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Source– The post is based on the article “Consumption-based poverty estimates have relevance” published in “The Hindu” on 16th August 2023.
Syllabus: GS2- Issues related to Poverty
Relevance: Measurement of poverty
News– A recent report by NITI Aayog on multidimensional poverty shows that the percentage of the poor has gone down from 25% in 2015-16 to 15% in 2019-21 and around 135 million people were lifted out of poverty during this period.
What is the comparison of poverty estimation of Multidimensional Poverty Index and survey based on consumption expenditure?
As per the report of the Global Multidimensional Poverty Index 2018, India has made significant progress in reducing multidimensional poverty.
The prevalence of multidimensional poverty witnessed a reduction of nearly 50% between 2005-06 and 2015-16. It dropped to 27.5%. In the last decade, the count of poor individuals in India decreased by over 271 million.
The calculations of poverty based on methodology recommended by the Tendulkar committee reveal that the number of individuals living in poverty decreased by 137 million between 2004-05 and 2011-12.
According to the approach suggested by the Rangarajan Committee, the reduction between 2009-10 and 2011-12 amounts to 92 million. It means an annual reduction of 46 million. Over a decade, this reduction surpasses the global Multidimensional Poverty Index.
In terms of absolute figures, the poverty rates calculated using the Tendulkar and Rangarajan Committee methods are lower than those estimated by the global MPI.
What are issues with the multidimensional poverty index?
As per Expert Group to review the Methodology for Measuring Poverty (2014), there are issues regarding the use of multiple indicators.
It is due to the various complexities like measurement challenges, the need for aggregation across indicators, and the availability of suitable data at reasonably frequent intervals.
These matters should be scrutinized and evaluated. For example, the child mortality indicator is calculated for a population group rather than individual households. So, aggregation of the 2 types of data is difficult.
Ideally, these indicators should remain distinct. For instance, an indicator like access to safe drinking water shouldn’t be combined with an indicator like child mortality.
It’s entirely possible to analyse the non-income indicators like education, health, sanitation, alongside income or consumption poverty. But transforming all these indicators into a single index presents numerous challenges.
For most individuals, wealth or poverty is associated with income levels. The various non-income poverty indicators fundamentally reflect insufficient income.
Defining poverty in terms of income or, when income data is unavailable, in terms of expenditure, appears most suitable. This is the method followed by most countries.
Official data on consumer expenditure beyond 2011-12 is lacking. It makes it difficult to compare trends with the multidimensional poverty index. The consumption expenditure survey conducted in 2017-18 has not been officially released.
In the absence of such data, several studies have been conducted on poverty using indirect methods and data sources like the CMIE and PLFS. It results in varying conclusions.
What should be done?
Past surveys have clearly demonstrated a substantial decrease in the poverty ratio during periods of robust economic growth. However, in recent years, including the period affected by the COVID-19 pandemic, the growth rate has diminished.
It’s reasonable to suspect that the rate at which the poverty ratio is decreasing may have slowed down.
The current consumption expenditure survey is going on. To ensure consistency for comparison purposes, it’s advisable to adopt a single methodology. Hence, awaiting the publication of the survey results is the most prudent course of action.
It is crucial to complement the outcomes of consumption surveys with an examination of the influence of public expenditure on health and education.
The disparity in aggregated consumption estimates between the National Accounts Statistics (NAS) and the NSS data. Such disparities are not unique to India.
This discrepancy in India has been widening over time. It has escalated to 53.1% in 2011-12.
The National Statistical Office should investigate this issue and propose potential measures to enhance data collection through both avenues.
