Why in the News?
The government recently published a factsheet on the 2023-24 Household Consumption Expenditure Survey (HCES), highlighting a reduction in poverty levels across both urban and rural areas.
What are the key findings of the report?
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What are the concerns related to the HCES data?
- Incomparability of Data Sets: The methodology used in the 2022-23 HCES differs significantly from previous surveys, making it difficult to compare results over time. The introduction of a “modified mixed reference period” complicates direct comparisons with earlier data collected under different methodologies.
- Sampling Bias: There are concerns that the survey may not adequately represent extremely poor households, leading to an overestimation of average expenditures. This bias could arise from changes in sampling strategies that favor more affluent households.
- Data Availability Issues: The absence of recent and reliable consumption data prior to the 2022-23 survey has led to a reliance on outdated estimates, which may not accurately reflect current poverty levels. The last comprehensive survey before this was conducted in 2011-12.
What does the consumption pattern tell about poverty in rural and urban regions?
- Divergent Poverty Levels: The average monthly per capita expenditure (MPCE) indicates significant disparities between rural and urban areas, with rural areas averaging Rs 4,122 and urban areas Rs 6,996 in 2023-24. This suggests that urban populations generally have higher consumption levels.
- Survival on Minimal Incomes: Reports indicate that a substantial portion of India’s population survives on less than Rs 100 per day, highlighting persistent poverty despite claims of decline. This raises questions about the adequacy of the poverty line used for estimation.
What are the criticisms faced by the Multidimensional Poverty Index?
NITI Aayog, the policy think tank of the Government of India, adopted the Multidimensional Poverty Index (MPI) as a measure to evaluate poverty in India.
- Methodological Concerns: Critics argue that the MPI’s reliance on equal weighting for health, education, and living standards may oversimplify the complexities of poverty. The selection of indicators can significantly influence outcomes, potentially leading to biased representations.
- Dynamic Nature of Poverty: The MPI may not adequately capture the fluidity and changing nature of poverty over time, as it relies on static indicators that do not reflect immediate economic conditions or shocks such as those experienced during the COVID-19 pandemic.
- Political Implications: Some economists suggest that using MPI as a primary measure for poverty could be politically motivated, aiming to present favourable statistics while ignoring deeper economic issues such as stagnant real wages and rising inequality.
Way forward:
- Strengthen Data Collection: Conduct regular and comprehensive Household Consumption Expenditure Surveys (HCES) to ensure updated and accurate poverty assessments. This will bridge data gaps and provide a more reliable basis for policy decisions.
- Refine Poverty Metrics: Combine the Multidimensional Poverty Index (MPI) with traditional consumption-based measures to capture a holistic and dynamic picture of poverty, accounting for region-specific and pandemic-induced challenges.
Mains PYQ:
Q “The incidence and intensity of poverty are more important in determining poverty based on income alone”. In this context analyse the latest United Nations Multidimensional Poverty Index Report. (UPSC IAS/2020)
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