Health and Poverty Data of India

The authoritative DHS(Demographic and Health Surveys) household data for 2005–06, 2015–16, and 2019–21 establishes that the pace of decline in Indian poverty accelerated post 2014.

NITI Aayog(using sample survey data of NFHS 4) estimated the Multidimensional Poverty Index (MPI).

Demographic and Health Surveys (DHS)

• They are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition.

There are two types of DHS surveys:

• Standard DHS Surveys
• Interim DHS Surveys

Multidimensional Poverty Index (MPI) :

• The MPI seeks to measure poverty across its multiple dimensions and in effect complements existing poverty statistics based on per capita consumption expenditure.
• It has three equally weighted dimensions:
• Health
• Education
• Standard of living
• Indicators: These three dimensions are represented by 12 indicators such as: nutrition, school attendance, years of schooling, drinking water, sanitation, housing, bank accounts among others.
• Global Multidimensional Poverty Index: It is released by the United Nations Development Programme (UNDP) and the Oxford Poverty & Human Development Initiative (OPHI).

Poverty estimates provided by the Oxford Poverty and Human Development Initiative (OPHI)

compiles data primarily from Demographic and Health Surveys (DHS).

• Uncensored estimates for individual indicators: whether a household is deprived (poor) in a given indicator — for example, nutrition.
• Indicator-specific censored poverty estimate via a two-stage process:
• First stage: It estimates the population that is multidimensionally poor
• Second stage: It estimates the population that is poor in each indicator for the multi-dimensionally (MP)

What data says?(For DHS India survey, censored estimates were used) :

• In 2005–06: The MP poor were 55.1(fifty five point one)percent
• Uncensored nutritionally poor were 57.3(fifty seven point three)percent.
• censored nutritionally poor: 44.3(forty four point three)percent
• Multidimensionally poor: close to 80 percent of the nutritionally deprived are also multidimensionally poor.
• Annual pace of improvement in the health, education and living standards indicators during 2005–15: 7.3(seven point three), 10 and 9.6(nine point six)percent respectively.
• After 2014: 11, 8.4(eight point four) and 17.2(seventeen point two)percent annual gain in living standards.
• Inclusive growth:
• Before 2014(period I): It would show a greater improvement because the dominant component of poverty decline, growth in per capita consumption, was about 0.8(point eight)percentage point higher in period I (compared to 3 per cent in period II).
• The pace of MPI index decline was almost twice the pace in period II relative to period I.
• Performance index for poverty decline:
• The index is defined as the ratio of the rate of poverty decline in period II relative to period I.
• Nine out of 11 indicators: the pace of poverty decline was faster in the Modi period II.
• Performance index average for all indicators: Approximately a 60 percent higher rate of decline in poverty.
• For the uncensored (Dreze’s preference): the average rate of improvement is only slightly lower at 1.55(one point five five)
• Regardless of censored or uncensored poverty measurement: the average pace of poverty reduction was considerably faster during 2015–21.
• For only four indicators is the rate of uncensored poverty decline lower in period II.
• Assets and school attendance: They are lower in period II for both uncensored and censored poverty.
• School attendance improvement is lower as one approaches 100 percent enrolment .
• Multidimensional poverty HCR: It declined faster in the second period.
• Because a given percentage decline is easier to achieve from a lower base.
• MPI poverty: It moved from 27.7(twenty seven point seven)percent in 2015–16 to 16.4(sixteen point four)percent in 2019–21

Logic for preferring censored estimates:

• For some individual indicators such as assets: some households may be considered as deprived (poor) even as they are relatively better off in other areas such as nutrition, sanitation, etc.
• Censored data helps shift the focus onto those who have been (multidimensionally) identified as poor.
• A higher MPI suggests greater intensity of deprivation while a higher censored poverty rate is an important signal to policymakers to redirect policy focus.
• It allows the capture of interlinkages between several poverty indicators.
• For example: environmental enteropathy is known to have a key role in nutrition absorption in children.

Source: Global MPI Data

The next steps are to make sure the changes are made to the above mentioned problems.

• Several policies: more importantly an efficient redistribution combined with direct fiscal resources targeted specifically to reduce deprivation across individual indicators after 2014.
• According to the sampling distribution reported in of the NFHS 2019–21 report, for slightly more than half of the sample interviews ended between January and April 2021
• The claim that sample households in the 2019–21 NFHS survey were interviewed before the Covid crisis is not true.
• The next NSSO consumer expenditure survey and third India Human Development Survey They will soon make figures more clear.
• Address deprivations across the entire population: In order to reduce the Intensity of Poverty we need to address deprivations across the entire population, that is there should be a universal approach instead of a targeted approach to addressing it.
• Programmatic interventions should be curated with ground-level realities: The survey data gives us only broad policy pointers whereas programmatic interventions should be curated with ground-level realities.
• Continuous engagement with survey data in terms of improving the sample design and response quality has to be sustained.