A recent report by NITI Aayog sheds light on India’s progress in combating multidimensional poverty. The report highlights a significant reduction in poverty rates over the years, emphasizing the importance of understanding the multidimensional poverty index (MPI) and its implications
About Multidimensional Poverty
Traditional poverty measurement has been centered on a single dimension, typically income. However, multidimensional poverty acknowledges the complex web of deprivations that impact the lives of the poor. This includes factors like health, education, living standards, disempowerment, work quality, exposure to violence, and living in hazardous environments.
Recent Reports on India’s Progress
NITI Aayog’s report highlights a remarkable reduction in poverty from 25% in 2015-16 to 15% in 2019-21, lifting approximately 135 million people out of poverty. The global Multidimensional Poverty Index (MPI), co-released by UNDP and OPHI, further underscores India’s achievement by indicating that 415 million people escaped poverty between 2005/2006 and 2019/2021. The MPI gauges deprivations in education, health, and living standards, revealing a decline from 645 million to 230 million in multidimensional poverty within India.
Consumption-Based vs. Multidimensional Poverty Indices
While consumption-based poverty measures offer insights into income-related deprivation, they fall short in capturing other dimensions of poverty. The MPI, on the other hand, accounts for both the extent and intensity of multidimensional poverty, providing a comprehensive view of deprivations faced by the poor.
Challenges Associated with the MPI
- Ambiguity in Breakthrough: Despite MPI’s significance, it’s essential to note that the reduction in poverty estimates based on consumer expenditure data and methodologies like the Tendulkar and Rangarajan committees challenges the notion of the MPI as a novel breakthrough.
- Complexity from Indicators: The plethora of dimensions and indicators involved in the MPI necessitates effective asset mapping for accurate assessment. However, the sheer volume of indicators can hinder policy implementation and outcome effectiveness.
- Indicator Aggregation: Aggregating indicators can lead to issues when dimensions are not independent. For instance, combining access to safe drinking water with child mortality may distort accurate insights.
- Data Gaps: The absence of official post-2011-12 consumption expenditure data for comparison with MPI trends poses challenges. Varied conclusions from studies using indirect methods and alternative data sources highlight the need for comprehensive and consistent data.
Addressing MPI Challenges
- Combined Analysis: To enrich poverty assessment, analyzing MPI alongside consumption-based poverty estimates can offer a holistic perspective on both income and non-income aspects of poverty.
- Including Public Services: Treating public services as a distinct dimension beyond consumption can provide a more comprehensive view of poverty and contribute to effective policy formulation.
- Enhancing Data Collection: Addressing discrepancies between National Accounts Statistics (NAS) and NSS data in terms of consumption estimation is vital. The National Statistical Office must examine this issue and propose strategies for improved data collection.
UPSC Mains Questions
- How does the multidimensional poverty index differ from traditional income-based poverty measurements, and why is it crucial to consider both approaches?
- What are the key findings from NITI Aayog’s recent report on India’s multidimensional poverty, and how do they align with global indicators?
- Discuss the challenges associated with aggregating indicators within the multidimensional poverty index. How might this aggregation impact policy formulation?