Context

There is debate going on over Multi-dimensional Poverty (MPI) estimation and Covid impact on poverty. Various experts are arguing that poverty decline faster during NDA years than UPA years.

How MPI is estimated as per oxford poverty and human development initiative (OPHD)?

Two set of estimates: There are two sets of poverty estimates provided by the Oxford Poverty and Human Development Initiative (OPHI) that compile these data across countries, primarily from Demographic and Health Surveys (DHS).
Uncensored estimate: The first are uncensored estimates for individual indicators, which correspond to a simple question regarding whether a household is deprived (poor) in a given indicator for example, nutrition.
Censored estimate: Alternatively, one can obtain an indicator-specific censored poverty estimate via a two-stage process. 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.

Multidimensionally poor: The first stage estimates the population that is multidimensionally poor.
Poor on each indicator: The second stage estimates the population that is poor in each indicator for the multi-dimensionally (MP) poor. For example, in 2005-06, the MP poor were 55.1 per cent; uncensored nutritionally poor were 57.3 per cent; and 44.3 per cent were censored nutritionally poor.
Poor in both categories: In other words, close to 80 per cent of the nutritionally deprived are also multidimensionally poor. For the DHS India survey years, censored estimates are used.

No mismatch in absolute and indicator specific poor: 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.
Interlinkages between poverty indicators: Other advantage of a censored approach is that 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. Therefore, investments made towards providing sanitation facilities and piped water connections will have an impact on nutritional absorption.

Annual improvement in health and education: Annual pace of improvement in the health, education and living standards indicators during 2005-15: 7.3, 10.0 and 9.6 per cent respectively. In the NDA years: 11, 8.4 and an outsized 17.2 per cent annual gain in living standards.
Efficient redistribution of resources: An efficient redistribution combined with direct fiscal resources targeted specifically to reduce deprivation across individual indicators.
Inclusive growth: The inclusive growth belief was that period I would show a greater improvement because the dominant component of poverty decline, growth in per capita consumption, was about 0.8 percentage point higher in period I (annual 3.8 per cent increase vs. 3 per cent in period II).
Faster poverty decline: The pace of MPI index decline was almost twice the pace in period II relative to period I! This result is strongly indicative of considerably more inclusive (and more efficient and less corrupt) growth in period II compared to period I.
Poor performance on some indicators: For only four indicators is the rate of uncensored poverty decline lower in period II. Assets and school attendance are lower in period II for both uncensored and censored poverty. Incidentally, school attendance improvement is expected to be lower as one approaches 100 per cent enrolment, the pace of change from 20 to 25 per cent enrolment is 25 per cent versus a pace of only 1 per cent when enrolment increases from 95 to 96 per cent.

Poverty estimation debate will continue among the experts. Government should solely focus on poverty reduction policies. Present priority should be reducing the Covid induced poverty.

Mains Question

Q. What is the censored and uncensored poverty estimates? Analyse the recent data on multi-dimensional poverty index (MPI) in India?