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The Reliability of Poverty Survey Data


Based on the fourth and fifth rounds of the National Family Health Survey (NFHS) data, the Poverty Ratio (Head Count Ratio) in Tamil Nadu decreased from 4.89% in 2015-16 to 1.57% in 2020-21.


GS Paper 2: Population and associated issues, poverty and developmental issues, urbanization, their problems and their remedies.

Mains Question

“The poverty line in India does not allow for a comfortable existence, but it does allow for an existence above subsistence.” Examine the concept of the poverty line in India critically. (250 words)

The Burning Question

  • The flames Based on the previous four rounds of NFHS databases, academics have questioned the quality of NFHS data for a variety of reasons.
  • Such queries may also be directed at the NFHS 5 database. But first, let us look at the poverty statistics derived from NFHS 5 data using the NITI Aayog’s multidimensional poverty measurement and policy intervention pointers.
  • Following that, we will raise concerns about the quality of NFHS data, with the goal of using it with caution and improving data quality with the future in mind.

According to the MPI

  • NITI Aayog estimated the Multidimensional Poverty Index (MPI) and published the baseline report in 2021, armed with a fairly large sample survey data of NFHS 4 (with more than six lakh households in India).
  • The MPI was founded on the idea that poverty is the result of simultaneous deprivation in multiple functions, such as health, education, and standard of living.
  • The NITI Aayog identified 12 indicators in these three sectors and calculated the weighted average of deprivation for all men and women surveyed in NFHS 4. Individuals were considered multidimensionally poor if their aggregate weighted deprivation score was greater than 0.33.
  • The non-poor may also be deficient in a few of these indicators, but not to the extent that they are classified as multidimensionally poor.
  • The Poverty Ratio or Head Count Ratio is defined as the proportion of the population with a deprivation score greater than 0.33 to the total population.
  • The authors estimated the MPI and its components for Tamil Nadu using NFHS 5 and compared them to NITI Aayog’s estimates based on NFHS 4.
  • Another intriguing aspect of this approach is the estimation of Poverty Intensity. This is the multidimensionally poor’s weighted-average deprivation score.
  • For example, in Tamil Nadu, the Intensity of Poverty decreased from 39.97% to 38.78% during this period, indicating that the summary measure of multiple deprivations of the poor has only marginally decreased in these five years, which must be highlighted for policy focus.
  • The MPI is calculated using the Head Count Ratio and the Intensity of Poverty. Tamil Nadu’s MPI fell from 0.020 to 0.006.
  • This sharp decline in the MPI is largely due to a greater decline in the Head Count Ratio when compared to the Intensity of Poverty. This suggests that any further decline in MPI in Tamil Nadu should be achieved only by addressing all dimensions of poverty and significantly reducing its intensity across the state.

Interventional Strategy

  • The estimation of deprivation also shows that the overall population that has been identified as deprived in most of the indicators individually is larger than the population that has been identified as multidimensionally poor.
  • This emphasises the point that people may be severely deprived in a few functions but not be multidimensionally poor.
  • This adds another dimension to public policy intervention, namely, combating poverty in Tamil Nadu should be multidimensional as well as universal.
  • Only this method can address deprivation across all indicators. This will undoubtedly and directly reduce the level of poverty in Tamil Nadu.
  • The Head Count Ratio and Intensity of Poverty can be calculated statistically for each district and separated by gender, rural/urban, and other dimensions.
  • As a result, the MPI and its components are extremely useful in terms of understanding poverty in its entirety as well as the granular details required for sectoral and spatial policy and programmatic interventions.
  • The MPI’s strength as a tool for data-driven public policy is dependent on the quality of survey data, specifically the NFHS data.

NFHS data quality

  • In academia, there has been much discussion about the quality of survey data. Since its inception in the 1950s, the National Sample Survey Organisation’s (NSSO) sample surveys have sparked debate among economists and statisticians, both in terms of sampling and non-sample errors.
  • Following several review reports on the NSSO’s methodologies, the NSSO has attempted to improve sampling design and reduce non-sampling errors, particularly with regard to recall periods for providing household consumption expenditure. All of this is well documented.
  • Substitution of dry rations for hot meals in mid-day meal programmes, as well as high hospital pressures in handling COVID-19 cases are expected to increase deprivation in nutrition and maternal health in the post-lockdown period, contrary to the decline in deprivation in nutrition and maternal health that we derived from this database in the post-pandemic period.
  • Tamil Nadu is known for increasing enrolment and decreasing dropout rates year after year; thus, the increase in deprivation in terms of education should raise concerns.
  • In terms of school attendance, we don’t know how parents interpreted school attendance during the long period of school closure during the lockdown.
  • As a result, when interpreting statistics derived from the entire database, combined survey data from two different time periods separated by a major pandemic must be approached with caution.
  • Assuming that the survey data are from a single time period, it is normal to compare the results of specific indicators from the survey data with programmatic data derived from official records.
  • There are claims that the deprivation indicators for drinking water and sanitation in Tamil Nadu are higher than the claims made by the respective State government departments. These kinds of issues are common in survey data.
  • For example, consumption expenditure on foodgrain derived from NSSO data would not agree with the System of National Accounts’ estimation of food consumption.

Data utilisation and quality

  • For various (well-founded) reasons, the quality of survey data has always been a contentious issue in academic and policy debates.
  • However, this has not prevented academics and policymakers from deducing policy directions because such data at a reasonably aggregate level (say, at the level of a State) should be useful.
  • As previously stated, the sharp decline in Head Count Ratio and a marginal decline in Intensity of Poverty in NFHS 5 compared to NFHS 4 in Tamil Nadu cannot be ignored.
  • From this, we can conclude that in order to reduce the Intensity of Poverty, we must address deprivation across the entire population, implying that a universal approach, rather than a targeted approach, is required.
  • The survey data only provides broad policy recommendations, whereas programmatic interventions should be informed by ground-level realities.
  • Simultaneously, ongoing engagement with survey data in terms of improving sample design and response quality must be maintained. Analyzing data and identifying inconsistencies in inferences from different databases on a given issue would aid in the improvement of data collection systems. Allow us to continue using survey data to draw policy conclusions (with caution) and to help improve data quality.

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September 2022