According to a NITI Aayog discussion paper, multidimensional poverty in India declined from 29.17% in 2013-14 to 11.28% of the population in 2022-23, with about 24.82 crore people moving out of this bracket in nine years to 2022-23. They also claim that Uttar Pradesh, Bihar and Madhya Pradesh registered the largest decline.
The National Multidimensional Poverty Index or NMPI measures simultaneous deprivations across three equally weighted dimensions of health, education, and standard of living that are represented by 12 sustainable development goals-aligned indicators, according to NITI Aayog. These include three Health (nutrition, child and adolescent mortality, maternal health), two Education (years of schooling, school attendance), and seven Standard of Living indicators (cooking fuel, sanitation, drinking water, electricity, housing, assets, and bank accounts).
Thus, the NMPI by NITI Aayog has 12 indicators while global MPI covers 10 indicators. Are these claims believable?
The NITI Aayog is using data from the National Family Health Surveys or NFHS 3 for 2005-6. It has claimed that the NFHS 4 of 2015-16 applies from 2014 to 2016. Since there is no year-by-year data for the 12 indicators, just a straightforward compound annual growth rate projection is carried out by NITI, assuming that United Progressive Alliance period improvement rates between 2005-6 and 2015-16 in the 12 indicators applied to two years (2015 and 2016) of the NDA government’s term of office.
Is that credible? There is no prima facie reason for assuming that the 7.9% per annum GDP growth rate would deliver similar results to a period when the GDP growth rate for the recent 9 years fell to 5.7% per year.
As though that presumption was not incredible enough, the NITI Aayog paper goes further, drawing upon NFHS 5 data for 2019 and 2021 (please note, not 2019 to 2021 because the survey was stopped after data collection was stopped in 22 states due to COVID), to project beyond 2021 – to 2022 and 2023. In other words, yet another linear projection was made by the authors to extend their conclusions to two years beyond the end of COVID. In other words, it was using data for non-COVID years to extend non-COVID rates of improvement after COVID, to 2022 and 2023. Thus the question is legitimate as to whether that assumption is justified and credible or not.
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Let us look at how COVID impacted on the 12 dimensions of NMPI, starting with Education – school attendance and years of schooling. In an evaluative frame of analysis, the log frame is used, where we use input (e.g. schools, teachers) to achieve a process (school attendance), which lead to outputs or outcomes. Both the education sub-indicators of NMPI are input or process indicators (not indicators of output or outcome). The substantive implication of this evaluative frame is that there is a logical connection between inputs, processes, outputs, and outcomes. Hence, during COVID children lost two years of school: so no attendance over two years, and that is likely to affect their learning not just in those two years, but also till later. The latter will likely adversely affect their years of schooling down the road.
However, that does not bother the analysts who decided to ignore the medium, and potentially long-term, impacts of COVID well into two post-COVID years.
A similar flawed logic is used in the NITI paper for the Health indicators. Mortality (an outcome) increased sharply during COVID. People’s health (a process in the log frame) deteriorated. So, a legitimate concern is: how can pre-COVID rates of improvement (or CAGR) in health status apply to the immediate post-COVID period, when COVID was primarily a health shock to the whole world? It adversely affected people’s health, not just immediately, but over the medium run.
The three health indicators (nutrition, child and adolescent mortality, maternal health) were all adversely impacted by COVID. People are still suffering the health impacts of the virus itself. All three health indicators of NMPI are outcome indicators (unlike the education sub-indicators of NMPI), and health outcomes turned very adverse. And yet, the authors of NITI’s NMPI have no qualms about ignoring the long term health impacts of the COVID shock to human health.
NITI has no qualms also about claiming that India’s poorest states (Uttar Pradesh, Bihar, Madhya Pradesh) had the best NPMI improvement, despite being the worst affected COVID states.
The NMPI’s Standard of Living indicators (cooking fuel, sanitation, drinking water, electricity, housing, assets, and bank accounts) are all input indicators. One could claim that the trend of improvements in the first five input indicators pre-COVID would have recovered in the post-COVID period to pre-COVID levels, despite disruptions to the programmes of implementation in each of these areas.
However, the fact remains that the government went into drastic fiscal consolidation mode even as COVID began in FY21 – India’s fiscal stimulus was among the weakest among emerging market economies (a max of 3% of GDP over FY21 and FY22), when central and state debt to GDP ratio rose to over 90% before falling to 81%, at present. Consolidation has continued, and spending is constrained: health spending was 1.3% of GDP (despite the intention expressed in the National Health Policy 2017 to take it to 2.5% of GDP by 2025 – just one year away.
The new National Education Policy 2020 sets the goal of 6% of GDP for public expenditure. Far from moving in that direction, education public expenditure to GDP has shrunk from 4% pre-2014 to 2.9% currently. None of this fiscal consolidation bodes well for any of the NMPI Standard of Living indicators. Public spending is what is the basis for expanding basic services on which depend most of NMPI indicators’ improvement. Yet, NITI Aayog is claiming these indicators would have improved post-COVID in FY22, and FY23.
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Unfortunately, manufacturing evidence has been rather systematic in the last several years. In fact, the whole purpose of making NMPI the poverty indicator for India, while consumption expenditure surveys were not done for eight years from 2014 to 2022, is part of a political strategy. A paper by government economists (Bhalla, Virmani, Bhasin), purportedly an IMF Working Paper in 2021, claimed on the basis of a flawed methodology that consumption poverty in India has fallen to 1% of India’s population. However, no one in the world estimates consumption poverty in developing countries by using National Accounts Estimates of private consumption, rather than a consumption expenditure survey. That does not prevent government spokesmen at the highest levels to claim consumption poverty is down to 1% in the last 10 years.
The newest fabrication of a multidimensional poverty estimate by NITI Aayog is yet another effort to create a narrative in the run up to Lok Sabha elections 2024.
Santosh Mehrotra is Visiting Prof of Development Economics, Centre for Development Studies, University of Bath, UK.
