gThe NITI Aayog along with the UNDP released a discussion paper on the findings of their Multidimensional Poverty Index, attempting to study the decline of poverty rates and the number of multidimensionally poor people in India across various periods.
This author has previously written (quite) extensively about the complex nature of the debate around both poverty measurement, and the underlying data (of poor quality) responsible for a polarising debate around poverty in India (see here and here for a longer discussion on the subject).
There are serious theoretical, methodological, and empirical questions that are yet to be settled on a subject that has severe policy, welfare, and state-ideological implications for the Indian economic and political landscape. Unfortunately, there is not much intent or willingness expressed by the government, or by those within the NITI Aayog as well, to engage with these issues.
Rather, what we see is a coercive push via a variety of institutional means, to keep beating down any possible data towards a rhetorical reality painted by those who are obsessed with the view that poverty reduction in India has really happened in the last ten years under the Modi government.
Understanding the NMPI
Let's take a closer look at the Multidimensional Poverty Index created by the NITI Aayog in alignment with the globally acclaimed Alkire Foster index methodology.
The National Multidimensional Poverty Index or NMPI measures simultaneous deprivations across three equally weighted dimensions — health, education, and standard of living — represented by 12 sustainable development goals-aligned indicators, according to the NITI Aayog. These include three health indicators (nutrition, child and adolescent mortality, maternal health); two education indicators (years of schooling, school attendance); and seven standard of living indicators (cooking fuel, sanitation, drinking water, electricity, housing, assets, and bank accounts).
As per the findings of the Index, multidimensional poverty (MPI) in India declined from 29.17 per cent in 2013-14 to 11.28 per cent of the population in 2022-23, with about 24.82 crore people moving out of this bracket in nine years. They also claim that Uttar Pradesh, Bihar, and Madhya Pradesh registered the largest decline.
Given what we have seen in the quality (and politicisation) of India’s statistical infrastructure over the last 10 years, one may ask the question: Are these claims, as projected by the MPI findings, credible?
Economist Santosh Mehrotra doesn’t think so (and on grounds of empirical reasoning, one can agree with his observations below).
“There is no prima facie reason for assuming that the 7.9 per cent per annum GDP growth rate would deliver similar results (as applicable for MPI) to a period when the GDP growth rate for the recent 9 years fell to 5.7 per cent 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-19, to 2022 and 2023. Thus the question is legitimate as to whether that assumption is justified and credible or not. The whole purpose of making the 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.”Santosh Mehrotra, for The Wire
The Impact of COVID on Poverty is Missing from the MPI Discussion
As part of our InfoSphere edition on the Great Poverty Debate in India, we discussed earlier how the disproportionate impact of the pandemic and the pandemic-induced lockdowns (and other restrictions) affected poorer households and states.
An analysis of household income data analyzed by APU revealed that the decline in incomes during the COVID-19 period was higher for lower-income percentiles and gradually decreased for higher percentiles.
The bottom 10 percentiles experienced a significant 27 per cent drop in incomes, while the decline was 23 per cent for the 40th to 50th percentiles and 22 per cent for the top 10 percentiles. Furthermore, there was a 15-percentage point increase in rural areas and nearly 20 percentage points in urban areas.
Although the income declines in urban areas were relatively higher compared to rural areas, the difference between poor and relatively well-off households may seem small in percentage points. However, it represents a significant decline in absolute terms, exacerbating the challenges faced by vulnerable populations.
Even for non-monetary deprivations (as measured by a vulnerable group’s access to education, healthcare, nutrition, etc.), one can see a sharp rise in absolute and relative poverty measures.
According to the Hunger Watch national survey of the Right to Food campaign, a crisis emerged in December 2021 – January 2022 due to declining incomes and severe food insecurity, especially among the economically vulnerable and marginalized sections of society:
● 80 per cent of people reported some form of food insecurity, and 25 per cent reported severe food insecurity, such as skipping meals, cutting back on food, running out of food, not eating throughout the day, and going to bed hungry
● 41 per cent of respondents said the nutritional value of their diet had worsened compared to pre-pandemic times
● 67 per cent of people could not afford cooking gas in the month before the survey, further reducing their ability to cook
Further, a report by Pew Research Centre highlights that around 75 million more people in India fell into poverty in year 2022 because of the pandemic-induced economic recession, compared with what it would have been without the outbreak. That number for India accounts for nearly 60 per cent of the global increase in poverty in 2020. In that study, it defined the poor as people who live on $2 or less daily.
Data, Indices, Choice and Design-Errors
More importantly, having closely worked on development indices, while creating the Access(In)Equality Index in 2021 and updating its input-output indicators more recently, one becomes aware of choice issues related with the ‘form’, ‘structure’, and ‘design’ limitations of such hand-picked, crafted metrics and what they may directionally, if not empirically, imply.
In a country where we haven’t seen the regular conduct and release of national consumption expenditure survey data (from which poverty measurement principally happened in the past) or the census, there is room for much debate on whether the Modi government is honest about its intent to show/reflect the ‘truth’ of poverty performance in India.
There are government economists, along with those writing to align with the regime’s rhetoric and ideology who constantly argue that ‘proxy’ indicators on poverty reflect ‘a fall’ in its actual estimate, while others weighing their evidence against the ‘facts’ show contrary results (see here and here for a more detailed discussion of the author on this). It’s because of this reason that social policy debates in India, not just on poverty but so many other vital welfare issues, lack both clarity and critical scrutiny.
Poverty of Statistics Influencing the Statistics of Poverty
Research in social sciences, not just in economics, has broadly transitioned to ‘proving hypothesis I or II’ rather than seeing the ‘data for what it actually is’ and then making reasonable conclusions. In other words, data is used for theory-validation rather than the other way around.
It is critical to explore, investigate, and examine what different databases say about income-based poverty reduction/expansion for any period say, vs capability-based poverty reduction/expansion for that period too.
Poverty isn’t simply about knowing how income, food, and deterministic consumption baskets are determined, as Amartya Sen and Martha Nussbaum had argued a long time ago. There is more to it. Growth alone is a bad indicator of the quality of life as it fails to tell us the situation of deprived people. Thinking of developmental goals in terms of utility has perhaps the only merit of looking at what processes do for people in letting them ‘be’ what ‘they want to be’.
Poverty, for worse, represents a state of powerlessness – a lack of opportunity and possible upward mobility for an identified group, one that often lives/positions itself on the bottom of the consumption-income pyramid. And, as JNU economist Himanshu argues, “The differences in poverty estimates are due to the measure of income/consumption used as much as their choice of poverty lines.”
Part of the reason there are conflicting estimates of poverty for the same period is the loss of reliable data and a yardstick to measure poverty and inequality after 2011-12.
“The responsibility of providing official poverty estimates based on comparable and acceptable criteria was the government’s, in particular the erstwhile Planning Commission. Panels would regularly define and update our poverty line for use with NSO data, all of which was freely available, allowing for a healthy debate on poverty. Indeed, India can rightfully claim to be a pioneer in poverty studies as well as household consumption surveys, which were acclaimed and adapted by such agencies as the World Bank.”, argues Himanshu.
The real issue at hand – apart from the highlighted concern of agreeing to a common understanding of poverty’s conceptual and definitive meaning, which is critical in influencing the way it is ‘measured’ and analytically argued/presented – is the fact that public statistical data infrastructure (and its interpretation) in India has become severely politicised.
There is a poverty of statistics that crowds out any meaningful policy or academic discourse on the statistics of poverty – and much of other social policy.
Economists, dare I say, are increasingly toeing the government narrative when it suits them, and oft dismiss any critical insight that brings the government (or its own policy methodology) to account.
In Quest of the Truth: Using Alternative Designs
Ascertaining the ‘truth’ behind poverty estimates and its measurement for a country as economically unequal and asymmetrically shaped in policy as India remains critical, not just for an academic exercise but also for judging policy outcomes and in assessing the overall (welfare) goals of the government.
Our own Centre’s research team, while creating the Access Equality Index (AEI) had discussed the wider implications of states across India being asymmetrically ranked on providing ‘access’ to basic social, and economic services (from food, healthcare, education, job security, finance, legal recourse-to name a few). See an extension discussion on our work produced here.
Further, to assess progress towards nine out of the 17 SDGs, a few researchers also looked at 33 indicators of health and socioeconomic determinants of health using data from India's National Family Health Survey conducted in 2016 and 2021. The recommendation made by the study's authors was that a strategy roadmap should be developed to increase the momentum on four SDGs: ‘No Poverty’, ‘Zero Hunger’, ‘Good Health and Well-Being’, and ‘Gender Equality’.
The current Indian administration’s macro-track record in achieving each of these goals, over the last nine years, have remained far from satisfactory, which is deeply troubling. Over 75 per cent of Indian districts are off target for eight crucial indicators which include poverty. There is an urgent need for improved policymaking, but, as argued, in part one of this essay series, a greater politicization of numbers-and poverty assessment studies, crowd out critical thought and reason.
One of us argued earlier how this is also true of GDP data calculation and interpretations, on which there are subsequently endless debates (belonging to a polarised rhetorical axis), leading to more noise than concrete answers.
Poverty and income distribution are issues for public discussion as much as they are instruments of governance and public policy for an economy that still has a substantial population that’s financially vulnerable even if not officially poor.
Unless we take conscious measures, at the level of state authority, to consciously depoliticise data and encourage independent critical scrutiny of existing (and new) methodologies of public data accounting/analysis, there is little hope for what we might do on both social and economic policy. The policy-making framework and those behind social policy evaluation can do better in making the ‘discourse’ more analytically coherent than operate under constant cycles of confusion.
(Deepanshu Mohan is a Professor of Economics and Director, the Centre for New Economics Studies (CNES), Jindal School of Liberal Arts and Humanities, O.P Jindal Global University. This is an opinion piece and the views expressed above are the author’s own. The Quint neither endorses nor is responsible for them.)
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