Poverty points

 A recent paper by the Brookings Institution’s Stuart M. Butler and Nehath Sheriff underlines the impact of the US’ Housing First program, which shows that providing stable housing can improve the efficacy of psychiatric and substance abuse treatment as well as aid in connecting individuals to social services.

There is a raging debate on poverty levels in the country among that arcane group known as Indian economists. The argument centres around which dataset is the most credible to base assessments on whether poverty has increased or decreased over varying time spans ~ from 20 years to two.

But neither side is claiming that poverty has been eradicated or even that the number of Indians living on under $2 a day is not significant. Which brings us to the real-life impact on our fellow citizens attempting to survive on such meagre means. In this respect, there are useful learnings from the USA on how to deal with the causative implication of exposure to poverty ~ measured on metrics of homelessness, food security, and hygiene poverty ~ on mental health. India has begun to address some of these issues ~ the Pradhan Mantri Awas Yojana, the National Food Security Act, Midday Meal Scheme, and Swachh Bharat Abhiyaan are laudable initiatives that often don’t get the credit they deserve.

While these programmes are designed to lessen the incidence of poverty, its consequences on mental health are rarely dealt with given resource constraints. A recent paper by the Brookings Institution’s Stuart M. Butler and Nehath Sheriff underlines the impact of the US’ Housing First program, which shows that providing stable housing can improve the efficacy of psychiatric and substance abuse treatment as well as aid in connecting individuals to social services.

The authors assert there is a close connection between homelessness and mental health. Since the start of the Covid-19 pandemic, homelessness and associated behavioural health issues have increased in the USA ~ and there is no reason to assume the situation is any different in India. US Substance Abuse and Mental Health Services Administration data suggests between 20 and 50 per cent of the homeless have serious mental illness.

Additionally, official estimates (2021) are that over 34 million Americans, including nine million children, were living in households that did not have enough to eat. Many of these families do not qualify for federal nutrition programs and are dependent on food banks or community donations. A national study quoted by Butler and Sheriff found that food insecurity was associated with a 257 per cent higher risk of anxiety and a 253 per cent higher risk of depression among low-income families.

Mothers and children appear to be at an especially high risk of mental health distress associated with food insecurity. Inequitable access to personal care and hygiene products is an overlooked public health crisis, add the authors; data is limited on the mental health implications of what is widely described as “hygiene poverty.” A 2021 study found a link between women struggling to afford menstrual products and depression ~ for young women in low-income households, this added stress in their daily lives is a significant factor in their mental health.

Given these findings, Indian policymakers could consider addressing mental/behavioural health issues among the poor by integrating the effort, possibly under the aegis of the Ayushman Bharat (PM-JAY) scheme, with the four flagship initiatives mentioned above. This would improve the quality of our human resources which, in turn, will have positive effects in the social, economic, and psycho-cultural domains.

Source: The Statesman, 18/04/23

Counting India’s poor: Numbers suggest the need for a welfare state

 

Madan Sabnavis writes: In such a situation, it is but natural that the government has to assume the role of a welfare state. But the focus has to be on job creation. A joint effort between the Centre and states is needed to push this agenda forward.

The Global Hunger Report has caused a lot of controversy with questions being raised as to what exactly we are talking about. India is definitely the fastest growing economy and has received praise when it comes to reaching out to the needy during Covid or in technology-led innovations like UPI. We are an attractive market for foreign investment and can be reasonably confident of taking up where China has left. Can such a country be so low down the hunger index?

There is a need to introspect on who exactly is poor in India. The concept is nebulous. There was a time when calorie intake was the yardstick. But converting only 2,400 calories a day into a monetary value was always challenging. Besides, people cannot live with just calories. They need access to other amenities like housing, clothing, education and so on. Hence the calorie concept, though a possible criteria, is not really workable today. We need a broader concept.

The World Bank uses the concept of income per day, which is now taken at $1.90. Roughly, this translates to Rs 160 a day in India, and for a family of four would mean Rs 640 a day. On a monthly basis, this will come to around Rs 19,200 or Rs 2.3 lakh for a year. Such an approach runs the risk of using a universal yardstick across the world which is not right. While a weak currency can give a high value in India, this amount may be too low for a developed country (in the US a burger costs more than $1.90). Besides, using either the calorie or income approach runs the risk of extrapolation as it is not possible to get these numbers for the entire population.

Income tax data, while useful in indicating who pays tax, captures only a small segment as it leaves out the big universe of rural people. Hence one cannot even use the Rs 2.5 lakh per annum criterion as a cutoff for measuring the poor.

If, however, the concept of poor is broadened to represent the needy or the vulnerable section of society, there can be some ideas from government action. The government has been aggressive in reaching out to the vulnerable during the pandemic, providing them free food as well as income through cash transfers. This can be a good starting point to assess the population that requires support to maintain their minimum needs. But, here the support has been limited to cash transfers or free food. It does not cover education or health, which are supported through other schemes.

According to IBEF, the PMGKY covered 800 million people. Intuitively, this means that nearly 60 per cent of the 1,350 million population required support from the government and would have found it hard to survive without that. That this has been extended till December 2022 means that the vulnerable population is still very high. This number also includes the 136 million families that were covered under MGNREGA.

In fact, the National Portal of India in September 2020 had put out a statistic that 42 crore poor people benefited from PMGKY which means that around 30 per cent of the population was declared poor by this yardstick. The PM Kisan Scheme involves outlays of between Rs 60,000-70,000 crore. Considering that all the retired urban population does not make a claim by becoming farmers, the cut-off for the pension was put at Rs 10,000 per month. One can assume that the same yardstick was followed for cherry picking the farmers for delivering this benefit.

Here it has been highlighted that 110 million farmers were registered and drew the benefit of Rs 6,000 per annum. Using this policy as a measure to figure out the vulnerable class, which would be restricted only to the farming community, would yield a number of close to a third of the population, assuming that families comprise four members. This will not cover the vulnerable non-farming class, especially in urban areas where there is little information available as there are few urban support programmes run by even the states.

The government programmes are structured well and the use of technology has ensured that there is targeted delivery of benefits. Leakages can be ruled out. Putting all these numbers together, the proportion of vulnerable people in the economy would range between 30 per cent to 60 per cent. The higher end would be more time specific and the revelation of the number of beneficiaries of free food in the last quarter would give a more nuanced number of the vulnerable.

It can, hence, be said the size of the needy population is close to 60 per cent of the total with at least 30 per cent or half of this amount being most vulnerable. In such a situation, it is but natural that the government has to assume the role of a welfare state. But the focus has to be on job creation. Agriculture in particular should be commercialised — the farm laws sought to do so. State governments have a big role to play here. Also, manufacturing has to be revived to create meaningful jobs. A joint effort between the Centre and states is needed to push this agenda forward.

Source: Indian Express, 6/12/22

This is how poverty in rural India came down

 A recent World Bank Report has shown that extreme poverty in India more than halved between 2011 and 2019 – from 22.5 per cent to 10.2 per cent. The reduction was higher in rural areas, from 26.3 per cent to 11.6 per cent. The rate of poverty decline between 2015 and 2019 was faster compared to 2011-2015.

In an earlier article (‘A greater ease of living,’ IE, November 20, 2019), I had argued that poverty has reduced significantly because of the current government’s thrust on improving the ease of living of ordinary Indians through schemes such as the Ujjwala Yojana, PM Awas Yojana, Swachh Bharat Mission, Jan Dhan and Mission Indradhanush in addition to the Deendayal Antyodaya Yojana-National Rural Livelihood Mission and improved coverage under the National Food Security Act. While debates on the World Bank’s methodology continue to rage, it is important to understand how poverty in rural areas was reduced at a faster pace. Much of the success can be credited to all government departments, especially their janbhagidari-based thrust on pro-poor public welfare that ensured social support for the endeavour. It will nevertheless be useful to delineate the key factors that contributed to the success. First, the identification of deprived households on the basis of the Socioeconomic and Caste Census (SECC) 2011 across welfare programmes helped in creating a constituency for the well-being of the poor, irrespective of caste, creed or religion. The much-delayed SECC 2011 data was released in July 2015. This was critical in accomplishing the objectives of “Sabka Saath, Sabka Vikas”. Since deprivation was the key criterion in identifying beneficiaries, SC and ST communities got higher coverage and the erstwhile backward regions in Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh, Jharkhand, Odisha, Chhattisgarh, Assam, Rajasthan and rural Maharashtra got a larger share of the benefits. This was a game-changer in the efforts to ensure balanced development, socially as well as across regions. Social groups that often used to be left out of government programmes were included and gram sabha validation was taken to ensure that the project reached these groups.

Second, the coverage of women under the Deendayal Antyodaya Yojana and Self Help Groups (SHG) increased from 2.5 crore in 2014 to over 8 crore in 2018 as a result of more than 75 lakh SHGs working closely with over 31 lakh elected panchayati raj representatives, 40 per cent of whom are women. This provided a robust framework to connect with communities and created a social capital that helped every programme. The PRI-SHG partnership catalysed changes that increased the pace of poverty reduction and the use of Aadhaar cleaned up corruption at several levels and ensured that the funds reached those whom it was meant for.

Third, Finance Commission transfers were made directly to gram panchayats leading to the creation of basic infrastructure like pucca village roads and drains at a much faster pace in rural areas. The high speed of road construction under the Pradhan Mantri Gram Sadhak Yojana created greater opportunities for employment in nearby larger villages/census towns/kasbas by improving connectivity and enhancing mobility.

Fourth, the social capital of SHGs ensured the availability of credit through banks, micro-finance institutions and MUDRA loans. The NRLM prioritised livelihood diversification and implemented detailed plans for credit disbursement. New businesses, both farm and non-farm livelihoods, were taken up by women’s collectives on a large scale with community resource persons playing crucial handholding roles, especially with respect to skill development. Fifth, in the two phases of the Gram Swaraj Abhiyan in 2018, benefits such as gas and electricity connections, LED bulbs, accident insurance, life insurance, bank accounts and immunisation were provided to 6,3974 villages that were selected because of their high SC and ST populations. The implementation of these schemes was monitored assiduously. The performance of line departments went up manifold due to community-led action. The gains are reflected in the findings of the National Family Health Survey V, 2019-2021.

Sixth, the thrust on universal coverage for individual household latrines, LPG connections and pucca houses for those who lived in kuccha houses ensured that no one was left behind. This created the Labarthi Varg. Seventh, this was also a period in which a high amount of public funds were transferred to rural areas, including from the share of states and, in some programmes, through extra-budgetary resources.

Eighth, the thrust on a people’s plan campaign, “Sabki Yojana Sabka Vikas” for preparing the Gram Panchayat Development Plans and for ranking villages and panchayats on human development, economic activity and infrastructure, from 2017-18 onwards, laid the foundation for robust community participation involving panchayats and SHGs, especially in ensuring accountability.

Ninth, through processes like social and concurrent audits, efforts were made to ensure that resources were fully utilised. Several changes were brought about in programmes like the MGNREGS to create durable and productive assets. This helped marginal and small farmers in improving their homesteads, and diversifying livelihoods.

Tenth, the competition among states to improve performance on rural development helped. Irrespective of the party in power, nearly all states and UTs focussed on improving livelihood diversification in rural areas and on improving infrastructure significantly.

All these factors contributed to improved ease of living of deprived households and improving their asset base. A lot has been achieved, much remains to be done. The pandemic and the negative terms of trade shock from the Ukraine crisis pose challenges to the gains made in poverty reduction up to 2019.

Written by Amarjeet Sinha

Source: Indian Express, 9/05/22

Definitions are important for poverty measurement

 We must take note of a discrepancy between what India’s government advocates and what the World Bank reports, as it makes a big difference to our poverty count

The most important agreement with some expert analyses of our paper (Bhalla-Bhasin-Virmani, Pandemic, Poverty and Inequality: Evidence from India, IMF Working Paper, April 2022) is on the need to raise the poverty line, possibly a 68 % rise in real terms (from PPP $1.9 per person per day to $3.2 pppd). The present line, in current rupee terms, is approximately ₹52 and we have recommended that it be raised to around ₹88. For a poor family of five individuals, this would mean a household income of ₹1.6 lakh a year. With this poverty line, approximately a fifth of the population will be poor, the ‘right’ definition of relative poverty for a lower middle income economy. Note that poverty reduction is an intrinsic product of economic growth. In 2004, the poverty line was raised by the Indian government (Tendulkar committee) by 18% and this was accepted as a worthy step by all.

Among the substantive points raised in our paper was a simple point about measurement. Somewhat disappointingly, not a single expert, friendly or otherwise, has noted this discrepancy between what India’s government supports and advocates, and what the World Bank reports in its published Povcal reports on the world and individual country reports (hereafter World Bank).

It is a simple matter of definition, on which there should be no disagreement. The facts are as follows.

There are three different definitions of National Sample Survey (NSS) based per capita consumption. The differences have to do with the recall period of consumption for three broad categories, and there is convergence in the academic and policy community (and the World Bank) on the appropriateness of each recall period. The ‘right’ recall period is also supported by common-sense justification of memory and accuracy. For perishables (vegetables and fruits), accuracy is enhanced with a recall period of 7 days. For periodic consumption (e.g. toothpaste, club fees, visits to the doctor, etc), a 30-day recall is considered appropriate; and for durables (e.g. clothing, cars, carpets, furniture, etc), a 365 recall period is deemed proper.

Prior to the 1983 NSS report on consumption, all items were tabulated under a 30-day “Uniform Recall Period" (URP). Starting in 1983, the NSS added the Mixed Reference Period (MRP) with the addition of a 365-day recall period for “durables". And after some experimentation and validation in the NSS surveys of 1999-00, 2009-10 and 2011-12, the NSS organization officially converged to the MMRP (Modified Mixed Recall Period) method. The difference between MMRP and MRP is the addition of a 7-day recall documentation of answers to questions pertaining to the consumption of fruits and vegetables.

It also bears emphasis to note that the Tendulkar committee rejected the URP in favour of MRP in the 2009-10 survey; and post 2011-12 (e.g. 2017-18 and onwards), the MRP was officially junked in favour of MMRP. Somewhat bafflingly, the World Bank, the unofficial ‘gold standard’ of poverty measurement, continues to present Indian poverty estimates for 2011-12 and beyond on the basis of the ‘junked’ and old (pre-1983) method of measuring consumption, and therefore poverty. It is very likely that for no other country does the World Bank not use the official method of measurement, and for no other country does it use a 45 year old outdated method (the last exclusive URP survey was in 1977-78).

It wouldn’t matter if it did not matter. But it does. MRP estimates of extreme poverty are about 3 percentage points lower than URP, and MMRP estimates are about 10 percentage points lower. In 2011-12, for India, it meant that the World Bank was wrongly classifying a 100 million Indian individuals as extremely poor. The World Bank has the slogan that it dreams of a world free of poverty. The practice of using the URP method for India, a country with more than a fifth of the developing world’s population, prolongs the nightmare of a world not free of absolute poverty.

Given the huge importance of the recall period in generating representative estimates of poverty, it is puzzling to note the advocacy and recommendation by World Bank authors Sutirtha Sinha Roy and Roy van der Weide (Poverty in India has Declined over the Last Decade but not as Much as Previously Thought, World Bank Working Paper, April 2022) to use the CMIE Consumer Pyramid Household Survey which has a 4-month (120-day recall) period for all consumption items!

Not all poverty estimates are created equal. It is unfortunate that in India, a one- question consumption estimate, as in 2017-18 onwards labour force surveys conducted by the NSS (the PLFS surveys) is seen to have equal validity as a 33 question-based estimate (pre-2017-18 NSS labour force surveys). Or a 120-month recall period consumption (as in CMIE) for considerably less consumption items has equal validity as 30-day recall questions for more detailed consumption estimation; or a 30-day recall period is preferred by the World Bank for India, when other official and equally detailed estimates are available (as in the 2009-10 and 2011-12 MMRP surveys). For their estimates of non-survey year poverty (note that most countries have at least a 3-4 year gap between national surveys), the World Bank has to rely on a base-year estimate of consumption and national account growth rates for intervening years. This is exactly what we do, with the critical difference that we use the 2011-12 base year MMRP estimate, not the 10% lower 2011-12 URP estimate. Why should the base-year reflect an unofficial lower estimate of consumption is a question not answered by our critics, or by the World Bank.

Surjit S. Bhalla is executive director, IMF, representing India, Sri Lanka, Bangladesh and Bhutan

Source. Mintepaper, 3/05/22

Poverty estimates are a shot in the dark

 Earlier this month, two different estimates of poverty and inequality were published by authors affiliated to the International Monetary Fund (IMF) and the World Bank (WB). They added to the existing pool of private estimates of poverty and inequality since 2011-12. Private, because the government, which used to conduct consumption expenditure surveys (CES) and update poverty lines, has abdicated its responsibility.

The last consumption survey of 2017-18 was junked for no reason. Based on the leaked estimates of consumption expenditure from that survey, S Subramanian reported an increase in poverty from 31 per cent in 2011-12 to 35 per cent in 2017-18 with the number of poor increasing by 52 million. Santosh Mehrotra and Jajati Parida reported an increase in headcount poverty from 22 per cent in 2011-12 to 26 per cent in 2019-20 using the consumption aggregates from the Periodic Labour Force Survey (PLFS) with the number of poor increasing by 78 million. As against these estimates, which used different consumption aggregates from the NSO surveys, estimates from the IMF and WB have reported a significant decline in poverty after 2011-12 although they differ from each other on the level of poverty as well as the magnitude of poverty reduction since 2011-12.

The IMF working paper is authored by Surjit Bhalla, Arvind Virmani and Karan Bhasin. This paper is similar to Bhalla’s earlier work on poverty in terms of methodology with not very different conclusions. Bhalla has argued for long that CES surveys do not capture the estimates of consumption expenditure correctly and are unfit for poverty measurement. He maintains that position here as well and in fact justifies the withholding of the 2017-18 CES survey by the government. He prefers using the Private Final Consumption Expenditure (PFCE) estimates from the national accounts. The difference between survey estimates of consumption expenditure and national accounts (NA) are not unique to India.

But the PFCE estimates do not give the distribution of consumption across households which is a prerequisite for estimating poverty. These are derived aggregates and are available for the country as a whole with no separate estimate for rural/urban or states. Bhalla or anyone else is left with no choice but to use the same CES surveys they dismiss as faulty for getting consumption estimates. One implication of this is that while the consumption expenditure estimates obtained from the surveys are deemed faulty and biased, the ranking of households from the same consumption surveys is seen as free from error. While they find the 2011-12 survey to be right, they see the 2017-18 survey as faulty even though both have been done using the same sampling strategy and concepts and by the same institution.

There is nothing new in this method of updating the NSS survey estimates using estimates from the PFCE. But it has been rejected multiple times by official expert committees after careful examination of the differences between the two estimates. All committees concluded that these are essentially non-comparable because of differences in concepts, design and aggregation methodology. All committees have unanimously rejected the practice of adjusting survey estimates based on NAS estimates of PFCE. This is not the practice in India, or anywhere in the world.

Based on this flawed methodology, Bhalla concludes that India has eradicated extreme poverty even before the pandemic with the percentage of population below the $1.9 poverty line of the World Bank at only 1.4 per cent. While the levels may vary, his conclusions on the trend in poverty reduction are not very different from a completely different exercise by the World Bank. Both conclude that poverty reduction has slowed down in the last seven years of the present NDA government compared to the 10-year period of 2004-2014 of the UPA. While Bhalla reports 26 million people moving out of poverty every year during the UPA regime, this number is one third at 8.6 million for the NDA government. In terms of percentage point per annum (ppa) reduction in poverty, it is 2.5 ppa for the UPA declining to one fourth at 0.7 ppa for the current NDA.

The World Bank estimates also come to a similar conclusion with the rate of poverty reduction between 2004-11 at 2.5 ppa which declines to almost half at 1.3 ppa for 2011-18. They arrive at their figures by using estimates from the Consumer Pyramid Survey of Households (CPSH), a privately conducted survey by the Centre for Monitoring Indian Economy (CMIE). They do this by reweighing the household and population weights of the CPSH given the problems with the survey. While their methodology is also questionable, they try and adjust for the anomalies of the CPSH surveys to arrive at estimates as close as possible to the NSS surveys. Notably, they also dismiss the Bhalla methodology as one of the options.

While the broad conclusion of a sharp slowdown in poverty reduction during the present NDA government compared to the UPA period may be valid, there are differences in the level and extent of poverty reduction claimed, with some studies showing a rise in poverty. But the real issue is not just what happened to poverty and inequality but also what factors contributed to poverty reduction.

There appears to be a consensus that many of the initiatives during the UPA era, including the rural employment guarantee programme and the Food Security Act have contributed to improvement in the lives of the poor, pulling them out of poverty. Bhalla also agrees and documents the stellar role of the in-kind transfers through the subsidised food scheme under the Public Distribution System. The expansion of the PDS during the pandemic has certainly contributed to reducing the misery of the poor who suffered through a sharp slowdown of the economy and the subsequent disruption in economic activity during the pandemic. This calls for strengthening the social safety nets and expenditure on food and livelihood schemes given the challenge of economic recovery coupled with rising inflation.

But an important message is also to strengthen the statistical system and make it independent of state interference. Poverty, inequality and a deeper understanding of what works for poverty reduction is not just an academic exercise but is crucial for designing policies and programmes that work. The responsibility of anchoring policies and programmes to clearly defined goals of poverty reduction rests with the government. Given the controversy over poverty estimates, it is all the more important that the government conducts the CES at the earliest and decides the yardstick of measuring poverty which is the poverty line.

Written by HIMANSHU

The writer teaches at JNU

Source: Indian Express, 12/04/22

Economics Nobel laureates and the credibility revolution

 

Pranav Patil writes: The work of this year’s Nobel Prize-winning economists helped in formulating more rigorous, objective and rational interventions to solve problems like poverty

This year’s Sveriges Riksbank Prize in Economic Sciences (the Nobel prize) has been awarded to David Card for his empirical contribution to labour economics and to Joshua Angrist and Guido Imbens for pioneering new methods to analyse causal relationships. The trio invented methods that have led to the so-called “credibility revolution” in empirical economics.

The scope of issues that economists examine has widened over the last three decades as the discipline began exploring answers beyond mathematical models and ideological discourse. Although neoclassical theories are elegant, questions were raised about their real-life evidence. Do economists have credible evidence such that policymakers and the public can take them seriously? Nobel laureates Abhijit Banerjee and Esther Duflo point out that the lack of evidence is one of the reasons economists were considered less credible.

For an evidence-based approach, understanding the causal relationship between different factors, therefore, becomes imperative. A classic example of a causal relationship is the impact of education on lifetime earnings — would one extra year of education increase earnings and by what magnitude? Economists embraced the experimental approach to tackle the credibility crisis and to assess the precise causal effect of policies. Like in medical science, development economists launched smaller randomised controlled trials in the hope of establishing causality between different variables and to investigate which policy interventions were effective. In a randomised control trial, Duflo, along with others tested how monitoring and financial incentives reduced teacher absenteeism and improved learning in India. Based on experimentally derived causal inferences, economists can recommend more rigorous, objective and rational interventions to solve larger problems like poverty.

However, it is dreadfully challenging to conduct field experiments in many cases. They are expensive, time-consuming and ethically tricky. That is where the idea of “natural experiments” becomes illuminating which rely on random variation without any manipulation by researchers. Card and Alan Krueger designed their famous natural experiment based on the changes in the minimum wage in New Jersey and compared it with Pennsylvania, which has not experienced similar changes. They studied employment in the fast-food industry in the two states before and after the wage changes in New Jersey. Contrary to the predictions of standard economic theory, they found a slight increase in employment in New Jersey compared to Pennsylvania. This finding was a massive blow to conventional supply and demand models. Angrist and Imbens have also designed many natural (quasi) experiments and have been developing a statistical toolkit to precisely estimate the causal effects of policies.

The study of causality is not novel to the research community. However, causal relations were not extensively studied with empirical methods in social sciences. Newton’s second law proposes that an object in uniform motion will continue its motion unless some external force is applied. Credibility revolutionists use this very principle to explain economic dynamics. Nonetheless, “causality is no correlation” is the most common catchphrase for these revolutionaries. To distinguish causal links from correlation, economists rely on counterfactuals. For example, in the Card and Krueger study, they show that employment in two states had been evolving in parallel fashion before changes in the minimum wage. Based on that, they assume that employment would evolve similarly in both states without any intervention. Even if they did not observe what would have happened in New Jersey if there was not any intervention, they could observe the counterfactual situation in Pennsylvania.

Since economics closely deals with politics and the market, it is critical to identify which policy interventions are best (and cost-effective). It is worth considering two studies based on two flagship programmes of the Government of India — the Pradhan Mantri Gram Sadak Yojana and the Rajiv Gandhi Grameen Vidyutikaran Yojana. The general assumption that policymakers make is that rural infrastructure programmes would increase farm and off-farm economic activities and reduce poverty. However, recent studies by Sam Asher, Paul Novosad, Fiona Burlig and Louis Preonas point out that while such programmes increase road and electricity connectivity, they do not cause significant economic development even four to five years after completion. It is thus meaningful to examine whether such interventions cause development, to what extent they increase welfare and where they fail.

Source: Indian Express, 20/10/21

Creating an inclusive welfare architecture

 

Cover all of India’s poor; and merge welfare programmes under one umbrella scheme

The recently announced Atmanirbhar 3.0 package offers important insights into the Centre’s approach to welfare spending in response to the economic shocks caused by Covid-19. The choices point to critical limitations in India’s current welfare architecture and the politics that shape spending choices. With the focus now shifting to the 2021 budget, there is an urgent need to reflect on these choices and articulate a road map for the next year. A robust, inclusive welfare architecture is both a moral imperative as well a critical component for economic recovery.

First, the good news. India’s existing welfare architecture has proved resilient and capable in preventing deep distress in rural India. The Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) and the Public Distribution System (PDS) proved to be lifelines as central and state governments were able to mobilise the administrative machinery and expand the welfare net, at relative speed. By October, nearly two-thirds of the MGNREGS budget had already been spent while demand for work remains unabated. In response, Atmanirbhar 3.0 has increased allocations by a further ₹10,000 crore; this may not be enough given the scale of demand but continued budgetary expansion highlights the essential role of MGNREGS.

For those with ration cards, the PDS was a vital source of relief. Independent surveys point out that a large number of eligible beneficiaries (the numbers range from 63% to above 90%) received grains allocated through the Atmanirbhar package. That the central government extended the expanded PDS scheme till November 2020 (just in time to reap benefits from the Bihar elections) is a good indicator of its effectiveness. The problem with the PDS was not its failure to deliver but rather the failure to universalise the PDS.

For years, policy debates on India’s welfare architecture have sought to pit MGNREGS and PDS as inefficient schemes against the deceptively elegant promise of cash transfers. That both schemes have proved effective in responding to the large-scale shock of Covid-19 should put this debate to rest. The emphasis now needs to shift to expansion and strengthening delivery.

Urban India, however, has not been well served. India’s welfare architecture is simply not designed to respond to the needs of the urban poor, especially migrant workers. With the exception of the PDS (available only to residents) and a smattering of insurance and pension programmes (accounting for a mere 6% of total central government spending on social protection), social protection for urban India is conspicuously absent.

The urban (largely casual, daily wage) worker has paid a heavy price for this absence. Yet, the horrific images of millions of workers walking home, and surveys repeatedly highlighting the sharp income drop amongst urban workers have failed to elicit an adequate policy response.

Absent a pre-existing scheme, the Centre had few instruments at its disposal to deploy to respond to urban distress, although, with a little imagination, this was not an insurmountable hurdle. The emphasis has thus been limited to portability of ration cards under the one nation, one ration card scheme. Reports indicate that a welcome proposal to launch an urban MGNREGS was discussed but later abandoned in favour of increased expenditure in the urban housing scheme and boosting urban employment through incentives for EPFO-registered firms. Given the realities of India’s informal economy — in recent months, the number of EPFO registered firms has dropped — it is unlikely that the latter will be able to respond to the scale of unemployment and associated urban distress.

The lesson to be drawn from the Covid-19 induced economic distress and the Centre’s response is the urgent need to transform India’s social protection architecture into a dynamic system that ensures universal coverage of all of India’s poor. Several interlocutors have argued for a universal (or quasi-universal) cash transfer as the critical missing link that can bridge this gap between rural and urban social protection. However, this debate misses the dynamic nature of the social protection needs of India’s poor. Fifty per cent of India’s population is vulnerable, ie can slip back into poverty with one income shock. This population needs a dynamic basket of social protection instruments — pensions, life insurance, health insurance and distress-linked cash or employment in times of crisis or sluggish growth. Prioritisation will depend on local labour market conditions.

The only effective strategy is to build a decentralised social protection system that allows states and even districts to design schemes to their specific conditions. Some states have begun experimenting, but urban schemes need fiscal support. This is where politics trumps first principles. The impulse to centralise and seek direct credit for welfare is entrenched in our politics.

There is one way to balance politics and first principles. As recommended by the World Bank, the Centre can, alongside core national schemes like the MGNREGS and PDS, repurpose its 400+ social protection transfer schemes into one umbrella scheme but leave states to design interventions to their needs, political credit and blame can be apportioned across Centre and states. The 2021 budget is an opportunity to implement this much-needed reform. Sensible rationalisation and expenditure repurposing can serve as the foundation of an agile, dynamic and inclusive social protection architecture.

Yamini Aiyar is president and chief executive, Centre for Policy Research

Source: Hindustan Times, 18/11/20

The 2020 Global Multidimensional Poverty Index (MPI)

 The 2020 Global Multidimensional Poverty Index (MPI) data and publication "Charting pathways out of multidimensional poverty: Achieving the SDGs" released on 16 July 2020 by the Oxford Poverty and Human Development Initiative at the University of Oxford and the Human Development Report Office of the United Nations Development Programme. The global Multidimensional Poverty Index (MPI) measures the complexities of poor people’s lives, individually and collectively, each year. This report focuses on how multidimensional poverty has declined. It provides a comprehensive picture of global trends in multidimensional poverty, covering 5 billion people. It probes patterns between and within countries and by indicator, showcasing different ways of making progress. Together with data on the $1.90 a day poverty rate, the trends monitor global poverty in different forms.

The COVID-19 pandemic unfolded in the midst of this analysis. While data are not yet available to measure the rise of global poverty after the pandemic, simulations based on different scenarios suggest that, if unaddressed, progress across 70 developing countries could be set back 3–10 years.

It is 10 years before 2030, the due date of the Sustainable Development Goals (SDGs), whose first goal is to end poverty in all its forms everywhere. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'. By detailing the connections between the MPI and other poverty-related SDGs, the report highlights how the lives of multidimensionally poor people are precarious in ways that extend beyond the MPI’s 10 component indicators.

Poverty index: Well done, but still a long way to go

Despite poverty reduction across religions and caste groups, the report also found that 50% of tribals in the country are poor, as are 33% of Dalits and 33% of Muslims. Keeping in view the ambitions of the 2030 Agenda for Sustainable Development, how can India ensure that Dalits, Muslims and tribals are not left behind?

The 2019 global Multidimensional Poverty Index (MPI) from the UN Development Programme and the Oxford Poverty and Human Development Initiative, which was released last week, confirmed that India’s poverty reduction programmes are on the right track.

The report said that incidence of multidimensional poverty almost halved between 2005-06 and 2015-16, climbing down to 27.5%, indicating that the number of poor people in the country fell by more than 271 million within 10 years. Among states, Jharkhand showed the greatest improvement, with Arunachal Pradesh, Bihar, Chhattisgarh, and Nagaland slightly behind. Multidimensional poverty defines poor not only on the basis of income, but also on other indicators, including poor health, poor quality of work and the threat of violence. The poorest district is Alirajpur in Madhya Pradesh, where 76.5% of people are poor – the same as Sierra Leone in Sub-Saharan Africa.

India’s progress in reducing multidimensional poverty has happened thanks to investments in key areas. The country reduced deprivation in nutrition from 44.3% in 2005-06 to 21.2% in 2015-16. Child mortality dropped from 4.5% to 2.2%; deprivation in sanitation from 50.4% to 24.6%; people deprived of cooking fuel from 52.9% to 26.2%; and those deprived of drinking water from 16.6% to 6.2%.

Despite poverty reduction across religions and caste groups, the report also found that 50% of tribals in the country are poor, as are 33% of Dalits and 33% of Muslims. Keeping in view the ambitions of the 2030 Agenda for Sustainable Development, how can India ensure that Dalits, Muslims and tribals are not left behind? The UN’s recently released World Economic Situation and Prospects as of mid-2019 makes a critical point: Economic growth alone is not sufficient for poverty reduction.

What matters are the types of investment by the State. Countries that have driven poverty reduction trends, the report added, have focused their investments on people, importantly through the provision of health, education and social protection. India has been doing that; now it needs to put the pedal to the metal by not just increasing investments in these areas, but also improving implementation of national programmes and ensuring that they reach the last mile.

Source: Hindustan Times, 17/07/2019

Gender 5.0

India’s poverty is a child and parent of women’s role in our economy and society. But a new ambition is starting to work

A roundtable on the challenges of Indian women organised by the Harvard School of Government a few years ago at the beautifully restored Bikaner House in Delhi was predictably inconclusive on whether the problems — and solutions — lie with society or the economy. But changes in our economy (women’s access to income) and society (women’s aspirations, treatment of women and girls by men and elders, influence, beliefs about women’s potential) need simultaneous work to create a virtuous cycle. I believe this virtuous cycle needs Beti Bachao, Beti Padhao, Beti Swastha Badhao and Beti ko Rozgar dilao.

While a new ambition for women is starting to work, another decade of persistence (Gender 5.0) is needed to reach escape velocity.

Most people think about gender bias in terms of economics (labour-force participation and missing GDP) or interpersonal dynamics (men being insensitive to women). But gender bias is a set of interlocking dynamics with lots of well-meaning people implementing and protecting systems, practices, structures and institutions that fundamentally exclude, disenfranchise, and marginalise women. I can’t claim to understand the situation of all women, but I know politics is not an easy calling; I think many women will enjoy and relate to the chapter “On being a woman in politics” in Hillary Clinton’s recent book.

Even if there is some of what American sociologist William Ogburn calls a “cultural lag” — the mismatch between the material conditions of life which change quickly and behaviour and attitudes, which are more resistant to change — huge progress has been made. Gender 1.0 was set off by Raja Ram Mohan Roy. Gender 2.0 came from Gandhiji’s recognition that the freedom movement “walked on one leg”. Gender 3.0 was votes for everybody in 1947 (some women in Switzerland only got voting in 1971). Gender 4.0 started after 2014 with schemes like Beti Bachao Beti Padhao, Ujwala, Maternity Leave Bill, and many other initiatives. Gender 5.0 will include working on men and issues such as triple talaq, fixing our employment exchanges, more learning outcomes in schools, more formal enterprises, more apprentices, more cities, more manufacturing and macroeconomic stability.

Any agenda for women’s empowerment will not be sustainable unless women are empowered to pursue it for three reasons. First, research suggests the strongest predictor of women’s empowerment is having waged work and parents are more likely to invest in girls if there is a strong economic return to having them. Second, reservation is important to discuss — research suggests that getting women into political leadership roles changed parental aspirations for girls and even closed the gender gap in education in some states. Third, many issues for young rural males — especially in North India — likely increasingly relate to the social problems associated with skewed sex ratios.

We need to creatively design policies to counteract the market failures caused by cultural norms, for example, in designing employment exchanges we need to address lower registration by women by having information campaigns on returns to employment for women. In designing apprentice schemes, we need to require factories to invest in hostels and child care that will get women to take up apprenticeships. In reducing labour laws, we need to push harder to remove discriminatory acts like The Factories Act 1948 that prevent women working at night. I am hopeful both productivity and culture will respond. Gender 5.0 could raise labour force participation to above 30 per cent quickly.

Tourism, education and healthcare — probably the fastest-growing areas of jobs for the next decade — hire more women for many reasons but jobs near home attract women workers. Mckinsey estimates India could add $490 billion to its GDP by 2025 by increasing female labour force participation that would add 68 million more women to the labour force. But we don’t live an economy but a society — the latest NSS round suggests that 31 per cent of women engaged in domestic work state that they would like to work for a wage. Women face significant restrictions of mobility — past Indian human development surveys suggest over 50 per cent of female respondents report needing permission to go to a kirana store. And women working or controlling money lowers rates of domestic violence.

Rajasthan is doing its part. The PM’s ambitious Beti Bachao Beti Padhao simultaneously targets the sex ratio and girl’s education; Jhunjhunu and Sikar have been recognised as two of the best performing districts nationally. Our Mukhyamantri Rajshree Yojana, started in 2016 to offer financial support for girls from birth to the completion of class 12, has benefited more than 11 lakh children. Our Mukhyamantri Hamari Betiyan Yojana offers scholarships to meritorious girls after class 10 up to Rs 2.25 lakh per year. Our Padmakshi Award started in 2017 recognises district exam toppers in Classes 8, 10 and 12 with a certificate and cash award of up to Rs 1 lakh. We have distributed more than 15,000 scootys for post-class 10 meritorious girl students from low-income families and 12 lakh bicycles to girl students who enter class 9. Similarly, we distribute 27,000 laptop computers every year to girl students who score 75 per cent or more in Class 8, 10 and 12. And our Menstrual Hygiene Scheme is creating awareness about the issue among women of reproductive age. And women were the obvious anchor for our flagship Bhamashah programme that pioneered direct benefit transfers in 2008.

Nelson Mandela said, “Like slavery and apartheid, poverty is not natural. It is man-made, and it can be overcome and eradicated by the actions of human beings”. Gender issues are also man-made because a nation is shaped by the stories its children are told and a nation is sustained by the stories it tells itself. India is changing the stories it tells itself and its children. Persistence, courage, and continuity could create a level playing field for men and women soon.

Source: Indian Express, 29/08/2018

The dynamic nature of poverty

We need to rethink social safety nets in India’s growing economy so that they can also focus on the accidents of life rather than solely on the accidents of birth.

Sometimes the grand narratives of the Left and the Right do not seem to have any relationship with the lived experiences of ordinary Indians. For the past two decades, the Left has tried to expand social welfare programmes for the poor in the country by highlighting the growing disparities between the rich and the poor. The Right, on the other hand, points to the growing burden of politically driven welfare policies and emphasises the need for economic growth to alleviate poverty and improve the lives of the poor. These grand narratives often obviate the fact that the concept of poverty today is fundamentally different from that of poverty three decades ago, and that safety nets need to be tailored to meet the needs of a society in transition.

Complicated data

For example, most of our anti-poverty policies rely on identifying the poor by using Below Poverty Line (BPL) Censuses conducted approximately once every 10 years. In 1993-94, when half the Indian population fell in the BPL category, it was easier to identify the poor — they lived in rural landless households in underdeveloped districts such as the Dangs and Bastar and often belonged to the Scheduled Castes (SC) or Scheduled Tribes (ST). Even if all the above identification strategies failed, we still had a 50 per cent chance of being right in identifying the poor. Today, however, when one in four rural Indians and one in six urban Indians is poor, our chances of being wrong in identifying the poor are far greater.

Data from the India Human Development Survey (IHDS) point to another trend. This survey, conducted by the University of Maryland and the National Council of Applied Economic Research (NCAER) for the same households at two points in time, viz. 2004-05 and 2011-12, is the first large panel survey in India. Results from the survey show that if BPL cards had been handed out in 2004-05 on the basis of the household’s average consumption expenditure, 25 of the 38 Indians who would have received these cards in 2004-05 would have been out of poverty by 2011-12. On the other hand, of the 62 Indians who were not eligible to receive BPL cards in 2004-05, nine became newly poor in 2011-12. Thus in 2011-12, 66 per cent of the BPL card-holders would have already moved out of poverty, while 40 per cent of the poor would not have had a BPL card.

Spreading the net wide

Once we recognise that poverty is dynamic in nature, and that as per our conventional definition of poverty, poor households may move out of poverty and the non-poor may become poor over a period of time, we are forced to question the veracity of our fundamental assumptions about poverty. Perhaps poverty occurs not simply due to the accident of birth or as defined in terms of where and in which family people are born, but also due to the accident of life caused by the occurrence of disease, disability and unemployment. Achieving this recognition entails a complete transformation in our mindset.

The second concern about our approach to poverty is that we want to cover the maximum number of people, consequently diluting the support that we are able to provide the poor. Empirical data point to a strange paradox. Ironically, in spite of a decline in poverty, the proportion of the population receiving welfare benefits has risen sharply. The IHDS shows that between 2004-05 and 2011-12, the proportion of the population deemed to be poor fell from 38 per cent to 22 per cent. But the proportion of households receiving any of the benefits under different government schemes, such as old age pension, widow pension, and the Janani Suraksha Yojana, or scholarships and other benefits, grew from 13 per cent in 2004-05 to 33 per cent in 2011-12. The proportion of households buying cereals from the Public Distribution System (PDS), which was intended to provide subsidised foodgrains to the poor, grew from 27 to 52 per cent. Further, the newly initiated Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) provided employment to 17 per cent of the households, signifying a substantial increase from the almost negligible participation in erstwhile public works programmes. Thus, the proportion of households covered by all these schemes taken together grew from 35 per cent to 68 per cent of the total population over the period under study.

Despite this massive expansion in the coverage of welfare programmes, the incomes and subsidies accruing from them still account for a relatively small proportion of the overall household budget. In 2004-05, the transfers and subsidies under the above schemes accounted for an average of Rs.3,129 per recipient household per year, which had increased to Rs.6,017 in constant terms in 2011-12. This amounts to only about Rs.100 per person per month in 2011-12. Moreover, since incomes also grew over the period between the two surveys, the average proportion of the household income accruing from benefits grew only marginally from 11 per cent to 14 per cent for all the recipients. Thus, while the burden of these programmes on the public exchequer may be huge, their impact on households is relatively limited.

Today, the number of welfare schemes has proliferated beyond belief. During fieldwork in 2012, the authors discovered that 131 schemes were in operation in one of the study districts. However, most of the supposed beneficiaries had never heard of these schemes. The IHDS found that less than 2 per cent of the households had registered their daughters under the widely touted girl child-protection schemes. The more the number of schemes, the greater is the likelihood of leakage and inefficiency. Moreover, our country has the tendency to initiate schemes without setting aside enough funds to successfully implement them, thereby almost willing them to failure.

Unintended consequences

A third problem is that we often fail to think of the unintended consequences of our policies. The Rashtriya Swasthya Bima Yojana (RSBY) covers hospital costs but not outpatient services. Consequently, many patients delay treatment until the severity of their medical conditions forces them into hospitalisation, which, in turn adversely affects their health and increases public expenditure. Similarly, the focus on cereals in the PDS encourages people to obtain most of their calories from cereals and reduces dietary diversity.

This situation begs the question: Is there another way of providing social safety nets that would circumvent these problems while genuinely taking care of the people’s needs? Fundamentally restructuring social safety nets necessitates meeting three key challenges: identifying those in need of assistance in the context of rapid economic changes; efficiently delivering this assistance to prevent unintended consequences which may pervert the very purpose of social safety nets; and ensuring that this assistance is meaningful rather than simply tantamount to applying a bandage to a cancer. Each of these challenges needs to be addressed through a pragmatic approach devoid of the burden of any ideology.

One strategy could be to start with simple and limited goals while attacking the problem more potently to make a meaningful dent. It would make sense to divide social safety net policies into three categories: first, provision of back-up manual work at below market wages to those who are able to work; second, provision of insurance against catastrophic events such as health-care emergencies or crop failure that push people into poverty; third, provision of cash support, say in the form of old age pension, to people who are no longer able to work.

MGNREGA offers an excellent model for employment programmes in rural areas, which could be expanded to urban areas. High wages paid under this programme may encourage people to work for MGNREGA instead of resorting to other forms of employment, though since this is not a desirable outcome, the wages offered must be below market wages. However, for them to have a noticeable impact, these employment programmes must be universally available for the promised 100 days. The number of crop and health insurance programmes is growing but a better framework is needed to prevent cost escalation, as has been observed in the United States. While old age and disability pension schemes exist, they need to provide a greater level of benefits and offer easier access. But for all these programmes to work, we must first recognise the need for drastically revamping our traditional policies in a growing economy so that they can also focus on the accidents of life rather than solely on the accidents of birth.

Sonalde Desai is Professor of Sociology, University of Maryland and Senior Fellow, NCAER. Amit Thorat is Assistant Professor of Economics, Jawaharlal Nehru University. Views are personal.

Keywords: Poverty, MNREGA, Urban poverty, rural poverty

Source: The Hindu, 2-08-2016