Analysing poverty levels in India by comparing various surveys, Pg9

Key Highlights:

• New study suggests India’s poverty fell significantly between 2004–05 (37%) and 2011–12 (22%), and further to 18% in 2022–23.
• Uses survey-to-survey imputation: a statistical method to fill gaps in one dataset using a related base survey.
• Estimates are from the paper “Poverty Decline in India after 2011–12: Bigger Picture Evidence” by Himanshu et al., using consumption and employment datasets due to absence of an official poverty line after 2011–12.

Detailed Insights:

Three Methodologies Used:

1. HCES-based Consumption Estimation:
   • Most common method; last official Household Consumption Expenditure Survey (HCES) was 2011–12.
   • Recent gaps are filled using proxy surveys like NSSO’s 2014 food expenditure data or private consumption data from national accounts.
2. Private Final Consumption Expenditure (PFCE) Model:
   • Based on GDP component PFCE and National Accounts Statistics.
   • Extrapolates poverty decline from PFCE growth post-2011–12.
3. Survey-to-Survey Imputation (PLFS–CPHS match):
   • Uses Consumer Pyramids Household Survey (CPHS) from CMIE and Periodic Labour Force Survey (PLFS).
   • Matches employment and consumption behavior using econometric modelling.

Key Findings:

• Poverty fell by 18% in 2022–23, using the best estimate.
• The EUS-imputed method (combining PLFS & CPHS) shows 13.5% poverty in 2022.
• Sectoral decline: Greatest drop seen in non-agriculture employment, with urban areas seeing faster decline.
• Wage data (WRRI) show rural real wages grew slower post-2012, which reflects moderate poverty alleviation in some backward states.

Limitations Highlighted:

• No official poverty line after 2011–12 → all estimates are proxy-dependent.
• Survey tools differ in design, sampling, and recall periods → non-comparable across years.
• Several surveys show inconsistent trends (e.g., PLFS vs CMIE).
• Data gaps weaken policy debates on targeting subsidies, employment schemes, or welfare transfers.

Key Concepts:

• Imputation: Statistical technique for filling missing data using values from related datasets.
• PFCE: Indicator of household consumption used to estimate living standards when direct consumption surveys are unavailable.
• PLFS: Periodic Labour Force Survey – India’s primary source of employment data post-2017.

Significance:

• Reinforces the urgency for restoring regular HCES to ensure data-driven welfare.
• Highlights how multi-source triangulation can give a clearer picture of poverty in the absence of official data.
• Informs debates on inequality, subsidy targeting, and policy effectiveness under schemes like PM-GKAY or MGNREGA.

Mains Mock Question:

With no official poverty data since 2011–12, researchers have turned to imputation methods and alternate datasets. Critically examine the reliability of such estimates and the implications for welfare policymaking in India.