29 December 2023 – The Hindu

Details of the Multi-Dimensional Poverty Index

• Traditional income-based metrics of poverty are constrained and exclusively concentrate on the lack of resources needed to survive. However, poverty is far more than just struggling to survive.
• Amartya Sen, the recipient of the Nobel Prize, developed a comprehensive, cutting-edge, and expansive viewpoint on well-being by emphasising capabilities and functionings—a concept known as the capacity approach.

What is the Capability Approach of Amartya Sen?

• The capacity approach developed by Amartya Sen provides a normative framework for assessing social structures and individual well-being.
• Rather than happiness, preferences, or wealth, it focuses on the actual possibilities or freedoms people have to live the kinds of lives they value.
• Sen claims that functionings and capabilities are the two primary parts of the capability approach.
• The worthwhile states of being and doing that people can attain, including being well-educated, socially engaged, or in good health, are known as functionings.
• A person’s capabilities are the range of possible functionings that they can select from based on their social and personal conditions.
• For instance, depending on their diet and food availability, a person may be able to be either undernourished or well-nourished.
• Sen contends that the capacity approach—as opposed to utilitarianism and resourcism—is a superior method of evaluating human welfare.
• According to him, these methods are either too limited or too indifferent to the complexity and diversity of human existence.
• Making decisions that result in the greatest happiness or fulfilment of desires is the essence of utilitarianism.
• Conversely, resourcism deals with the distribution of resources in society, such as money, goods, and income.
• Sen believes that increasing people’s capabilities—rather than just their earnings or access to utilities—is the ultimate goal of development.
• Sen’s capacity-based approach served as the model for the Human Development Index.

The Human Development Index (HDI): What is it?

• The three facets of human development—health, education, and standard of living—are combined to create the HDI, a composite indicator.
• The Human Development Report, which is released yearly, contains the HDI, which is determined by the United Nations Development Programme (UNDP).
• The Multidimensional Poverty Index (MPI) was developed as a complement to the HDI because it was unable to adequately capture the myriad facets and complexities of poverty on its own.

How does MPI differ from HDI, and what does it mean?

The MPI, or Multidimensional Poverty Index:

• The Multiple Poverty Index (MPI) is a metric used to quantify poverty that accounts for the various forms of deprivation that people experience in relation to health, education, and standard of life.
• The United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) created the MPI, which is included in the Human Development Reports.
• The average number of dimensions in which a person is deprived (the intensity) is multiplied by the percentage of those who are multidimensionally poor (the incidence or headcount ratio) to determine the MPI.
• Higher values on the MPI scale, which goes from 0 to 1, denote higher degrees of multidimensional poverty.

In India, who does the MPI calculation?

• The Oxford Poverty and Human Development Initiative (OPHI), the United Nations Development Programme (UNDP), and the NITI Aayog work together to compute the MPI in India.
• The National MPI’s nodal body, NITI Aayog, assigns rankings to States and UTs based on their respective performance.
• The National Family Health Survey-5 (NFHS-5) served as the main data source used to calculate these numbers.

Why is there doubt about the MPI 2023 data?

• Quick Drop in MPI Values: There have been doubts raised about the veracity of the claimed drop in India’s national MPI 2023 value, which went from 24.85% to 14.96% between 2015–16 and 2019–21. A significant improvement in poverty levels is suggested by the drop of 9.89%, which means that over 135.5 million individuals have been lifted out of poverty during this time. These approximations are thought to be inaccurate and uninformed, nevertheless.
• Ignoring the Effects of the COVID-19 Pandemic: The significant drop in MPI values in 2020–21 does not take into consideration the disruptions to the economy and society that the pandemic brought about.
• There are concerns over the veracity of the claimed reductions in poverty levels because the MPI estimates do not sufficiently account for the economic shock and the subsequent challenges faced by the Indian economy.

What problems does India’s MPI calculation face?

• Aggregation with Uniform Weighting: The MPI employs an aggregation with uniform weighting approach, which is akin to that of the UNDP Human Development Index. This indicates that while determining the total index, each dimension is given equal weight. This method may oversimplify the intricacies of poverty by failing to take into consideration the differing importance of various forms of deprivation.
• The national MPI may not accurately represent the diversity and heterogeneity of the nation because it employs uniform weights and cut-offs for all of the variables across all states and districts.
• For instance, the minimum four hours of electrical access per day is the deprivation cut-off, which could not be sufficient for certain areas or industries.
• Data Source Problems: The MPI uses information from the fourth and fifth National Family Health Surveys (NFHS). Opponents contend that the surveys lack sufficient detail to enable precise calculation, and the inconsistency of NFHS 5 with government assertions regarding open defecation raises doubts about the survey’s trustworthiness. The MPI uses the NFHS data despite these problems and fails to appropriately address the restrictions.
• Since NFHS is only conducted every five years, the MPI might not reflect the most recent changes in the nation’s poverty rate.
• Exclusion of Useful Data Sources: The MPI does not include household consumption expenditure data from the 75th Round of the National Sample Survey (NSS).
• Integrating data from the NFHS and NSS could yield a more accurate and thorough picture of multidimensional poverty. This pertinent data source isn’t included in the estimating procedure, though.
• Missing Dimensions: A number of significant aspects of poverty that may have an impact on the poor’s quality of life, including as social exclusion, discrimination, violence, insecurity, and environmental degradation, are not included in the national MPI.
• Furthermore, some of the indicators—such education and nutrition—might not be able to fully represent the multifaceted elements of these dimensions, like food quality and academic results.
• Intra-household inequalities: Because the national MPI employs the household as the unit of analysis, it may not be able to capture the intra-household inequalities in poverty, particularly the gender and age disparities.
• This could conceal the disparities in the hardships experienced by various household members, including women, kids, the elderly, and those with disabilities.

What Actions Are Needed to Enhance MPI Calculations?

• Adjustment for Income Fluctuations: You should think about putting in place a system to adjust for income fluctuations, especially in light of the sharp decline in State per capita income that coincided with a rise in MPI. This could entail applying smoothing methods or adding a lagged income variable in order to represent the long-term economic consequences.
• Impact of Dynamic Urban-Rural Migration: Recognise the influence of dynamic dynamics on urban areas, particularly in light of the Covid-19 pandemic’s reverse migration. Create a model that takes into account the shifting trends in rural-urban migration and how they affect MPI and living circumstances.
• Stress Education Spending: The elasticity of education is stronger (in absolute value) than that of health care, meaning that a 1% increase in education lowers MPI more than a corresponding increase in health care. Both health care and education spending are linked to lower MPI. An increase in MPI is expected since State-level estimations point to a decrease in educational spending.
• Mitigation of the Impact of Criminal MPs: Examine ways to lessen the impact, as there is a link between a higher MPI and the proportion of MPs with criminal cases. This may entail taking steps to improve openness, reduce corruption, and deal with the problems created by criminal elements in legislative bodies.
• According to a study, the MPI increased if the percentage of State MPs who had criminal charges topped 20%.
• Sensitivity Analysis: To evaluate the MPI model’s resilience, perform sensitivity analysis. This entails adjusting important parameters in order to comprehend how modifications to the inputs affect the outcome and offer insights into the stability and dependability of the MPI computations.
• Policy Suggestions: Make use of the results to guide policy suggestions that target the recognised causes of poverty. This could entail supporting steps to prevent corruption and criminal activity in legislative bodies, as well as policies that encourage income stability and targeted investments in healthcare and education.