A five-judge bench of the Supreme Court recently pronounced its verdict on the long-drawn case challenging the constitutional validity of the Aadhaar project. While much has already been said of the judgment, the biggest let-down is that Aadhaar will remain mandatory where it has already caused the most damage –
While upholding Section 7 of the Aadhaar Act, the verdict notes that Aadhaar leads to inclusion, empowers the marginalised and there is no substantial evidence of Aadhaar leading to exclusion. As Reetika Khera notes, such claims suggest that the majority judges wrongly accepted the government’s ‘assertions’ as ‘facts’ without critical inspection.
Until very recently, we were made to believe that government welfare programmes in India are “leaky” and this is because the lists of welfare recipients are plagued with “identity fraud” – “fakes” and “duplicates”. This belief continues to be the bedrock for claiming that Aadhaar will be a “game-changer” for welfare delivery. However, recent evidence from varied sources suggests that identity fraud accounts for a tiny fraction of total beneficiaries – see for MNREGA; PDS (here and here); pensions; mid-day meals (here and here).
It is also clear that the virtuous claim of Aadhaar leading to ‘inclusion’ is false. As per replies to an RTI query, 99.97% of Aadhaar holders already possessed existing IDs at the time of enrolling for an Aadhaar. Further, Aadhaar, by itself, does not guarantee access to any welfare benefits. Persons must separately meet the eligibility criteria of the respective schemes.
Aadhaar is now an additional hurdle to accessing benefits and varied sources confirm that technological failures at different points of the system are leading to serious issues of exclusion and disruption. For instance, A.B. Pandey’s oft-cited presentation to the Supreme Court during the hearings itself noted that authentication failure for government services was as high as 12% in 2018. Despite UIDAI and GoI claiming otherwise, field surveys and ground reports suggest that a fair share of these result in increased transaction costs at best and denial of benefits at worst. In some tragic cases, Aadhaar had a role (directly or indirectly) in starvation deaths.
Nonetheless, the Jharkhand government recently made Aadhaar mandatory for social security pensions. This means that pensioners must have an Aadhaar number, get it “seeded” in the pensions’ database and link it to their bank account (commonly referred to as “Aadhaar seeding”). In September 2017, the government claimed that this had resulted in the deletion of close to three lakh “fake” pensioners in 2016-17, leading to savings of Rs 200 crores. This claim, widely relayed in the media, has been cited as evidence of the revolutionary capability of Aadhaar in welfare provision.
In December 2017, with the help of the NREGA Sahayata Kendra in Khunti and student volunteers, we conducted a small survey in Khunti town to verify these deletions and to understand when and why pensions were cancelled. The results of the survey were recently published in a longer article in the Economic Political Weekly.
While the Jharkhand government claimed that close to three lakh pensioners have been deleted, they refused to make the list of deleted pensioners available in public, making any verification difficult. The local administration in Khunti was kind enough to provide us a list (henceforth, the “official list”) of all the names deleted from pensioners’ list in Khunti town during the financial year 2016-17. The list contained 103 names from 22 wards in the town. For the verification survey, we chose wards with three or more deletions, leaving us with 90 deletions (henceforth, the sample) in 13 wards (henceforth, sample wards).
Against each name, the official list mentions the relevant pension scheme and the reason for deletion (henceforth, the official reasons). Figure 1 presents the breakdown of schemes in the sample. About three-quarters of the deletions pertained to old-age pensioners, 22% to widow pensioners, and 2% to disability pensioners. Further, women accounted for about 75% of the deleted names.
Figure 1: Distribution of types of schemes in sample
Figure 2 presents the reasons for deletion as per the official list. Two of the three reasons, “death” and “duplicate,” are self-explanatory and accounted for 16% of the sample.
A third reason, vaguely worded as “discontinue,” accounted for the remaining 84%. Preliminary discussions with the local administration revealed that “discontinue” was a code for failed Aadhaar seeding.
Figure 2: Reasons for deletion as per official list
Early into the survey, a clear pattern emerged: many respondents reported that they used to receive a pension, but then payments stopped after October 2016. It seems that from then on, the local administration in Khunti (and perhaps elsewhere in Jharkhand as well) stopped paying pensions to those who had not seeded their Aadhaar numbers.
The survey revealed that, contrary to what the government had claimed in September 2017, many of the deleted pensioners were alive and eligible, and had been unfairly deprived of their pensions. Figure 3 presents the breakdown of reasons as ascertained by the field survey. About 35% of the deletions (the largest share) were cases of exclusion due to failed or faulty Aadhaar seeding.
Figure 3: Reasons for deletions based on field survey
The ghost of identity fraud
Corruption in leakages are mainly of three types (i) identity fraud – fakes or duplicates receive pensions (ii) quantity fraud – pensioners receive less than their entitlement or must pay a bribe and (iii) eligibility fraud – ineligible persons receive pensions.
Aadhaar, by design, is of little help in reducing bribes (quantity fraud) or identifying the poor (eligibility fraud). Potential benefits are restricted to identifying “duplicates”, where the same person receives two pensions, and “fakes”, where pensions are drawn in the name of a non-existent person. The survey allows us to estimate the extent of these for Khunti town.
Duplicates: Identifying duplicates in a household survey can be difficult because it is unlikely that anyone would self-report duplication. However, about 7% of the respondents reported no disruption in their pensions at all. These can be considered as duplicates, given that most of these cases were also classified as “duplicate” on the official list as well. This does not, however, mean that fraud was necessarily involved. Given the slow (and opaque) selection process into pensions, many people repeat their application after waiting in vain for a response to the second. It is not clear if they received a second pension or not.
Fakes: About 20% of the sample included cases where the pensioner could not be traced and were not known to local residents. These could possibly be cases of “fakes”. However, about 40% of these cases were from a single ward in the heart of Khunti town. Locating persons in a crowded urban area without information of their exact address can be extremely difficult.
An upper-bound (max possible) for identity fraud can be worked out by considering all untraceable persons as fake, and all those who reported no pension disruption as duplicates and comparing these numbers with the total number of pensions in the sample wards. This would imply that about 2% of all pensions were fake and 1% duplicates, at most. This is consistent with earlier findings suggesting low levels of identity fraud in pension schemes.
Deaths and migrations
Irregularities in updating administrative records, for example, to record cases of death or migration, create scope for leakages. The survey suggests that Aadhaar seeding was used as a one-off opportunity to weed out cases of death and migration, amounting to 19% and 3% of the deletions respectively (Figure 3). This was possible because dead persons and migrants were not available to submit their Aadhaar numbers for seeding.
From then on, however, the role of Aadhaar seeding is likely to be limited to preventing the entry of duplicates and fakes on pension lists. After the initial seeding exercise is complete, Aadhaar seeding on its own does not particularly help in identifying pensioners who die or migrate. Discussions with the local administration indicate that such cases are usually identified through periodic surveys.
While Aadhaar seeding can perhaps be credited with plugging some identity fraud, the process has led to the exclusion of many pensioners. About a fifth of the sample (18%) reported not receiving their pensions for even a single month after Aadhaar imposition (October 2016). All of them possessed an Aadhaar number but they had failed to seed it before the deadline.
While there can be numerous reasons for this, a few are highlighted through case studies collected during the survey.
Poor information dissemination and coercive deadlines
Many reported being unaware of the new mandate regarding Aadhaar and learning of it only when bank officials informed them that their pensions had been discontinued. With limited family support, many subsequently made multiple trips to the block office and bank to submit their Aadhaar details, but their pensions have still not resumed. This is what happened to Mangri Pahnaian. She and her disabled son barely leave their house given their old age and physical condition. They found out about the new mandate only when her pensions were discontinued in October 2016. Despite subsequently submitting the Aadhaar details, Mangri’s and many other’s pensions had not resumed.
Faulty Aadhaar seeding
Aamna Khatun, a 70-year-old woman who lives in the heart of Khunti town, stopped receiving her pension in October 2016. Enquiries at the bank revealed that the last digit of her Aadhaar number was incorrectly entered during the manual seeding process.
Mismatch of names between with Aadhaar and bank records
Asha Devi’s name is spelt as “Aasha Devi” on her Aadhaar. She has not received her pension since October 2016. Bank officials believe the name mismatch to be the reason. She has made several visits to the bank to get this fixed, without success.
Lack of support to travel to bank and block office
Some old and disabled persons, due to their old age and physical conditions, expressed their inability to make repeated trips to the bank and block office required to complete the Aadhaar seeding formalities. Soma Tidu, a 45-year old man, had not received his disability pension for the last nine months at the time of the survey. The bank told him to go to the block office to link his Aadhaar card. As he does not have any family members or close relatives, he couldn’t visit the block office to inquire about the cancellation.
By the time of the survey (December 2017), it had been about 14 months since these persons received any pensions – this amounted to Rs 8,000 each, not a small sum by any means.
About 17% of the sample reported a disruption in payments for some (not all) months between October 2016 and November 2017, because of failed or faulty Aadhaar seeding. Upon realising that their pensions were not being deposited in their accounts, they made several trips to the bank and block office, which fortunately resulted in their pensions resuming. Bhoja Mahto was deprived of eight months of pensions (approximately Rs 5,000) for Aadhaar seeding-related issues. Verification of his (and other’s) passbooks suggest that these “arrears” have not been paid.
Contrary to the claims of “empowerment”, the survey reveals the coercive and high-handed nature of the Aadhaar seeding in Khunti, Jharkhand. Aged, widowed and disabled pensioners, who missed the deadline for Aadhaar seeding, have been left in a Kafkaesque bureaucratic limbo to prove their existence.
The findings also raise questions about the potential and impact of Aadhaar as a policy tool to enhance efficiency. Contrary to the government claims, fakes and duplicates were found to make up only a fraction of deleted pensions (28% as an upper bound), and 3%, at the most, of all pensions in the sample wards.
In fact, Jharkhand government’s own data suggests that fraud is very limited in pension schemes. An RTI query filed by Inayat Sabhikhi, in response to the Jharkhand government’s claim regarding fake pensioners, reveals that only 7% of deletions could be on account of fraud. The rest 93% were for genuine reasons like death, migration, etc. Instead, the findings from Khunti suggest that Aadhaar seeding seems to have left many pensioners excluded.
The Jharkhand government seems disconnected from the real-world implications of making Aadhaar compulsory for social security pensions. It is particularly worrying that the government does not even acknowledge the resulting exclusions. Worse, the expenditure reductions resulting from these exclusions are projected as valuable Aadhaar-enabled savings.
In reality, poor information dissemination, coercive deadlines, increased hurdles and limited grievance redressal have left pensioners in a state of heightened vulnerability. Many of them are paying the price for obsessive and misguided attempts to eradicate “identity fraud” where it hardly exists.
A longer version of this article first appeared in the Economic and Political Weekly on September 8, 2018.
Rishabh Malhotra and Anmol Somanchi are independent researchers based in New Delhi. They would like to thank Meghna Yadav for her thoughtful suggestions.