Question on Credibility of the Survey Data

Question on Credibility of the Survey Data

Question on Credibility of the Survey Data


  • Academics have questioned the quality of NFHS data for various reasons, based on the previous four rounds of NFHS databases.
  • Similarly, such questions have been raised against the NFHS 5 database also.

Multidimensional Poverty Index (MPI) –

  • NITI Aayog estimated the Multidimensional Poverty Index (MPI) and published the baseline report in the year 2021.
  • The NITI Aayog identified 12 indicators in three broad sectors including education, health and standard of living.
  • It quantified the deprivations to all men and women across the 12 indicators which was surveyed in NFHS 4.
  • If an individual’s aggregate weighted deprivation score was more than 0.33, they were considered multidimensionally poor.

What is the Poverty Ratio or Head Count Ratio?

  • The proportion of the population with a deprivation score greater than 0.33 to the total population is defined as the Poverty Ratio or Head Count Ratio.
Other Aspects –
  • The estimates suggests that a huge section of the population have been identified to be deprived in different indicators individually than being identified as multidimensionally poor.
  • So, this is an additional challenge to the public policy makers who have to go into the granular details before making any public policy intervention.
  • Statistically, the Head Count Ratio and Intensity of Poverty can be calculated for each district and segregated by gender, rural and urban, and other dimensions.
  • Therefore, the usefulness of the MPI and its components is enormous in terms of understanding poverty in its totality as well as the granular details that are essential for sectoral and spatial policy and programmatic interventions.
Debate over the quality of the data –
  • The National Sample Survey Organisation’s (NSSO) sample surveys have always been subjected to criticism since the 1950’s.
  • Several economists and statisticians have been raising apprehensions in the errors in both sampling and non-sampling data.
  • They tested, for instance, the arbitrariness in reporting the age of the dead, differences in data quality between educated and uneducated respondents, data quality based on differences in time taken to complete a survey of different household types, etc.

Source The Hindu

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