The US Congress acted expeditiously in late March to provide relief on account of the COVID-19 to poor and middle-class individuals and to stimulate the economy by enacting a Coronavirus Aid, Relief and Economic Security (CARES) Act that sends $1,200 to each individual below the income threshold of $75,000. Nonetheless, as The Washington Post reported, even in October, millions of households were yet to receive their stimulus payments. What accounts for this and what is the lesson from this experience for India or other countries that are trying to build robust social safety nets?
What happened was simple: The tax authorities who were charged with disbursing the funds had no way of knowing how to send the cheques. For taxpayers who received refunds in their bank accounts in 2018 and 2019, account information was available with the authorities. But the poor — whose incomes were below the income threshold for filing or those who owed money to the government — had to cross several hurdles to get this money and the computer system did not make it easy for them to register their claim. There are reports that clearing up the backlog of sending stimulus checks could take until January 2021 in some states. Such exclusion from safety nets is particularly large for racial and ethnic minorities. A study by the National Opinion Research Centre (NORC) at the University of Chicago shows that while 24 per cent of White Americans were likely to receive unemployment benefits, only 13 per cent of Black Americans received such payment between April to June 2020.
In contrast, 23 per cent of Indians living in Delhi-NCR received a payment of Rs 500 in their Jan Dhan accounts within three weeks of the lockdown being declared. Farmers registered for PM-KISAN also received Rs 2,000 in their accounts immediately. However, while using prior registries allowed for quick disbursement of funds, it is not clear that the money reached the most vulnerable households. For example, recipients of PM-KISAN were not amongst the poorest households, nor were these the households that were most affected by the COVID-related lockdown. Data from round-3 of the NCAER Delhi Coronavirus Telephonic Survey (DCVTS-3), covering a sample of 3,466 households in June in the Delhi NCR area, suggests that 21 per cent of farm households received transfers through PM-KISAN. However, 42 per cent of such households belonged to the wealthiest one-third of the sample, while another 28.5 per cent belonged to the middle third. The PM-Kisan Yojana applies to landowners, thereby excluding agricultural labourers as well as the urban informal sector workers who were most affected by the lockdown.
Similarly, for the PMJDY payment, BPL and non-BPL households record similar receipt transfers. These findings are consistent with estimates by Anmol Somanchi as well as by Rohini Pande, Simone Schaner, Charity Troyer Moore, and Elena Stacy, who observe that nearly half of poor women are unlikely to receive PMJDY transfers.
These observations outline the twin challenges in designing social safety nets that reach the most vulnerable and can be activated effectively when disaster strikes. Unless a registry containing data about individuals and their bank accounts exists, money cannot be transferred expeditiously. However, registries based on specific criteria (for example, identified BPL households) may not identify individuals most vulnerable to crises. Using data from the Indian Human Development Survey (IHDS), Amit Thorat, Reeve Vanneman, Sonalde Desai, and Amaresh Dubey find evidence that factors that contribute towards alleviating poverty may differ from the ones that push people into it — indicating the challenge of targeting welfare beneficiaries in response to shocks. About 40 per cent of the poor in 2012 were pushed into poverty by special circumstances and would not have been classified as being poor based on their 2005 conditions.
Such exclusion errors can get magnified in the event of large-scale disasters when using pre-existing databases, since many people are likely to fall into poverty from an economy-wide negative shock, leading to coverage errors. Recent estimates from the World Bank suggest that 88 to 115 million people could slide into poverty in 2020, which presents a daunting challenge for targeting welfare beneficiaries. It also emphasises the need for post-disaster revalidation of any existing social registration database.
These observations suggest that in a disaster response situation, we cannot rely on registries based on individual characteristics to identify beneficiaries. Unfortunately, universal benefits may have serious fiscal impacts if expanded nationwide. However, most disasters are geographically clustered. Floods or earthquakes often devastate a swathe of districts; pandemics may affect densely-populated cities more than villages. If there is a way for us to set up social registries that identify individuals, their place of residence, and their bank accounts, these linkages can be used to transfer funds to everyone living in the affected area quickly. Aadhaar linkages of individuals and bank accounts already exist. If residential information in the Aadhaar database can be efficiently structured, this would allow for geographic targeting.
Any social registry that can serve as a potential beneficiary platform for safety nets inherently runs the risk of violating individual privacy. To the extent that such social registries store only basic information such as location, instead of more sensitive identifiers such as poverty status, they are unlikely to violate privacy, while still serving the purpose of providing a list of potential beneficiaries in the event of a sudden shock. As we try to disaster-proof future welfare programmes, these are some of the considerations that deserve attention.
This article first appeared in the print edition on November 28, 2020 under the title ‘Plugging holes in welfare net’. Choudhuri is fellow and Desai is professor and centre director, NCAER National Data Innovation Centre. Views are personal.x
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