07 Apr Multidimensional Poverty Index (MPI) and India
Multidimensional Poverty Index (MPI) and India – Today Current Affairs
The concept of multidimensional poverty accommodate both monetary and non-monetary aspects of poverty and the measuring of multiple and overlapping deprivations at the same time. During the last one-decade (since 2010), the OPHI has been generating estimates of MPIs for over 109 developing countries and these estimates are being disseminated by the global Human Development Report (HDR).
Multidimensional Poverty Index (MPI) for India is given by NITI Aayog. The MPIs are estimated with a focus on the Sustainable Development Goal monitoring indicator (SDG 1.2), one of the 29 indices for monitoring reform and growth, identified by the Government of India.
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The MPIs are being estimated by accounting for three key aspects of human development: health, education and standard of living of the population represented through a set of 10 (ten) indicators largely from Demographic Health Survey (DHS) data sets and other nationally representative surveys conducted by countries.
A growing number of studies provided national-, regional-, and district-specific estimates of MPIs using data from the National Family Health Survey (NFHS), and the Indian Human Development Survey (IHDS). The estimates from these studies are consistent and showed large variations and reduction of multidimensional poverty across geographical regions. The OPHI estimates for India disseminated in the Global Multidimensional Poverty Report, 2021, showed a reduction of multidimensional poverty (H) from 55.1% in 2005–06 to 27.9% by 2015–16 and intensity of poverty (a) from 51.3% to 43.9% during the same period. The corresponding decline in the MPI was from 0.283 to 0.123 (UNDP 2021). Since 2016, the same estimates have been repeated in the annual HDR report. We present the following critical reflections on the NITI Aayog’s MPIs. The NITI Aayog estimates are similar to that of global estimates with minor modifications.
Key Points : The Hindu Analysis
(i) Estimates of multidimensional poverty refer to 2015–16, the year NFHS-4 survey data set was publicly available and so the estimates are not for 2021.
(ii) Estimates are limited to variables available in the NFHS-4 survey data. It may be mentioned that the NFHS data sets are the Indian version of DHS, primarily aimed at providing data on demographic and maternal health situations in the country. Hence, many key indicators with regard to the dimension of health, education and standard of living are absent in this data set as well as in these estimates. For example, the nutrition-related indicator in the MPI frame does not take into account the nutrition level of children aged 6–14 years, as the NFHS does not compute the BMI for this group of children.
(iii) The cut-off point of 0.33 usually taken, in defining multidimensional poverty or deprivation, is arbitrary without a strong theoretical justification.
(iv) The estimates of multidimensional poverty do not include many key deprivations in the domain of health, education and standard of living, for example, “access to banking”
(v) Estimates are primarily derived from micro data that remains beyond the comprehension of most policymakers for any critical evaluation and interpretation.
Key deprivations in the dimensions of health, education and standard of living are missing in the estimates of multidimensional poverty.
The representation of dimensions and its corresponding indicators are vital in the estimation of MPIs. While the inclusion of three key dimensions of human development such as health, education and the standard of living qualifies for the human development paradigm, the chosen indicators are inadequate to capture the deprivations in these dimensions.
For instance, the nutritional deprivation of a household is defined as a union of failure in child undernourishment in the age group of 0–5 years or for adult, men and women in the age group of 15–49 and 15–54, respectively. Such deprivation overlooks the differential vulnerability to undernourishment between the early ages and adulthood, and further, owing to rapid fertility transition, a good number of households will escape this counting on child undernourishment in the complete absence of children below the age of five years. The conceptualisation of household undernourishment, therefore, based on the anthropometric measure becomes more challenging as against the calorimetric measure of calorie intake. That too is not without its limitation of intra-household allocation of intake across all individuals of the household.
Regarding maternal health indicators, the case of four antenatal care and medical assistance at delivery serves at best as a preventive maternal care indicator that has an implication on healthy maternal outcome. Such an indicator, in present times, will be selective to certain households with recent childbirth experience that is less frequent owing to fertility transition. In case nutrition, survivorship and maternal health had to represent the health domain, the indicators at the household level should have been worked out with more caution rather than computing anything that is available in the survey. The Hindu Analysis
Deprivation in the education domain is represented by the failure in having even one member with minimum years of schooling on the one hand, and on the other, a school going child of a certain age being out of school. If the household does not have a single member who has completed six years of schooling, the household is said to be educationally deprived. Similarly, if any school going child (up to Class 8) is not attending school, the household is said to be deprived with respect to the education domain. This way of comprehending deprivation completely overlooks the household composition of generations. No single member with schooling of at least six years in a household depicts clustering of educational deprivation, that is, educational endowment generates education.Educational deprivation in one region should therefore be different from another conditioned by the access and spread of educational infrastructure.
The standard of living component considers a number of basic amenities and consumer durables of the household along with access to banking. While absence of them does represent deprivation, there remains a regional and residential facet to such deprivation. Four of them, like cooking fuel, electricity, water, and sanitation are largely conditioned by provisioning which ought to be different across regions and deprivation in them need differentiation across regions and rural/urban households. Deprivation in these four elements are largely concentrated in the rural areas and, therefore, the MPI framework seems to be biased. States/regions that have high rural residency would show high deprivations, both in terms of head counts as well as in terms of depth of deprivations.
On the comparability of multidimensional poverty—is it sensitive to the state of development and residential composition?
While multidimensional poverty is an ideal construct, it is systematically responsive to the state of development as poverty/deprivation becomes less and less with improvements in the state of development. Such a systematic connect is evidenced with MPI indicating 4.2% population in China, 3.6% in Indonesia as against 24.1% in Bangladesh, and 27.9% in India being estimated to be poor in the multidimensional sense (global multidimensional poverty report (UNDP and OPHI 2021). The lower estimates for China and Indonesia clearly suggest the need for context-specific estimates. The Hindu Analysis
The lowest multidimensional poverty was estimated at 1% in Kerala, 4% each in Goa and Sikkim, and 52% in Uttar Pradesh. These numbers suggest a strict systematic response to the state of development. Such results call for a revisit of its indicators and the methodology of estimating multidimensional poverty. In fact, consumption poverty, too, does not compare well with multidimensional poverty in many states of India.
The more urbanised a district is, the less is the ability to accurately estimate the extent of multidimensional poverty in India.
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Conclusions
Should multidimensional poverty replace consumption poverty despite all its limitations? The answer is “No” because an alternative is not acceptable on the sole ground of being different, but differentially sensitive to the levels of development. The consumption poverty provides information on economic deprivation and its trends are commensurate with other deprivations to a large extent. Despite all limitations of consumption poverty with regard to identification, the indicators designed for identification of components in multidimensional poverty does not seem better and less ambiguous. Hence, an exercise of this kind may have a genuine intent but surely does not have the necessary rigour to qualify as a better measure than the existing ones. So, the MPI can at best supplement consumption poverty but cannot be an ideal alternative. Hence, the Consumption Expenditure Survey carried out by the NSSO (latest being 2011–12) has no replacement, as it not only offers estimates of consumption poverty, but also helps in reading development transition at large.
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