Why is it in the News?

It is important to recognize India’s progress in alleviating poverty based on norms established in the 1970s. However, there is a need to update the poverty threshold to reflect contemporary notions of a ‘decent standard of living’.

Context:

• The recent release of the Household Consumer Expenditure Survey (HCES) data by the National Sample Survey Office (NSSO) has sparked renewed debates on poverty estimation in India.
• According to estimates derived from the methodologies proposed by the Tendulkar and Rangarajan Committees, poverty levels have reportedly decreased significantly since 2011-12, with figures standing at approximately 6.3% and 10% respectively for the year 2022-23.

Evolution of Consumption-Based Poverty Measurement in India:

• India's consumption-based poverty measurement has evolved since its inception in the 1960s.
• The approach was first introduced by the 1962 Working Group and further refined by the 1979 Task Force.

The 1979 Task Force:

• The task force defined the poverty line as the per-capita consumption expenditure level that could satisfy the average daily calorie requirement of 2,400 kcal in rural areas and 2,100 kcal in urban areas.
  • This included associated non-food expenditures, and the monetary value of this norm became the foundation for subsequent poverty line revisions.

Tendulkar and Rangarajan Committees:

• These committees revisited the calorie norms and expenditure levels established by the 1979 Task Force.
  • However, they did not adequately address non-food components.
  • The committees argued that if expenditures on specific necessities meet nutritional requirements, they should also cover other essential non-food needs.

Implications of the estimates:

• The estimates indicate a notable decrease in poverty since 2011-12.
• However, some commentators advocate for a reassessment of the poverty line in light of changes in survey methodology.
• They contend that recent alterations in survey methodology in the Household Consumer Expenditure Survey (HCES) make previous methodologies unsuitable for analyzing HCES data.
• Surjit S. Bhalla, an economist and former member of the Economic Advisory Council, has provided comprehensive counterarguments to such assertions.
• Nevertheless, none of these discussions have addressed the suitability of current methodologies for monitoring poverty using consumption data.

Historical Background of Poverty Measurement:

• Early approaches: Consumption-based poverty measurement was initiated with a Working Group established by the Planning Commission in 1962 and further refined by a task force in 1979.
  • This task force meticulously outlined its rationale for establishing a poverty line specific to India.
• Definition of poverty line: It was delineated as the per-capita consumption expenditure required to fulfil average daily calorie needs (2,400 Kcal in rural areas, 2,100 Kcal in urban areas) along with associated non-food expenses.
  • This average calorie standard was derived from an analysis of the demographic and activity-based composition of the population during that period.
• The monetary value assigned to this standard became the foundation for subsequent revisions of poverty lines.
  • However, the fundamental methodology underpinning this calculation was not critically reassessed.

Reevaluating Poverty Measurement Norms in India:

• The Tendulkar and Rangarajan Committees acknowledged the evolving demographic and activity composition in India, leading them to propose adjustments to calorie norms and expenditure levels for poverty measurement.
  • However, these committees did not adequately reconsider the non-food components of the poverty line.
• At the core of their argument was the assertion that if spending within an expenditure class is sufficient to meet nutritional requirements, it should also be adequate to cover associated non-food needs.
  • This assumption is questionable in the context of modern India, as the country has undergone significant changes since the 1970s when the Task Force on Poverty was established.

Demographic and Educational Indicators in India Since the 1970s:

Several significant demographic and educational shifts have taken place in India since the 1970s:

• Life Expectancy: Life expectancy at birth has improved from 49.7 years in 1970 to 69.4 years in 2018, reflecting advancements in healthcare and overall quality of life.
• Ageing Population: The proportion of individuals aged 60 and above has grown from 6.1% in the 1970s to 10.1% by 2021.
  • This shift highlights the need for policies and programs that address the unique needs of an ageing population.
• Primary Education: The Gross Enrolment Ratio (GER) in primary education has experienced substantial growth, increasing from 62% in 1971 to universal enrolment today.
  • This progress demonstrates a stronger emphasis on ensuring access to basic education for all children.
• Higher Education: The Gross Enrolment Ratio for higher education has also seen significant growth, rising from below 6% in the 1970s to approximately 28% in recent years.
  • This development signals a greater focus on providing opportunities for advanced education and skill development.

Implications of Demographic and Educational Changes in India:

• The demographic and educational shifts observed in India since the 1970s have significant implications for out-of-pocket expenditures on health and education, as revealed by National Sample Survey (NSS) data. Key implications include:

Education Expenditures:

• The increase in primary and higher education enrolment has led to stiffer competition for aspirational jobs.
• This competition has driven higher spending on private tuition, leading to 'education poverty' among families with young children.
• Addressing this issue requires a change in the approach to education, as outlined in the National Education Policy (2020).

Health Expenditures:

• The rise in life expectancy and an ageing population have led to increased health expenditure needs, especially among the elderly.
• Changes in household composition and a growing elderly population living independently have further highlighted the need for better healthcare provisions.
• Political parties, like the ruling party with its Ayushman Bharat promise, recognize the importance of addressing these healthcare needs, indicating a potential shift in policy focus.

Elderly Population and Household Composition:

• The increase in life expectancy and decline in mortality rates have created a more age-diverse population with a larger elderly population.
• This ageing population necessitates greater attention to healthcare and financial support for the elderly, an issue that may not be adequately captured by current poverty measurement norms.

Redefining Poverty Norms for a Changing India:

• Current consumption-based poverty measures capture average population attributes but face limitations when accounting for increased population heterogeneity.
• As the population structure evolves, using averages to describe poverty becomes problematic.
  • For example: Elderly households may meet nutrition expenditure requirements but struggle with healthcare costs.
• Households with young children might cover food needs yet face challenges in meeting aspirational education expenses, like private tuition.
• These examples highlight how households could surpass extreme poverty thresholds yet lack resources for a decent living standard.
  • Consequently, updating poverty norms is essential to accurately capture the realities of diverse household needs.
• To achieve the Sustainable Development Goal of eradicating poverty in all forms, India must redefine its poverty norms, moving away from outdated standards based on 1970s data.
• By establishing fresh norms for the Amrit Kaal, India can better address the unique challenges faced by its population and make significant strides towards alleviating poverty.

Conclusion

India has made remarkable progress in alleviating poverty based on existing norms. However, to ensure a decent living standard for all citizens, it is crucial to update poverty measures to reflect current demographic and economic changes. By acknowledging the evolving needs of its population, India can continue making significant strides in poverty reduction and work towards a more inclusive and prosperous future.

Why is it in the News?

Recently, there has been growing concern about the urban employment scenario for women in India, revealing a significant gap between the demand for employment and the opportunities available to urban women.

Context:

• According to the Periodic Labour Force Survey (PLFS), there has been a notable rise in women's workforce participation in India, increasing from 22% in 2017-18 to 35.9% in 2022-23.
• Despite this growth, India's female labor force participation rate (FLFPR) remains lower than the global average of 47% and lags behind countries like China, which boasts an FLFPR of 60%.
• While there has been progress, the FLFPR in India still presents a considerable gap.

Rural Areas:

• The FLFPR in rural areas has shown significant improvement, rising to 41.5% in 2022-23 from 24.6% in 2017-18.

Urban Areas:

• In urban regions, the FLFPR has also experienced growth, increasing to 25.4% in 2022-23 from 20.4% in 2017-18.
• However, women's employment rates in urban areas stood at 22.9% in the last quarter of 2023.

What is the Current Situation (Unmet Employment Demand)?

• The current landscape highlights a substantial unmet demand for employment among urban women, indicating a disparity between urban and rural areas.
• Urban areas exhibit a notably higher proportion of unemployed women actively seeking employment compared to their rural counterparts, with an unemployment rate of 9% in urban regions versus 4% in rural regions.

Two Categories of Unemployment:

• Unemployment manifests in two forms: individuals actively seeking employment and those desiring to work but not engaging in active job-seeking.
• Underutilized Potential: Approximately 25% of urban women have attained higher secondary education, a stark comparison to the 5% in rural areas, suggesting significant untapped potential.
  • The low urban employment rates among women underscore the underutilization of their skills and qualifications.
• Role of MGNREGA and Deendayal Antyodaya Yojana National Rural Livelihood Mission (DAY-NRLM): Initiatives like MGNREGA and DAY-NRLM have played a vital role in empowering women financially in rural settings, with over half of the MGNREGA workforce comprising women.
• However, urban settings present unique challenges. Social norms, safety concerns, and inadequate transportation options pose significant barriers to urban women's workforce participation.

Causes of Urban Unemployment Among Women in India:

• Challenges of Social Norms and Safety: Urban women face barriers to entering the workforce due to entrenched social norms, safety concerns, and inadequate transportation options, which hinder their participation.

• Gender-Based Occupational Segregation: Gender-based segregation of occupations and sectors persists in India, leading to limited growth in job opportunities for women and reduced participation rates.
• Economic Impacts: The rapid adoption of new technologies in response to the pandemic has resulted in widespread unemployment, particularly due to business closures and job losses.
  • This has widened the skill gap among job seekers.
• Population Growth: The increasing population and labor force contribute to rising unemployment in India, making it challenging for economic growth to keep pace with population expansion.
• Insufficient Investment: Insufficient investment in unorganized sectors, Micro, Small, and Medium Enterprises (MSMEs), and rural development exacerbates unemployment among women.
  • Combined with safety concerns and regressive social norms, this leads to underemployment or unemployment.
• Financial Burden: Calculations suggest that funding 150 days of work per year at ?500 daily wages would cost around 1.5% of the GDP. Factoring in material and administrative expenses, this could increase to around 2%.

Proposed Solutions:

• Government Initiatives: The government has introduced protective provisions in labor laws to ensure equal opportunities and a supportive work environment for women.
• Need for Women's Urban Employment Guarantee Act (WUEGA): To address urban unemployment comprehensively, there is a call for a national-level Women's Urban Employment Guarantee Act (WUEGA) and a Decentralized Urban Employment and Training Scheme akin to MGNREGA for rural women.

Vision for Women's Urban Employment Guarantee Act (WUEGA):

• The vision for WUEGA entails women comprising at least 50% (ideally 100%) of the program management staff, fostering gender inclusivity and empowerment at all levels of decision-making.
• Involving women and local communities in program management can enhance the constitutional principle of decentralization, ensuring grassroots participation and ownership.
• Every worksite under WUEGA would be equipped with essential facilities, including childcare services, to support working mothers and promote their participation in the workforce.
• Job opportunities provided by WUEGA will be accessible within a 5-km radius of each participant's residence, with free public transportation available for women, ensuring ease of access to employment opportunities

What are Some Key Urban Employment Initiatives?

• Atmanirbhar Bharat Rojgar Yojana (ABRY): Launched under Atmanirbhar Bharat package 3.0, ABRY incentivizes employers to create new employment opportunities while providing social security benefits and addressing employment loss during the Covid-19 pandemic.
• Pradhan Mantri Garib Kalyan Rojgar Abhiyaan (PMGKRA): PMGKRA focuses on providing immediate employment and livelihood opportunities to distressed individuals, emphasizing the creation of public infrastructure and livelihood assets in rural areas.
• Deendayal Antyodaya Yojana-National Urban Livelihoods Mission (DAY-NULM): DAY-NULM aims to alleviate poverty and vulnerability among urban poor households by facilitating access to self-employment and skilled wage employment opportunities.
• Women-led Waste Management: In Karnataka, women-led initiatives in waste management have empowered women to manage waste collection and drive 'Swacch' vehicles, leading to successful outcomes and increased acquisition of driving licenses among women.
• Pradhan Mantri Rojgar Protsahan Yojana (PMRPY): PMRPY incentivizes employers to generate new employment opportunities, thereby contributing to job creation across various sectors.
• National Career Service (NCS) Project: NCS offers comprehensive career-related services including job matching, career counseling, vocational guidance, and information on skill development courses, internships, and apprenticeships.
• PM Street Vendor’s AtmaNirbhar Nidhi (PM SVANidhi): PM SVANidhi provides collateral-free working capital loans up to ?10,000 with a one-year tenure to approximately 50 lakh street vendors, enabling them to restart their businesses post the Covid-19 lockdown.

Way Forward:

• Existing urban work opportunities, such as plantation and harvesting reeds on floating wetlands, should be expanded and tailored to local needs through inclusive community consultations.
• Introducing incentives, such as automatic inclusion in welfare boards, can serve as a mechanism to provide essential benefits like maternity entitlements, pensions, and emergency funds, thereby promoting economic security and social welfare for women.
• Closing gender gaps and empowering women align with the Sustainable Development Goals, underscoring not just ethical and constitutional obligations but also the potential for women's increased workforce participation to drive economic growth.
• Addressing societal norms and challenges that hinder women's workforce participation is crucial for fostering an inclusive and equitable work environment.
• Furthermore, the implementation of additional initiatives and policies aimed at promoting women's participation in the workforce will play a pivotal role in advancing the female labor force participation rate in India.

Why is it in the News?

The share of India’s population living in multidimensional poverty is estimated to have fallen to 11.28 per cent in 2022-23 from 29.17 per cent in 2013-14, according to a discussion paper released by NITI Aayog on Monday.

Context:

• According to the NITI Aayog’s discussion paper, multidimensional poverty in India declined from 29.17% in 2013-14 to 11.28% in 2022-23, with about 24.82 crore people moving out of this bracket during this period.
• The national multidimensional poverty measures simultaneous deprivations across three equally weighted dimensions of health, education, and standard of living that are represented by 12 sustainable development goals-aligned indicators, according to NITI Aayog.

Key Highlights of the MPI in India Since 2005-2006:

• Overall Decline in Multidimensional Poverty: As per the NITI Aayog discussion paper, India's multidimensional poverty has decreased from 29.17% in 2013-14 to 11.28% in 2022-23.
  • The trend signifies the upliftment of 24.82 crore people from this bracket during the specified period.
• State-wise Decline: "Uttar Pradesh registered the largest decline in the number of poor with 5.94 crore people escaping multidimensional poverty during the last nine years followed by:
  • Bihar at 3.77 crore
  • Madhya Pradesh at 2.30 crore and
  • Rajasthan at 1.87 crore.
• NITI Aayog's approach to measuring multidimensional poverty involved considering 12 indicators aligned with the sustainable development goals.
  • These indicators encompass crucial aspects such as nutrition, child and adolescent mortality rates, maternal health, educational attainment, access to basic amenities like clean cooking fuel, sanitation, safe drinking water, electricity, and housing, as well as possession of assets and bank accounts.
• "Significant initiatives covering all dimensions of poverty have led to 24.82 crore individuals escaping multidimensional poverty in the last 9 years.
  • As a result, India is likely to achieve its SDG target of halving multidimensional poverty well before 2030.
• The report emphasized impactful programs, such as Poshan Abhiyan and Anemia Mukt Bharat, which have markedly improved accessibility to healthcare services, significantly reducing deprivation.
  • Managing one of the globe's largest food security initiatives, the targeted Public Distribution System (PDS) under the National Food Security Act encompasses 81.35 crore beneficiaries, ensuring the distribution of food grains to both rural and urban populations.
• "The government's persistent dedication and resolute commitment to enhancing the lives of the most vulnerable and deprived have been instrumental in this accomplishment.

What is Multidimensional Poverty?

• Poverty can have several negative effects at once. Some of these include inadequate nutrition or health, a lack of power or clean water, low-quality employment, or insufficient education.
• The true nature of poverty cannot be fully captured by concentrating only on one aspect, such as income.
• Multidimensional Poverty, as a metric, goes beyond income or consumption alone.
  • It encompasses deprivations in education and access to essential infrastructure, considering factors beyond the monetary aspect.
  • The measurement is conducted at the $2.15 international poverty line, as defined by the World Bank (in 2017 purchasing power parity terms), ensuring a comprehensive assessment of poverty that extends beyond monetary value.

What is the National Multidimensional Poverty Index (MPI)?

• Prepared By: NITI Aayog
• Objective: The aim is to gauge poverty across various dimensions, complementing existing statistics based on per capita consumption expenditure.
• Purpose of the National MPI: Provides an enhanced, high-level overview of poverty at the national level.
  • Acts as a complement to monetary poverty measures.
  • Furnishes information crucial for shaping effective policy initiatives.
  • The MPI is founded on the individual or household profile of overlapping or "joint" deprivations experienced by each person.
• Key Features: Serves as an incentive for leaving no one behind and prioritizing the most marginalized.
  • Adaptable to the national context and maintains transparency.
• Credible Methodology: India's national MPI employs a methodology developed by the Oxford Poverty and Human Development Initiative (OPHI) and the United Nations Development Programme (UNDP), aligning with the globally accepted and robust standards used in the publication of the Global Multidimensional Poverty Index.
• Three Macro Dimensions: The National MPI is structured around three macro dimensions, each with specific indicators and weights, outlined below –

Significance of the MPI:

• Crucial Public Policy Instrument: The establishment of India's National MPI introduces a pivotal public policy instrument that monitors multidimensional poverty, facilitating evidence-based and targeted interventions to ensure inclusivity and prevent any individuals from being left behind.
• Assesses the Efficacy of Multi-Sectoral Interventions: It offers valuable insights into the effectiveness of multi-sectoral interventions aimed at addressing diverse facets of poverty.
• Encompasses Diverse Deprivations: Notably, functioning as a metric for multidimensional poverty, it captures the myriad and simultaneous deprivations experienced by households.
• Comprehensive Analysis Across All Tiers: This report conducts a thorough analysis of the headcount ratio and intensity of multidimensional poverty, encompassing national, State/UT, and district levels.

Conclusion

The National MPI in India has been conceived as an all-encompassing tool, expediting purpose-driven initiatives to gauge and systematically eliminate multidimensional poverty. The dimensions of the index have demonstrated their efficacy in identifying and facilitating precise policy interventions aimed at achieving targeted goals.

NITI Aayog

• NITI Aayog, established on January 1, 2015, succeeded the Planning Commission with a distinctive focus on a 'Bottom-Up' approach.
• Embracing the vision of 'Maximum Governance, Minimum Government' and echoing the ethos of 'Cooperative Federalism,' NITI Aayog serves as a dynamic institution facilitating collaborative decision-making.

Functional Components: NITI Aayog operates through two principal hubs-

• Team India Hub: This hub is a crucial interface, fostering effective communication and collaboration between the states and the central government.
  • It plays a pivotal role in aligning the diverse interests of different regions in the spirit of cooperative federalism.
• Knowledge and Innovation Hub: This hub is dedicated to enhancing the intellectual capacity of NITI Aayog. It functions as a think tank, driving innovative ideas and knowledge creation to inform policy decisions and contribute to the overall development agenda.

INDICES of NITI Aayog:

• Composite Water Management Index
• District Hospital Index
• Export Preparedness Index
• Global Innovation Index
• India Innovation Index
• Multidimensional Poverty Index
• School Education Quality Index
• SDG India Index
• State Energy Index
• State Health Index

Why is it in the News?

The Multidimensional Poverty Index exaggerates the National Democratic Alliance’s success in fighting deprivation.

Context:

• Some critics argue that the initiatives undertaken by the NDA Government might not have comprehensively tackled the various dimensions of poverty.
• Additionally, there are concerns that the reported successes may not be accurately reflected in the Multidimensional Poverty Index (MPI), raising questions about its alignment with the ground reality of deprivation.
• It is crucial to delve deeper into the methodologies and criteria employed for measuring multidimensional poverty to ascertain their resonance with the nuanced challenges confronted by the population.
• Nobel Laureate Amartya Sen introduced a comprehensive and innovative outlook on well-being, emphasizing capabilities and functionings, commonly referred to as the capability approach.

What is Amartya Sen's Capability Approach?

• Amartya Sen's Capability Approach serves as a normative framework for assessing individual well-being and societal arrangements.
  • Rather than focusing on happiness, preferences, or resources, this approach directs attention to the genuine opportunities and freedoms available to individuals in realizing lives aligned with their values.
• Sen's Capability Approach comprises two central elements: functionings and capabilities.
  • Functionings represent valuable states of being and doing that an individual can attain, such as good health, education, or social engagement.
  • On the other hand, capabilities encompass the array of alternative functionings that individuals can choose from within their personal and social contexts.
• Illustratively, an individual's capability may extend to being either well-nourished or undernourished, contingent upon factors like access to food and dietary choices.
  • Sen argues that the capability approach offers a superior means of evaluating human welfare compared to other approaches like utilitarianism and resourcism, which he deems either excessively narrow or insufficiently attuned to the diversity and intricacy of human experiences.
• Utilitarianism centres around choices leading to the greatest happiness or satisfaction of desires, while resourcism concerns the distribution of resources like income, wealth, or goods in society.
  • According to Sen, the ultimate aim of development should be the expansion of people's capabilities, surpassing mere considerations of income or utilities.
• Amartya Sen's Capability Approach has notably influenced the development of the Human Development Index.

What is the Human Development Index (HDI)?

• HDI serves as a statistical tool employed to assess a country's overall achievements across its social and economic dimensions.
• It is published by the United Nations Development Programme (UNDP).
• It stands as the second most widely utilized indicator for gauging economic progress, following national income statistics (GDP).
• Components: HDI comprises three key components, namely:
  • Health, quantified by life expectancy at birth;
  • Education, determined by a combination of mean years of schooling and expected years of schooling; and
  • Income, evaluated by gross national income per capita (at purchasing power parity).
• Calculation: The ultimate score is computed as a geometric mean of the aforementioned three categories.

What is the Multidimensional Poverty Index (MPI)?

• The Multidimensional Poverty Index (MPI) is an indicator of poverty that takes into account diverse aspects of well-being, extending beyond monetary considerations or income alone.
  • Originating from collaborative efforts between the Oxford Poverty and Human Development Initiative (OPHI) and the United Nations Development Programme (UNDP), the MPI offers a holistic perspective on poverty by incorporating a range of factors contributing to deprivation.
• In assessing poverty, the MPI identifies individuals or households as multidimensionally poor when they experience deprivation across various indicators within different dimensions of well-being.
  • These dimensions typically encompass health, education, and the standard of living. By considering a broad spectrum of factors, the MPI provides a nuanced and comprehensive understanding of poverty.

What is the Current Status of MPI?

• Regrettably, the United Nations Development Programme (UNDP) has chosen to adopt a capabilities-based approach to formulate a comprehensive measure of human development, applying uniform weights to the three primary components—health, education, and standard of living, along with their sub-indices.
  • Under this methodology, both NITI Aayog and the UNDP recently released the National Multidimensional Poverty Index/MPI: A Progress Review 2023, mirrored in the UNDP Report titled "Making Our Future: New Directions for Human Development in Asia and the Pacific," unveiled on November 7, 2023.
• However, these reports share the same shortcomings as the UNDP human development index, primarily stemming from aggregation with uniform weighting.
  • Notably, the narrative surrounding the MPI exacerbates these distortions.
• Surprisingly, the MPI 2023 estimates indicate a nearly halved value for India's national MPI and a decline from 24.85% to 14.96% between 2015-16 and 2019-21.
  • This substantial reduction of 9.89 percentage points suggests that approximately 135.5 million people have transitioned out of poverty during this period.
  • Furthermore, the intensity of poverty, gauging the average deprivation among individuals in multidimensional poverty, decreased from 47.14% to 44.39%.

Why the Reduction in MPI Numbers Under the Current Government is Flawed?

• Misleading and Inadequate Information: The MPI relies on data from the National Family Health Surveys (NFHS) 4 and 5, which are deemed insufficiently detailed for accurate estimation.
  • NFHS 5 data is particularly questionable due to its suppression, driven by discrepancies in open defecation estimates conflicting with official claims of complete elimination.
• Ideally, combining NFHS 4 and 5 with the 75th Round of the National Sample Survey (NSS) on household consumption expenditure should have provided a more comprehensive perspective.
  • However, this was abandoned as leaked poverty estimates indicated an increase.
• Compounding the skepticism is the profound impact of the COVID-19 pandemic in 2020-21.
  • The widespread loss of livelihoods, reverse migration fatalities, and inadequate access to vaccines and medical care have created a substantial economic shock, hindering India's recovery.
  • For instance, GDP growth has plummeted from 8% in 2015-16 to 3.78% in 2019-20, reaching -6.60% in 2020-21, along with a decline in per capita income.
• Greater Emphasis on Covariates: Comparing the elasticities of MPI with respect to various covariates reveals that the most significant reduction in MPI is attributed to higher State per capita income.
  • However, due to a drastic income decrease, MPI has spiked.
  • Urban location follows in importance, with a 1% increase resulting in a 0.90% MPI increase, reflecting the challenges associated with rural-urban migration and the growth of substandard living conditions.
• Both healthcare and education expenditure are associated with lower MPI, with education exhibiting a higher elasticity, implying that a 1% increase in education spending reduces MPI more than a comparable increase in healthcare spending.
• Reduction Between 2015 and 2019-21 is Considerably Lower than the Official Estimate:
  • Some research suggests that the reduction between 2015 and 2019-21 is notably lower than the official estimate, standing at 4.7 percentage points compared to the reported 9.89 percentage points.
  • A selective review of MPI estimates indicates a rise in poverty in Uttar Pradesh, India's most populous state, by over seven percentage points.
  • Among states that went to elections in November (Chhattisgarh, Madhya Pradesh, Mizoram, Rajasthan, and Telangana), MPI fell in Chhattisgarh (by over six percentage points), in Rajasthan (by two percentage points), and significantly in Madhya Pradesh (by about eight percentage points).

What Steps Should Be Taken in order to Improve MPI?

• Adjustment for Income Variability: Given the substantial decrease in State per capita income causing a surge in MPI, consider implementing mechanisms to account for income fluctuations.
  • This may involve incorporating smoothing techniques or introducing a lagged income variable to capture economic effects over time.
• Dynamic Impact of Urban-Rural Migration: Recognize the dynamic impact of factors on urban locations, particularly in light of the reverse migration during the COVID-19 pandemic.
  • Develop models that reflect changing patterns of rural-urban migration and its influence on living conditions and MPI.
• Focus on Education Expenditure: Emphasize expenditure on education, as both healthcare and education spending are linked to lower MPI.
  • Notably, the elasticity of education is higher, suggesting that a 1% increase in education reduces MPI more than a comparable increase in healthcare.
  • Given the reported decline in state-level educational expenditure, a rise in MPI is likely.
• Mitigate the Impact of MPs with Criminal Cases: Recognize and address the correlation between the share of Members of Parliament with criminal cases and higher MPI.
  • Explore strategies such as policy initiatives to curb corruption, enhance transparency, and address challenges posed by criminal elements within legislative bodies.
  • Studies indicate that when the share of MPs with criminal cases exceeds 20%, the MPI tends to be higher.
• Sensitivity Analysis: Conduct sensitivity analyses to test the robustness of the MPI model.
  • This involves varying key parameters to understand how changes in inputs impact results, providing insights into the stability and reliability of MPI calculations.
• Policy Recommendations: Utilize findings to inform policy recommendations targeting identified drivers of poverty.
  • Advocate for policies promoting income stability, targeted investments in education and healthcare, and measures to combat corruption and criminality within legislative bodies.

Conclusion

Enhancing the Multidimensional Poverty Index (MPI) requires proactive measures. Addressing income variability, acknowledging dynamic urban-rural migration impacts, and prioritizing education expenditure is pivotal. Additionally, mitigating the influence of MPs with criminal cases and conducting sensitivity analyses for robustness are essential steps. Ultimately, informed policy recommendations should focus on promoting income stability, targeted investments in education and healthcare, and tackling corruption to advance holistic poverty reduction strategies.

Why in the News?

• In a recent assessment of multidimensional poverty conducted by NITI Aayog, the findings reveal a decline in the poverty rate from 25% in 2015-16 to 15% in 2019-21.
• Notably, approximately 135 million individuals were elevated from impoverished conditions within this timeframe.
• Consequently, it becomes imperative to scrutinize the methodologies underpinning the multidimensional poverty index and ascertain the continued pertinence of poverty evaluations based solely on consumption.

What is Multidimensional Poverty?

• Traditionally, poverty has been predominantly gauged through single-dimensional indicators, frequently revolving around income levels.
• Multidimensional poverty, in contrast, comprehensively addresses the array of deficiencies that individuals grappling with poverty encounter in their daily existence.
• These deficiencies encompass compromised health, limited access to education, insufficient living conditions, disempowerment, subpar employment situations, exposure to violence, and inhabiting environmentally precarious regions, among other factors.

Distinguishing Between Consumption-Based and Multidimensional Poverty Indices (MPI):

• Poverty gauges based on consumption levels primarily address one aspect of deprivation, without accounting for other dimensions.
• The global MPI encapsulates both the extent of multidimensional poverty (the percentage of individuals within a population grappling with multidimensional poverty) and its depth (the average number of deprivations experienced by each impoverished individual).
• Nonetheless, it's important to recognize that while Multidimensional poverty estimates offer valuable insights, they are not intended to replace the National Sample Survey (NSS) consumption-based poverty ratios.

Recent Findings on Multidimensional Poverty in India:

• Within a mere 15-year span, from 2005/2006 to 2019/2021, a striking 415 million individuals in India have successfully transcended poverty, showcasing a commendable accomplishment by the world's most populous nation.
• These revelations stem from the latest global release of the Multidimensional Poverty Index (MPI) by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) based at the University of Oxford.
• The global MPI stands as a comprehensive measure of poverty, encapsulating the various deprivations that underprivileged individuals encounter in the realms of education, health, and living conditions.
• In the year 2005/2006, approximately 645 million people grappled with multidimensional poverty in India; this figure dwindled to around 370 million in 2015/2016 and further down to 230 million in 2019/2021.

Challenges Associated with the Multidimensional Poverty Index:

• The Global MPI's Findings Are Not a Novel Revelation
• The application of the Global MPI as a conclusive breakthrough isn't unprecedented.
• Poverty estimations grounded in consumer spending and utilizing the Tendulkar committee's framework revealed a reduction of 137 million individuals living in poverty between 2004-05 and 2011-12, despite population growth.
• Following the Rangarajan Committee's methodology, the decrease from 2009-10 to 2011-12 stands at 92 million, equating to an annual reduction of 46 million.
• Myriad Indicators to Consider
• Given the extensive array of dimensions and indicators, it becomes imperative to engage in meticulous asset mapping to pinpoint the most pertinent parameters.
• However, owing to the abundance of indicators spanning multiple dimensions, the accurate evaluation, and consequent effective policy implementation might pose challenges.
• Aggregating Across Indicators
• This presents yet another predicament, as these indicators should ideally remain independent of each other.
• For instance, indicators like access to clean drinking water should not be aggregated with factors such as child mortality."

Challenges Regarding Consumption Expenditure Surveys:

• After 2011-12, there is a lack of accessible official records on consumer expenditure, which inhibits the capacity to juxtapose it with trends in the multidimensional poverty index.
• The survey outcomes about consumption expenditures from 2017-18 have not been officially disseminated.
• Given the dearth of this information, various studies have attempted to investigate poverty utilizing indirect techniques along with resources such as the Centre for Monitoring Indian Economy (CMIE) and the Periodic Labour Force Survey (PLFS). These endeavors, however, have yielded disparate findings.

Approaches to Tackle MPI Challenges:

• Integration of MPI with Consumption-Based Poverty Assessments
• Examining the advancements in non-monetary aspects like education, healthcare, sanitation, access to clean water, and child survival alongside income or consumption poverty can provide valuable insights.
• However, the conversion of all these variables into a single index introduces complexities.
• Incorporating Public Services as an Additional Dimension: Regarding multidimensional matters, considering public services as an independent dimension, beyond consumption, can yield more comprehensive results.

Reforming Consumption Expenditure Surveys: A Necessity

• Enhancing Data Collection Practices
• An imperative concern revolves around the disparities observed in aggregate consumption estimations between National Accounts Statistics (NAS) and National Sample Survey (NSS) data.
• It's worth noting that across all nations, the NSS and NAS consumption estimates invariably diverge – a trend India also follows.
• What is particularly perplexing is the escalating dissimilarity in India between NSS and NAS consumption figures over the years.
• From a divergence of under 10% in the late 1970s, this gap has widened to a substantial 53.1% in 2011-12. This disparity is too significant to disregard.
• The National Statistical Office ought to meticulously analyze this issue and proffer potential solutions to enhance data collection through both avenues.
• Moreover, it's essential to complement the outcomes of consumption surveys with an evaluation of the impact of public expenditure on the health and education of distinct expenditure strata.

A prevalent perception associates wealth or destitution with high or low-income levels. The insufficiency of income is mirrored in numerous non-income poverty indicators.

Defining poverty in terms of income, or when income data is lacking, in terms of expenditure, appears to be the most pertinent approach, and this methodology is prevalent across the majority of countries.