Survey data on poverty and broad policy pointers


  • Based on multidimensional poverty measurement, the Poverty Ratio (Head Count Ratio) in Tamil Nadu declined from 4.89% in 201516 to 1.57% in 202021, based on the fourth and fifth rounds of the National Family Health Survey (NFHS) data.
  • Academics have questioned the quality of NFHS data for various reasons, based on the previous four rounds of NFHS databases.

What is multi-dimensional poverty:

  • The single economic criterion-based definition of poverty (usually income or expenditure) that led to the concept of Poverty Line, was later criticised by some economists for being a lopsided and incomplete view of deprivation or poverty.
  • Multidimensional poverty is based on the idea that poverty is not unidimensional (not just depends on income and one individual may lack several basic needs like education, health etc.), rather it is multidimensional.

Global Multidimensional Poverty Index (MPI)

  • It is released by Oxford Poverty and Human Development Initiative (OPHI) and United Nations Development Programme (UNDP).
  • It uses 3 dimensions each of which has multiple indicators as follows:
  1. Health and Nutrition- Nutrition and Child mortality
  2. Education- Years of Schooling and School Attendance
  3. Living Standards- Cooking Oil, Sanitation, Drinking Water, Electricity, Housing, Assets

MPI estimates by NITI Aayog:

  • NITI Aayog estimated the Multidimensional Poverty Index (MPI) based on 4 rounds of National Family Health Surveys (NHFS) and published the baseline report in 2021.
  • The rationale for the MPI was derived from the concept that poverty is the outcome of simultaneous deprivations in multiple functions such as attainments in health, education, and standard of living.
  • NITI Aayog identified 12 indicators in these three sectors and calculated the weighted average of deprivations in each of these 12 indicators for all men and women surveyed in NFHS 4.
  • If an individual’s aggregate weighted deprivation score was more than 0.33, they were considered multi dimensionally poor.
  • The non poor may also be deprived in a few of these indicators, but not as much to be classified as multidimensionally poor. 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 (HCR).

Significance of the Index for india:

1.Help in public policy making :

The index is an important public policy tool which monitors multidimensional poverty, informs evidence-based and focused interventions

2. Presents Overall Picture of Poverty:

The index also enabling closer and more in-depth analyses of areas of interest such as regions – state or districts, and specific sectors and complements the existing monetary poverty statistics.

3. Help Achieving SDGs Goals:

The index helps in measuring India’s progress towards target 1.2 of the Sustainable Development Goals (SDGs) which aims at reducing “at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions.

Estimation of the Intensity of Poverty:

  • This is the weighted average deprivation score of the multidimensionally poor.
  • For instance, the Intensity of Poverty in Tamil Nadu declined from 39.97% to 38.78% during this period, indicating that the summary measure of multiple deprivations of the poor has only marginally declined in these five years, and has to be underlined for policy focus.
Multidimensional Poverty Index (MPI) = Head Count Ratio (HCR) x Intensity of Poverty (IoP)
  • The MPI for Tamil Nadu declined from 0.020 to 0.006. This sharp decline in MPI is largely due to a greater decline in Head Count Ratio compared to Intensity of Poverty. This gives us a clue that any further decline in MPI in Tamil Nadu should happen only by addressing all the dimensions of poverty and reducing its intensity substantially across the State.

Direction of intervention:

The deprivation estimation also indicates that the overall population that has been identified as deprived in most of the indicators individually is higher than the population identified as multidimensionally poor. This once again reiterates the point that people may be deprived severely in a few functions, but may not be multidimensionally poor.

Quality of NFHS data:

  • The quality of survey data has been widely debated in academia. The National Sample Survey Organisation’s (NSSO) sample surveys have been debated among economists and statisticians, both in terms of sampling and non-sample errors, right from its initial days in the 1950s.
  • Following several review reports on the NSSO’s methodologies, the NSSO has been attempting to improve sampling design and reduce non-sampling errors, particularly with reference to recall periods for providing consumption expenditure by households.

Impact of COVID-19:

  • Head Count Ratios were lower in the post-lockdown period than in the pre-pandemic period, leading to the inference that post-lockdown, the deprivation in several functionings was lower, implying a lower poverty ratio as well as Intensity of Poverty. In particular, the deprivation in terms of nutrition and maternal health declined, and schooling and school attendance increased in the post-lockdown period.
  • Substitution of dry rations for hot meals in the mid-day meal programmes and high pressures in hospitals 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 in the post-pandemic period that we derived from this database.

Way forward:

  • In order to reduce the Intensity of Poverty, we need to address deprivations across the entire population. There should be a universal approach instead of a targeted approach to addressing it.
  • The survey data gives us only broad policy pointers whereas programmatic interventions should be curated with ground-level realities. At the same time, continuous engagement with survey data in terms of improving the sample design and response quality has to be sustained.

Analysing the data and finding the incongruence of inferences from different databases on an issue would help improve data gathering systems. Let us continue to use survey data both to derive policy conclusions (with caution) and also to help improve data quality.


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