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HomeOpinionGovt linked JAM to DBT but the blindspot of ‘exclusion’ emerged. Here’s...

Govt linked JAM to DBT but the blindspot of ‘exclusion’ emerged. Here’s how to plug it

Large-scale scams in PM-Kisan suggest there's vast leakage in DBT advantages. While inclusion errors are being tracked, exclusion should take priority.

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The Direct Benefit Transfer system has become a vital cog in the social protection machinery of India. Launched in 2013, the system has introduced a certain degree of efficiency in the delivery of government to person — G2P — payments. Citizens enrolled under welfare schemes receive monetary benefits from the concerned government department directly into their bank accounts, the process typically facilitated through the Jan Dhan-Aadhaar-Mobile or ‘JAM’ architecture, set up in 2015. As of May 2022, over 300 welfare schemes used this mode of delivery. Any fault line in the DBT system, therefore, has serious implications for a large proportion of the population that is financially dependent on government transfers.

Governments implementing social protection schemes must typically reckon with two broad categories of errors. First, leakages or inclusion errors occur when individuals who are ‘ineligible’ (as per the specified rules) for a social protection programme benefit from it anyway. Second, exclusion errors are those wherein ‘eligible’ individuals are unable to access the benefits due to them. Since the former category typically results in monetary losses for the administering government, policy objectives are frequently directed towards leakage reduction. The same has been observed in the case of DBT in India. One of the key objectives of the DBT system has been the elimination of corruption and the deduplication of beneficiary records. However, our work at Dvara Research has shown that a variety of exclusionary factors — reliance on Aadhaar, digital modes of data collection, among others — may get embedded into a system designed to reduce leakages.

‘Gains’ hide the fault lines

While popular narrative deems that the objective of leakage reduction in the DBT system has been met, mounting evidence suggests otherwise. For instance, sobering tales of large-scale scams in the Pradhan Mantri Kisan Samman Nidhi (PM-Kisan) scheme have emerged in the recent past. In states such as Tamil Nadu and Assam, the cash transfers were fraudulently credited into the bank accounts of ineligible people despite the use of Aadhaar-seeded DBT payments. A key role of Aadhaar, as envisioned in the context of DBT, is to identify the uniqueness of an individual’s identity, support the deduplication of beneficiary lists, and thereby reduce leakages. However, a recent Comptroller and Auditor General of India (CAG) audit on the functioning of the Unique Identification Authority of India (UIDAI) revealed that the Aadhaar database is compromised in its ability to ascertain uniqueness — it may contain identical biometric data for multiple citizens. This compromises Aadhaar’s deduplication functionality. Despite the prevalence of such fault lines, the popular rhetoric only emphasises the monetary ‘gains’ (estimated at Rs 2.2 crore) afforded by the DBT system.

We learn from a data scraping exercise of the publicly accessible PM-Kisan dashboard that Aadhaar and bank account-related errors are the dominant reasons for payment failures. For instance, in East Godavari, Andhra Pradesh, for 51 per cent of the farmers whose PM-Kisan payments were unsuccessful (as of December 2020), Aadhaar-related error was the cause. This may imply (among other issues) the lack of seeding on the National Payments Corporation of India (NPCI) mapper or duplication of a record. An additional 5.3 per cent of farmers in East Godavari experienced failures in the processing of PM-Kisan benefits due to bank account-related issues. This may imply that a citizen’s provided bank account details were incorrect or that the account was under revalidation by the bank. These failures are likely to originate during the digitised collection of data and subsequent uploading of this data onto the Public Financial Management System (PFMS) platform.

In both types of payment failures, there are inadequate exception-handling and grievance redressal mechanisms. In pursuit of a system that minimises leakages, the DBT system has (perhaps inadvertently) become insensitive to the varied realities of genuine applicants.

Also read: A ‘golden record’ of all residents — Maharashtra plans master database for faster services

Where the poor stand

To illustrate, imagine the situation of a smallholder farmer who has not received the quarterly instalment of PM-Kisan benefits. They may first travel to the nearest cash-out point to enquire about their account balance and learn that no money has been received. Their next point of enquiry might be the local revenue officer (or patwari), provided the officer is willing to hear their complaint. They may finally learn that their payment is stuck, as an error was made in the spelling of their name during data entry by the enrollment point. As a result, their name as per the PM-Kisan application does not correspond with their Aadhaar card. They will have to lose another day’s wage to visit the nearest Common Service Centre to update these details – they do not have access to a computer or smartphone to do so using the scheme portal. Described here is the typical arduous journey undertaken by low-income citizens in accessing their DBT money. There is an immense burden on them to expend time, money, and effort to overcome a leviathan of structural barriers. Individual citizens are bearing the cost of creating a system that is more likely to weed out errors of inclusion.

We, therefore, emphasise that factors contributing to exclusion should be measured and tracked – just like inclusion errors are. A precursor for such a move is an understanding of the barriers along the social protection delivery chain that may engender exclusion. For instance, a scheme’s eligibility rule may fail to accurately target those living below the poverty line. Cumbersome documentation requirements during enrolment into a scheme may delay or discourage citizen enrolment. There exists a host of reasons (authentication failures, Aadhaar errors) that may cause backend failure of a DBT payment. Finally, even successful payments into an account may fail to reach the citizen promptly due to inadequacies in the cash-out networks.

Dvara Research’s proprietary exclusion framework identifies the factors that may cause beneficiaries to fall through the cracks in the welfare pipeline. It documents various layers of exclusion against the stages of a DBT payment – targeting and scheme design, enrolment, backend processing of transfers, and cash withdrawal. We see the potential for this framework to help administrators identify points of the DBT system where exclusion is likely to occur, with the hope that G2P payments delivery becomes more inclusive overall.

Aishwarya Narayanan is Research Associate at Dvara Research. Views are personal.

(Edited by Humra Laeeq)

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