms and growth, and its commitment to the 2030 Agenda, focused interventions can be better supported by strengthening measurement tools, learning from global best practices, tailoring them to local contexts, and using them actively. This aligns with the Viksit Bharat mission to make India a completely developed nation by 2047.
By recognising the differentiated vulnerability in health arising from the clustering of risk factors and integrating multidimensional indices with complementary datasets, a comprehensive picture of health-related deprivation can be painted. For instance, Colombia’s MPI and MVI include data on individuals with hypertension, diabetes, heart disease, chronic lung disease and cancer, among others. This information is triangulated with regional indicators such as the availability of health facilities, overcrowding, and demographic characteristics to assess the distribution of vulnerabilities. Afghanistan’s MPI incorporates shocks to income, production, and security. The Dominican Republic’s framework includes access to health services, health insurance coverage, and family support. Chile integrates indicators on environmental conditions, while Panama employs a child-specific MPI.
To address the several challenges faced by Indian healthcare, global best practices can be modified and adopted for the Indian context. Despite reductions in out-of-pocket expenditure in the past decade from 62.6 percent to 39.4 percent, catastrophic health expenditure still pushes an estimated 32-39 million individuals below the poverty line annually. Equity in access to health infrastructure, low uptake of health insurance and the rise of nuclear families continue to be challenges. Given India’s changing demography—with an ageing population and with five of six multidimensionally poor individuals belonging to Scheduled Tribes or Castes—specific indices for the elderly and marginalised groups need to be adopted.
Depending on national development priorities, some other indicators that India can consider include socio-economic and demographic characteristics, disability, comorbidities, immunisation, migration status, informal employment, regional data on hospitalisation trends, and social protection coverage. States may customise the index to suit their regional contexts as well.
Frequently collected, quality data is important to understand the realities for the most vulnerable. Colombia’s indices coupled household surveys with administrative records and social protection schemes. India could expand the multidimensional indices using existing national surveys to include health-specific indicators. Mexico institutionalised the index by issuing guidelines and making data public, improving transparency. Similarly, India could establish an independent body to regularly collect and collate quality data for multidimensional indices.
India is already laying the groundwork for a unified interoperable data ecosystem – making the convergence of MPI, MVI, and social protection systems an emerging reality.
While collecting extensive data frequently is challenging for a country like India, the returns are high as well. Doing so is becoming increasingly feasible with India’s growing digital public infrastructure ecosystem. Leveraging it, India can move beyond one-size-fits-all solutions that cannot adequately cater to the needs of 1.45 billion people who are very diverse from one another. The government’s proposed Social Registry aims to be an Aadhar-linked database updated in real time, allowing for a shift towards needs-based targeting and adaptive social protection. When integrated with the digital health ID under the Ayushman Bharat Digital Mission, electronic health records can be connected to social profiles. Leveraging the Jan Dhan-Aadhar-Mobile trinity, direct benefit transfers can be made seamlessly. Additionally, an appeal mechanism should allow households to contest accidental exclusion. Overall, India is already laying the groundwork for a unified interoperable data ecosystem – making the convergence of MPI, MVI, and social protection systems an emerging reality.
In Afghanistan, microsimulations based on the national MPI projected the impact of COVID-19 on poverty levels, guiding rapid social protection measures. Honduras launched an MVI in 2020 as well to identify potential beneficiaries of a transfer programme, providing 260,000 of the poorest with vouchers for accessing preventative healthcare during the pandemic. South Africa used its MVI for its vaccine rollout strategy, prioritising the elderly, and high-risk communities. India can similarly adopt MPIs and MVIs to not only respond effectively to health emergencies but also design programmes to achieve other national health goals.
By employing multidimensional indices with sliding scale mechanisms for differentiated protection in proportion to households’ intensity and multidimensionality of vulnerability, equitable resource allocation can be ensured.
Given that the financial burden of medical shocks remains a leading cause for impoverishment in India, aligning the MPI and MVI with existing social protection schemes such as AB-PMJAY could enhance these initiatives. Currently, the scheme provides INR 5,00,000 per family for hospitalisation to the bottom 40 percent of the population and those aged over 70. While it is commendable, the lack of coverage for outpatient services and the limited extent of coverage may not adequately extend financial protection for families facing overlapping vulnerabilities and thereby at higher risk of adverse health outcomes. By employing multidimensional indices with sliding scale mechanisms for differentiated protection in proportion to households’ intensity and multidimensionality of vulnerability, equitable resource allocation can be ensured. India uses an analogous composite index for its Aspirational Districts Programme. Going forward, the MPI and MVI can serve as uniform tools for such initiatives, ensuring consistent identification and tracking of vulnerability across geographies.
Utilising multidimensional indices can also help improve budget allocations, maximising efficiency and effectiveness in the use of public resources. This was demonstrated by Costa Rica in 2017, when, without further increasing the budget, poverty reduction was accelerated by leveraging the MPI. This was achieved by linking deprivations to programmes under the purview of relevant institutions, improving coordination, collaboration and budget allocation, preventing duplication, and enabling savings. Pakistan’s ‘proxy’ MPI guided district-level resource allocation through prioritisation.
For the effective application of the MPI in healthcare policy, inter-ministerial coordination is required. Panama formed a social cabinet comprising several ministries, chaired by the president, empowering institutions for collaborative action. Similarly, an “all of government” approach can help India enhance coordination among the various ministries to address health challenges and their social determinants.
India’s MPI signifies substantial progress in poverty measurement, capturing deprivations across health, education, and living standards at the district level. However, in its current form, it views health deprivations through a narrow lens, and does not account for vulnerabilities. Moreover, it is underutilised in health policy designing and targeting. A nuanced MPI and MVI framework can break the cycle of health-linked poverty and ensure timely and targeted social protection for those who need it the most, thereby eliminating residual deprivations and preventing backsliding.
Nimisha Chadha is a Research Assistant with the Health Initiative at the Observer Research Foundation.
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Nimisha Chadha is a Research Assistant with ORF’s Centre for New Economic Diplomacy. She was previously an Associate at PATH (2023) and has a MSc ...
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