Reforming Social Protection in India: Emphasizing Sustainable Development Goals through Multidimensional Indices

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Evolution of Poverty Measurement and the Multidimensional Poverty Index (MPI)

India has made significant strides in poverty measurement, evolving from subsistence-based estimates to multidimensional approaches that capture the quality of life. In 2021, Niti Aayog introduced the Multidimensional Poverty Index (MPI) for India, which measures simultaneous deprivations across health, education, and standards of living. This index, based on the Alkire-Foster methodology, quantifies both the incidence and intensity of multidimensional poverty through 12 weighted indicators aligned with national development priorities. Households are categorized according to their cumulative deprivation scores.

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 been instrumental in identifying overlapping deprivations, enabling targeted interventions. Between the 2015-16 and 2019-21 National Family Health Surveys (NFHS), the proportion of multidimensionally poor individuals decreased from 24.85% to 14.96%, lifting approximately 135 million people out of multidimensional poverty. This progress is attributed to granular data analysis, regional deprivation isolation, and targeted government schemes such as the Pradhan Mantri Awas Yojana, National Food Security Act (NFSA), Jal Jeevan Mission, and Swachh Bharat Mission.

Disparities in Progress and Challenges in Health-Related Deprivations

Despite overall improvements, health-related deprivations have seen only modest declines, highlighting the need for enhanced focus in this sector. Current healthcare schemes like the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) and the NFSA rely on the Socio-Economic Caste Census (2011) for eligibility, which uses income proxies that may exclude vulnerable households. For example, households owning a motor vehicle may be ineligible for AB-PMJAY despite significant health vulnerabilities. Moreover, beneficiary identification lacks systematic and scientific rigor, as revealed by government audits.

Limitations and the Need for Complementary Multidimensional Vulnerability Index (MVI)

The current MPI framework does not fully capture the spectrum of health-related vulnerabilities or the intensity of poverty. Experience from over 40 countries demonstrates the utility of MPIs and the added value of a complementary Multidimensional Vulnerability Index (MVI) to proactively identify populations at risk and prevent poverty cycles. For instance, Bangladesh employs both MPI and MVI in its strategy to become a high-income nation. A systematic review of 92 reports recommends the use of multidimensional indices for policy targeting, profiling, and local budget allocation, supporting the Sustainable Development Goals (SDGs) of no poverty (SDG 1) and good health and well-being (SDG 3).

The Potential of Multidimensional Indices in Indian Healthcare

Expanding Indicators for Enhanced Targeting

To better address health vulnerabilities, India can expand its MPI by integrating multidimensional indices with complementary datasets, capturing risk factors such as chronic diseases, access to health facilities, and demographic characteristics. International examples include:

• Colombia: Incorporates data on hypertension, diabetes, heart disease, chronic lung disease, and cancer, alongside regional health infrastructure and overcrowding.
• Afghanistan: Includes shocks to income, production, and security in its MPI.
• Dominican Republic: Considers access to health services, insurance coverage, and family support.
• Chile: Integrates environmental condition indicators.
• Panama: Employs a child-specific MPI.

India faces challenges such as high out-of-pocket health expenditures, inequitable access to health infrastructure, low health insurance uptake, and demographic shifts including an ageing population and marginalized groups. Incorporating indicators like socio-economic status, disability, comorbidities, immunization, migration status, informal employment, hospitalization trends, and social protection coverage can align with SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequalities).

Data Compilation and Infrastructure Development

Frequent and high-quality data collection is essential for understanding vulnerabilities. Colombia’s approach combines household surveys with administrative records and social protection data. Mexico institutionalized its MPI by issuing guidelines and ensuring data transparency. India can establish an independent body to regularly collect and collate data for multidimensional indices, leveraging its growing digital public infrastructure ecosystem.

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.

The proposed Social Registry aims to be an Aadhaar-linked, real-time updated database enabling needs-based targeting and adaptive social protection. Integration with the Ayushman Bharat Digital Mission’s health ID and leveraging the Jan Dhan-Aadhaar-Mobile (JAM) trinity can facilitate seamless direct benefit transfers. An appeal mechanism should also be implemented to address accidental exclusions, supporting SDG 16 (Peace, Justice and Strong Institutions).

Integration with Social Protection Schemes

International examples demonstrate the effectiveness of multidimensional indices in social protection:

• Afghanistan: Used MPI-based microsimulations to guide COVID-19 social protection measures.
• Honduras: Launched an MVI to identify beneficiaries for healthcare vouchers during the pandemic.
• South Africa: Utilized MVI to prioritize vaccine rollout among elderly and high-risk groups.

India’s AB-PMJAY currently provides hospitalization coverage to the bottom 40% of the population and seniors over 70 but lacks outpatient coverage and may not fully protect families with overlapping vulnerabilities. Employing MPI and MVI with sliding scale mechanisms can ensure equitable resource allocation proportional to vulnerability intensity, advancing SDG 1 (No Poverty) and SDG 3 (Good Health and Well-being).

Budget Allocation and Inter-Ministerial Coordination

Multidimensional indices can optimize budget allocation and enhance inter-ministerial coordination. Costa Rica accelerated poverty reduction in 2017 by linking MPI data to relevant programmes without increasing budgets. Pakistan’s proxy MPI guided district-level resource prioritization. Panama established a social cabinet chaired by the president to coordinate ministries for collaborative action.

India can adopt a similar “all of government” approach to improve coordination among ministries addressing health and social determinants, supporting SDG 17 (Partnerships for the Goals).

Conclusion

India’s Multidimensional Poverty Index marks significant progress in poverty measurement by capturing deprivations across health, education, and living standards. However, its current scope is limited regarding health vulnerabilities and is underutilized in healthcare policy design and targeting. Developing a nuanced MPI and complementary MVI framework can disrupt the cycle of health-linked poverty, ensuring timely and targeted social protection for the most vulnerable. This approach aligns closely with the Sustainable Development Goals, particularly SDG 1 (No Poverty), SDG 3 (Good Health and Well-being), SDG 10 (Reduced Inequalities), and SDG 17 (Partnerships for the Goals), thereby fostering inclusive and sustainable development in India.

Nimisha Chadha is a Research Assistant with the Health Initiative at the Observer Research Foundation.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 1: No Poverty
   • The article focuses on multidimensional poverty measurement and reduction in India, directly relating to SDG 1.
2. SDG 3: Good Health and Well-being
   • Health-related deprivations and healthcare schemes such as Ayushman Bharat are discussed, linking to SDG 3.
3. SDG 10: Reduced Inequalities
   • Targeting vulnerable groups such as Scheduled Tribes and Castes and addressing inequalities in health and social protection.
4. SDG 16: Peace, Justice and Strong Institutions
   • Emphasis on data transparency, institutional coordination, and governance reforms.
5. SDG 17: Partnerships for the Goals
   • International best practices and inter-ministerial coordination are highlighted for effective policy implementation.

2. Specific Targets Under Identified SDGs

1. SDG 1: No Poverty
   • Target 1.2: Reduce at least by half the proportion of men, women and children living in poverty in all its dimensions.
   • Target 1.3: Implement nationally appropriate social protection systems and measures for all.
2. SDG 3: Good Health and Well-being
   • Target 3.8: Achieve universal health coverage, including financial risk protection and access to quality essential healthcare services.
   • Target 3.c: Increase health financing and recruitment, development, training and retention of health workforce.
3. SDG 10: Reduced Inequalities
   • Target 10.2: Empower and promote social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.
4. SDG 16: Peace, Justice and Strong Institutions
   • Target 16.6: Develop effective, accountable and transparent institutions at all levels.
5. SDG 17: Partnerships for the Goals
   • Target 17.18: Enhance capacity-building support to developing countries to increase significantly the availability of high-quality, timely and reliable data.

3. Indicators Mentioned or Implied to Measure Progress

1. Multidimensional Poverty Index (MPI)
   • Measures simultaneous deprivations across health, education, and standards of living.
   • Includes 12 weighted indicators reflecting national priorities.
   • Used to track incidence and intensity of multidimensional poverty over time.
2. Health-related Indicators
   • Prevalence of health deprivations such as hypertension, diabetes, heart disease, chronic lung disease, and cancer (as seen in Colombia’s MPI).
   • Access to health services, health insurance coverage, and family support.
   • Catastrophic health expenditure pushing individuals below poverty line.
3. Social Protection Coverage
   • Coverage and targeting efficiency of schemes like Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) and National Food Security Act (NFSA).
   • Use of Socio-Economic Caste Census for eligibility identification.
4. Data Quality and Frequency
   • Regular collection and collation of quality data through household surveys, administrative records, and digital infrastructure.
5. Equity and Inclusion Metrics
   • Disaggregation by caste, tribe, age groups, and regional contexts to measure inclusion and targeted intervention effectiveness.

4. Table of SDGs, Targets and Indicators

Source: orfonline.org