by- Reetika Khera

India’s ambitious biometric identity documents project,
Aadhaar, was portrayed as one that would enhance
India’s welfare efforts by promoting inclusion and
reducing corruption. From being a voluntary ID, it has
become de facto compulsory for most welfare
programmes. Despite early warnings of its limited role in
achieving its stated objectives, successive governments
have ramped up its use. Using a variety of sources, a
review of the impact of Aadhaar on welfare programmes
is presented. It is found that far from being inclusive and
reducing corruption, Aadhaar is becoming a tool of
exclusion. The government’s estimates of savings also
do not stand up to scrutiny, and whatever is termed as
savings is often the result of a denial of legal
entitlements. In its current form, the Aadhaar project
undermines the right to life

A little learning is a dangerous thing;
drink deep, or taste not the Pierian spring:
there shallow draughts intoxicate the brain,
and drinking largely sobers us again.
—Alexander Pope (1709)
Alexander Pope aptly describes the origins and evolution
of the unique identity (UID) project (widely known as
“Aadhaar”) that was initiated to provide each resident
of India a unique number linked to his or her biometrics.
Among other things, it was meant to lead to greater inclusion
into welfare programmes, reduce corruption in them and
remove intermediaries from the delivery mechanism.
Based on the evidence of the source of corruption in programmes
such as the Mahatma Gandhi National Rural Employment
Guarantee Act (MGNREGA), public distribution system
(PDS), social security pensions (SSP), etc, and the proposed
use of UID in them, Khera (2011) concludes that, a priori, there
is a very limited role for Aadhaar in improving their implementation.
The government’s stated purpose of using Aadhaar
in welfare programmes was unlikely to be fulfi lled.
The government, however, continued to push for Aadhaarintegration
in these programmes. This paper looks at the impact
of this Aadhaar-integration primarily in MGNREGA, PDS and
SSP. The cases of the liquifi ed petroleum gas (LPG) subsidy and
the proposed application of Aadhaar in the mid-day meal
(MDM) scheme are also briefl y discussed.
This paper relies on a variety of evidence: quantitative data
from primary fi eld studies, secondary data from government
portals, fi gures obtained through queries made under the Right
to Information (RTI) Act, and responses to questions in Parliament.
Besides this, news reports are used to highlight emerging
issues for which offi cial data are currently not available.
What emerges is as follows: one, the extent of corruption in
these programmes varies, but even where it is high, there has
been a decline predating Aadhaar-integration (for example,
MGNREGA and PDS). Two, Aadhaar-integration cannot solve
the major forms of corruption that continue (primarily “quantity
fraud”). Three, far from improving the implementation of
these programmes, there are signs of Aadhaar-integration
causing serious damage. Four, based on its own data, the government’s
savings estimates due to Aadhaar-integration are
highly questionable.
In a nutshell, the gains are limited and tentative, whereas
the damage is certain and possibly substantial. Given this, the
government’s decision to make Aadhaar mandatory for a
range of welfare programmes is a cause for alarm. The “right
to privacy” challenge to the Aadhaar project has received a lot

of attention, but the “right to life” challenge to it has thus far not
been adequately highlighted. This paper attempts to fi ll that gap.

Background
Lack of an identity document:1 The proponents of the UID
project claimed that a large number of Indians were denied
welfare benefi ts because people did not have any identity documents
(IDs). This assertion, which was not really supported
with evidence, provided the initial justifi cation for the project.2
If, as the government claimed, the coverage of existing
forms of ID was incomplete, then those should have been
expanded to ensure wider coverage. This was rejected on the
grounds that existing databases were seriously fl awed.
The Unique Identifi cation Authority of India (UIDAI) was set
up to organise enrolment and generation of Aadhaar numbers.
Apart from the National Population Register (NPR), there are
two ways of enrolling for an Aadhaar number from the UIDAI
directly. One, using a proof of ID and a proof of address from a
list (including passports, ration cards, voter IDs, etc) drawn up
by the UIDAI.

Two, using the “introducer” system. This was set
up keeping in mind those people who were lacking in preexisting
IDs (Khera 2015).

According to a response to a right to information (RTI) query
in 2015, only 0.03% of Aadhaar numbers were issued through
the introducer system. The rest were issued to those who submitted
two IDs or through the NPR, raising questions about the
basic premise of the Aadhaar project.

Note also that the UIDAI used those very fl awed IDs to enrol
for Aadhaar that it had rejected as error-ridden. This circularity
has gone largely unnoticed. It has serious implications for the
reliability of the Aadhaar database.4 Being independent of biometric
data (fi ngerprint, photograph and iris scans), the accuracy
of demographic data (for example, name, date of birth, etc) in the
Aadhaar database is especially important now as it is becoming
the basis for claiming benefi ts. There has been no independent
audit of the database, so we do not know to what extent there
are errors, but media reports routinely highlight them.
The promoters of UID also succeeded in creating the impression
that Aadhaar would guarantee access to benefi ts, end the
mai–baap sarkar culture and enable people to assert their
rights vis-à-vis state structures. Here again, the UIDAI was misinformed.
Exclusion is largely the result of a weak “targeting”
mechanism (identifi cation of the poor) and the imposition of
stringent caps on coverage (arising from budgetary constraints).5
For instance, in the PDS, statewise central commitment
was fi xed at the poverty rate estimated using National
Sample Survey Offi ce (NSSO) data from 1993–94 until the passage
of the National Food Security Act (NFSA) in 2013. Caps were
applied in several schemes (for example, for pensions, housing,
etc). The possession of an additional ID cannot solve the problem
of exclusion, unless these caps are relaxed or identifi cation
methods improve. Instead, as discussed subsequently, Aadhaar
is slowly becoming a tool of exclusion, the last hurdle after
all the prior eligibility hurdles have been crossed. For some,
enrolling for Aadhaar has not been easy (Caribou Digital 2017).

Corruption:6 Another justifi cation for the Aadhaar project
was its purported role in reducing corruption in welfare programmes
such as MGNREGA, PDS and pensions. Fraud in these
programmes can be broadly categorised as “eligibility fraud,”
“identity fraud,” and “quantity fraud.”
“Eligibility fraud” refers to inclusion of persons who do not
meet offi cial eligibility criteria, for example, by presenting
fudged supporting documents. “Quantity fraud” takes the form
of eligible persons receiving less than their entitlements, for
instance under-selling in the PDS (people are forced to sign off
on more than what they actually get); in MDM, it could refer to
dilution of prescribed nutrition norms (for instance, not following
the menu at all, or giving watery dal, etc).
“Identity fraud” refers to cases where one person’s benefi ts
are claimed fraudulently by another. In the PDS, an offi cial
may defraud the system by getting a ration card in the name of
a non-existent person or dead person (“ghosts”), or getting
two cards when they are entitled to only one (“duplicates”). In
the MDM scheme, identity fraud can take the form of infl ated
attendance (where costs are booked for more children than
are actually being served meals).
In programmes such as MGNREGA and SSP, which provide
support in cash rather than kind, one big protection against
identity fraud comes from using the banking system to transfer
funds. This eliminates, by and large, the possibility of identity
fraud so long as banking norms are observed.7
Biometric technology, to the extent that it is reliable can help
eliminate identity fraud, but has a very limited role, if any, in reducing
quantity fraud or eligibility fraud. There is limited evidence
on the magnitude of each type of fraud, but available evidence
suggests that quantity fraud is the bigger problem (Khera 2011,
2015). Therefore, contrary to the government’s understanding,
Aadhaar can only play a marginal role in reducing corruption.
Aadhaar in Welfare

The following sections examine the evidence on Aadhaarintegration
in four important welfare programmes: the PDS,
the MGNREGA, SSP and MDM schemes. For each scheme, three
broad themes are examined: recent evidence on corruption,
the government’s claims about Aadhaar’s contribution to
improved implementation of these schemes, and the emerging
evidence on disruption due to Aadhaar-integration.
Aadhaar-integration is planned in two ways. One, “Aadhaarseeding”
refers to adding a data fi eld for the Aadhaar number to
the software (Management Information System or MIS) that is
used to administer these programmes. This is supposed to be a
simple one-off activity, yet it is not quite as simple as it sounds.
For each scheme, each entitled person needs to be informed of
what is needed, a range of supporting documents may be required,
the number may not be correctly entered, etc. Further, in many
cases, re-enrolment of biometrics has been necessary as fi ngerprints
or iris scans become outdated. Aadhaar-seeding can help
with eliminating identity fraud by weeding out “bogus” benefi –
ciaries (for example, dead, non-existent persons, etc). Once 100%
Aadhaar-seeding is achieved, benefi ciaries in the MIS without
Aadhaar numbers are deemed to be bogus and are deleted.

 

Two, Aadhaar-based biometric authentication (ABBA) refers
to the practice of installing a Point of Sale (PoS) machine
equipped with a fi ngerprint reader and authenticating a person
each time she accesses her entitlements. For instance, at
the time of purchase of PDS grain each month, any one person
listed on the ration card needs to authenticate themselves.
Similarly, for pensions, elderly persons must go to the point of
delivery (for example, post offi ce or gram panchayat offi ce) to
authenticate themselves. ABBA serves the role of signatures (in
the pre-Aadhaar days). ABBA on PoS machines is currently a
monthly activity, so each of its associated technologies (correct
Aadhaar-seeding, mobile connectivity, electricity, functional
PoS machines and UIDAI servers and fi ngerprint recognition)
needs to work for a person to get their entitlement.
Public Distribution System
The PDS provides subsidised ration (mainly wheat and rice) to
entitled households through a network of PDS outlets. Corruption
has been a serious problem in the form of identity fraud
(for example, duplicates, ghosts, non-existent, etc) and quantity
fraud (that is, under-selling). Eligibility fraud and exclusion
errors have also plagued the PDS: according to one estimate,
half of the poorest did not have a below poverty line
(BPL) ration card in the early 2000s (Drèze and Khera 2010).
There are other problems in the PDS too (for example, overcharging,
quality of grain) that are not as well documented.
The latest year for which estimates of corruption are available
is 2011–12 (Drèze and Khera 2015a). These estimates are
made by matching administrative data on offtake by states
with PDS purchase by households reported in nationally representative
surveys such as the NSSO and India Human Development
Survey (IHDS). By 2011–12, a reduction in leakages was
well established in a handful of states such as Chhattisgarh,
Odisha, etc. For instance, in Chhattisgarh, leakages declined
from 50% in 2004–05 to around 10% in 2011–12.8 In the same
period, according to NSSO data, all-India leakages declined from
54% to 42%; and according to IHDS data, from 49% to 32%.
With the passage of the NFSA in 2013, the coverage of the
PDS was to be expanded to 75% of the population in rural areas
and 50% in urban areas. The act also mandated the drawing
up of a fresh list of entitled households. Two categories of
households were created: “priority,” with a per capita entitlement
of 5 kg per person per month and “Antyodaya,” 35 kg per
household per month (Puri 2017).
The initial evidence from fi eld surveys so far suggests that
the rollout of the NFSA has led to a further reduction in leakages
(Puri 2017). In states like Madhya Pradesh and West Bengal,
initial evidence suggests that the gains from the rollout of the
NFSA have been dramatic: entitled households now get more
than 95% of their entitlements in these states (Drèze and
Khera 2015b; Drèze et al 2016).
Close on the heels of the implementation of the NFSA, orders
for Aadhaar-integration in the PDS were issued on 7 November
(GoI 2014). Different states are at different stages of accomplishing
this.9 A handful of states (Andhra Pradesh, Jharkhand,
Rajasthan and Telangana), at least, have moved to a more or

less 100% Aadhaar-integrated PDS, including the use of PoS
machines with ABBA each month. The effects of Aadhaarintegration
are perhaps best documented in the PDS.
Identity fraud: The extent of identity fraud before the rollout
of the NFSA was not documented. The National Institute of
Public Finance and Policy’s (NIPFP) cost–benefi t analysis also
cites the lack of data on identity fraud as the reason for resorting
to ad hoc assumptions on this (Khera 2013a, 2013b). The government
relied, at best, on anecdotal evidence of “ghosts” and
“duplicates” to make its case for Aadhaar-integration.
After the rollout of the NFSA, the evidence suggests that
there is hardly any identity fraud in those states that used
Socio-economic Caste Census (SECC) data. In states like Bihar,
Jharkhand and West Bengal, the preparation of new ration
card lists was on the basis of the SECC (for example, see Drèze
et al 2015; Drèze and Maji 2016; Puri 2017). The SECC itself, if
anything, suffered from exclusion errors (for example, some
hamlets were not covered or individuals in households did not
get enumerated). In other states, which did not use SECC we do
not know the extent of identity fraud.
As mentioned earlier, 100% Aadhaar-seeding can help detect
identity fraud by linking each person’s Aadhaar numbers to
their name in the PDS database (and those names or cards that
are without an Aadhaar number can be deleted.)
The government claimed that `14,000 crore has been saved
in the PDS due to “deletion of 2.33 crore ration cards up to
2016–17 and better targeting of benefi ciaries” (GoI 2017c).10
Questions answered in Parliament suggest that these claims
are bogus. Data presented in Parliament is the most credible
source of information on deleted cards. It is clear that what the
government is referring to is “ineligible cards,” that is, “eligibility
fraud” (GoI 2016c). The answer is worth quoting at length:
The exercise of deletion of bogus/ineligible cards and inclusion of eligible
families is a continuous process and State/UT Governments are to
periodically carry out the same. As per the information received from
State/UT Governments, as a result of the continuous exercise of reviewing
the list of Ration Cards, a total of 2.33 crores ration cards have been deleted
during the period 2013 to 2016 [upto 16–11–2016] (emphasis added).
This suggests that all the deletions are due to ineligibility.
Digitisation or Aadhaar-integration cannot eliminate eligibility
fraud. Eligibility is determined by the criteria notifi ed by states
(for instance, living in a mud house, or caste status, etc). Aadhaar
does not provide this information.
The data refers to the period during which the NFSA was
rolled out, which further strengthens the possibility that the
deletions were due to new ration card lists being drawn up. To
illustrate, let us look at the data for Karnataka. In 2014, the
state deleted 40% of all ration cards, and in 2015, 7% of ration
cards were deleted (GoI 2016a). The total number of cards in the
state, however, declined from 1.31 crore to 1.29 crore (that is,
only 2%). Thus, though a large number of cards were deleted in
2014, almost an equal number were also added. This is true for
the all-India numbers too. The overall reduction of cards in
circulation is only 6.6%. By focusing on deleted cards, the government
has sought to distract attention from the real issue.

 

There is another problem with these numbers: in all states
except West Bengal, ration cards are issued for each household. In
West Bengal, each individual in each household gets a ration card.
Therefore, the reporting unit is not the same. However, in government
records, this difference in unit is not taken into account.
This has the effect of infl ating the number of cards deleted. Finally,
the large number of the deletions predate Aadhaar-integration
orders (of November 2014). By April 2016, only half of all ration
cards had been Aadhaar-linked, raising further questions about
Aadhaar’s role in detecting these “bogus/ineligible” cards.
Quantity fraud: Quantity fraud in the PDS refers primarily to
under-selling. In the manual system, cardholders were told to sign
off (“authenticate”) on full purchases even though they may be
given less (for instance, receiving 32 kg after signing for 35 kg).
With the introduction of the PoS machine and ABBA, the register
has been replaced with the PoS, but underselling continues more
or less as before. Several fi eld studies in Delhi (Shagun and Priya
2016; Nayak and Nehra 2017), Gujarat (Yadav 2016d), Rajasthan
(Yadav 2016a, 2016b; Khera 2017a) and Jharkhand (Drèze 2017a;
Drèze et al 2017) corroborate this.
One form of quantity fraud that ABBA may help with is the
“skipping” of months. In some states (for example, Bihar and
Jharkhand), apart from taking a cut each month, dealers also
siphon off entire months’ worth of rations. Similarly, in many
states, dealers could divert “leftover” rations to the open market.11
The introduction of ABBA can put to an end to skipping and
leftovers.12 Skipping used to be rampant in several north Indian
states, but there is evidence of a decline in recent years
pre-Aadhaar (Drèze et al 2015).
Exclusion, denial, higher transaction costs: In a handful of
states, ABBA is mandatory each month at a PoS machine installed
at the ration shop. As discussed earlier, ABBA has no
role in stopping identity fraud (that only requires 100% Aadhaarseeding),
and can do little to stop quantity fraud.
ABBA is contributing to exclusion from the PDS in a number
of ways. One, families or individuals without Aadhaar numbers
cannot register, so they cannot get the rations to which they
are legally entitled. In Delhi, for instance, without Aadhaar
names are not included on the NFSA card (Shagun and Priya
2016; Nayak and Nehra 2017). The central government directive
requires that as long as any one member’s Aadhaar number
is linked, everyone listed on the ration card will get grain.
While Jharkhand follows this rule, in Delhi and Andhra
Pradesh, the quantity of ration provided is according to the
number of Aadhaar numbers that are linked to the ration card
(Somanchi et al 2017; Bhattacharjee 2017; Khera 2017a; Hossain
2017). Missing Aadhaar numbers mean ration is cut.
Two, outright exclusion can also be because no member of
the family is “PoS-able” (a PoS-able person is one who is
Aadhaar-linked and whose fi ngerprints are recognised by
the PoS machine). Biometric failures have been recorded in
states using Aadhaar (Hindustan Times 2015; Bhatnagar
2016; Yadav 2016c, 2016e) as well as non-Aadhaar biometrics
(for example, in Gujarat).

 

Figure 1: Wheat Transactions through ABBA (% of successful cards and quantities)
Source: State NFSA portal for ABBA sales (http://food.raj.nic.in/DistrictWise_Allotment_
Distribution_Details.aspx), state supply (http://food.raj.nic.in/District_Allotment.aspx),
number of successful cards (http://food.raj.nic.in/DistrictWisePOSDetails.aspx) and total
NFSA cards (http://food.raj.nic.in/NFSASummaryDistrictWise_eMitra_NFSAYES.aspx).

Apart from these fi eld studies, questions in Parliament corroborate
some of these issues: for instance, the government
stated that in Rajasthan, in February 2017, “78% of NFSA benefi
ciaries have been provided wheat through PoS transactions”
and it is claimed there that there were alternative arrangements
(like one-time password or OTP) for the others (GoI 2017b).13
The government admitted that “the disruption in the Aadhaar
authentication services in Rajasthan … were on account of
‘inadequate server capacity of the Rajasthan Government,’
‘insuffi cient lease line capacity,’ ‘poor mobile signal at PoS
devices,’ ‘incorrect seeding of Aadhaar numbers in PDS database’,”
etc (GoI 2016b).

 

Offi cial data from Rajasthan shows that, from July 2016 to
June 2017, between 25% and 30% of one crore cardholders in
the state (accounting for 12%–35% of allotted grain) do not
buy grain from PDS outlets (Figure 1). Khalid (forthcoming)
reports similar results using the Jharkhand food data portal.
Earlier studies show that demand for PDS grain is high, and
that eligible households would not purchase PDS grain only
under very compelling circumstances (like when the entire
family being out in a particular month). The state food portal
does not show whether those who did not buy in any particular
month, tried at all later on.14

 

Apart from outright exclusion, ABBA is leading to sporadic
denial of ration and higher transaction costs. For example, only
those members whose Aadhaar number is seeded in the PDS
database can withdraw rations (Maji 2017). Technology failures
(like, connectivity, failure of fi ngerprint authentication, server
issues, etc) also contribute to these problems (Drèze 2016a).
A recent survey of 900 households in rural Jharkhand corroborates
the fi ndings that when ABBA works for entitled
households, it comes with higher transaction costs and little
protection against quantity fraud. Those who are excluded by
ABBA tend to be the most vulnerable: the elderly who cannot
walk, widow with young children, etc (Drèze et al 2017).
National Rural Employment Guarantee Act
The MGNREGA was passed in 2005, and guarantees 100 days
of work (per household) to any adult willing to work. As per
the act, at least 60% of total expenditure is on wages and the
rest on material. Corruption is observed in both wage and
material expenditure.

 

In 2008, the central government made it mandatory for
wages to be paid into bank and post offi ce accounts. The move

from cash payments to bank payments led to a sharp reduction
in corruption. NSSO data suggests that between 2007–08 and
2011–12 wage corruption declined from 44%–58% to 22%–32%
(Imbert and Papp 2015). Using IHDS data for 2011–12, Drèze
(2014) fi nds that the decline is even more impressive. Less
than 5% of MGNREGA work in government records was not
confi rmed by respondents. As with the PDS, the decline in
wage corruption predates Aadhaar-integration.
This suggests that contrary to government rhetoric, there are
other methods of reducing corruption in these programmes. In
MGNREGA, the dramatic reduction in wage corruption is because
of separation of the implementing agency (like the panchayat)
and the payment agency (banks and post offi ces) (Adhikari and
Bhatia 2010; Drèze and Khera 2008). Even with payments into
accounts, wage corruption can continue (Adhikari and Bhatia
2010). It can take three forms: extortion (forcibly taking wages
once labourers have withdrawn it from their account), collusion
(workers allow corrupt functionaries “use” their job card and
account to infl ate work on muster rolls and sharing embezzled
funds with them) and deception (operating the workers’ account
without their knowledge).

Extortion and collusion can be characterised as “quantity
fraud,” so ABBA cannot help. Deception is a form of identity
fraud where ABBA can help. Two caveats: one, we do not know
the size of deception in total wage corruption. Two, once wage
corruption through deception is blocked, those who were using
it may resort to extortion and collusion.
Savings: In the MGNREGA `7,633 crore is assessed to have
been saved up to 31 December 2016 due to Aadhaar-integration
(GoI 2017c).

 

In a news report the secretary, rural development ministry,
was cited saying that “with the use of IT [information technology],
Aadhaar, leakages have come down” (Chitravanshi
2016). An RTI query enquired about the methodology used to
“arrive at the assessment (qualitative and quantitative) on decrease
in leakages.” The response stated that “Mahatma Gandhi
NREGA has been covered under Direct Benefi t Transfer
(DBT) and savings are in terms of increasing the effi ciency and
reducing the delay in payments etc” (Sabhikhi 2017). In other
words, there was no estimate of savings, as initially reported.
Chatterji (2017) reported that in 2016–17, 94 lakh “fake” job
cards (approximately 8% of total job cards) were deleted. An
RTI reply (5 May 2017) revealed that out of all deleted job cards,
only 12.6% were classifi ed as “fake” or “duplicate” (Table 1). The

rest were deleted for other reasons, such as change of address,
surrender of job card, etc. Nearly 60% were deleted due to “other”
reasons. This could include (though that information is not
provided in the RTI response) those who did not submit their
Aadhaar numbers.15 Thus, there is as yet no credible evidence
of a reduction in leakages due to Aadhaar in MGNREGA.
Disruption: While the gains from Aadhaar-integration are
dubious, it has led to several new problems in the implementation
of MGNREGA. Demand for work can only be registered through the
MIS, and the MIS requires the Aadhaar number of an applicant
to proceed. Further, for payments to be processed, correct Aadhaar-seeding
in the MIS and at banks is required. This has led to
hardship, especially for those who are unaware of these requirements.
It has been nobody’s business to inform MGNREGA workers
of what is required of them. The administrative arrangements
for this are inadequate (for example, the task of seeding
as well as routine MGNREGA work falls on the same person), so
ultimately the whole programme has suffered from a slowdown.
In some cases, the re-engineering of MGNREGA to make it
Aadhaar compliant has meant that workers’ job cards are deleted
(Khera 2016a). In others, wrong seeding has led to delays in
payment (even non-payment) of wages. Adding to the confusion,
due to multiple waves of enthusiasm over “fi nancial inclusion”
over the years, some workers have ended up with several
accounts. The bank account in the MGNREGA MIS may be different
from the one that is Aadhaar-seeded. For these reasons, wages
are either seriously delayed, rejected or, even “lost.” The MIS
shows that the worker has been paid, but when workers enquire
at the bank, their account has not been credited.16
Social Security Pensions
The National Social Assistance Programme (NSAP) provides SSP
for the elderly, single-women, and disabled persons. The central
government contributes `200 per month and most states top up
this pension with a state contribution. The pension payment
mechanism varies from state to state. For example, in Odisha,
pensions are paid in cash at the gram panchayat each month,
whereas other states use money orders, post offi ce accounts,
bank accounts, etc (Drèze and Khera 2017). Corruption appears
to be less of a problem in pensions (Bhattacharya et al 2015).
An administrative problem that opens the door to identity
fraud is that states do not have a systems for keeping lists updated
(additions due to births and marriage, deletions due to
death or migration). This means that in states where payments
are made through bank accounts, the pension keeps getting
credited, but not withdrawn, until family members try to close
the account. In such cases, the extra credits (based on the date
of death in the death certifi cate) cannot be encashed by family
members, and are supposed to be reversed.
Where pensions are given in cash, anecdotal evidence suggests
that the disbursing functionary may continue the pension
for a few extra months on sympathy grounds (for instance, to help
the family tide over death ceremony expenses), or may siphon
off the money for some months, or may honestly strike off the
name to accommodate someone on the pension waitlist.

 

As with other schemes, numbers on identity fraud in pensions
are hard to come by. The Public Evaluation of Entitlement
Programmes (PEEP) survey conducted in 10 states verifi ed pension
lists covering 3,789 pensioners and found only one case of
duplication (Drèze and Khera 2017).
Savings or exclusion? The government claimed that Aadhaarintegration
saved `399 crore up to 31 December 2016 (GoI 2017c).
At a given level of benefi ts, a reduction in government expenditure
in any particular transfer scheme can be on two counts: removal
of ghosts and duplicates (“effi ciency”); and a fall in the
number of genuine benefi ciaries (“shrinkage”), for instance, if
they do not link their Aadhaar numbers when required.
Across welfare schemes, the government has been treating any
reduction in expenditure as “savings,” even when it comes from
shrinkage. This is true for SSP as well. For instance, in Rajasthan,
pensioners were “mistakenly” recorded as dead and this was
presented as Aadhaar-enabled savings (Yadav 2016f).
In Jharkhand too, pensioners’ names have been deleted
because they did not complete Aadhaar-seeding formalities or
pensions stopped due to seeding errors (Sen 2017a). Studying
100 pensioners, selected from 10 randomly-selected villages
from fi ve blocks of Ranchi district in February 2017, Biswas
(2017) fi nds that 84% of her respondents receive pensions but
irregularity in payments was a big issue. The remaining 16%
were not receiving it due to Aadhaar-related issues.
Hardship: Paikra (2017) documents ABBA-related exclusion,
disruption and hardship in a village of Surguja district
(Chhattisgarh). Apart from seven elderly whose pension had
stopped due to biometric and other technical failures, all
pensioners have to travel 9 km to collect their pension, the
nearest point with connectivity.
Abraham et al (2017) analyse fi ngerprint and iris authentication
data from Andhra Pradesh and Telangana for two schemes
(SSP and MGNREGA) for 2015–17. Failure rates are very high
among pensioners (14.4% and 17.4% for iris and fi ngerprints
respectively in Andhra Pradesh).
Among MGNREGA workers in Telangana, the failure rates are
7% and 7.8% respectively.
Mid-day Meals17
The MDM scheme is one of India’s most successful social policies,
with far-reaching benefi ts in terms of school attendance,
child nutrition, and learning achievements, among others
(Drèze and Khera 2017). The most pressing problems in the
implementation of the MDM—improving menus, providing
safe storage and cooking spaces, timely release of funds—cannot
be resolved by Aadhaar-integration.
According to a notifi cation issued by the Ministry of Human
Resource Development, if a child has not enrolled for Aadhaar
they are required to produce their enrolment slip as well
as two other types of documentation (an undertaking from the
parent or guardian that the child is not enrolled in any other
school and another ID document out of seven options provided)
in order to remain eligible for school meals (GoI 2017f).

 

Infl ated attendance as identity fraud: The rationale for Aadhaarintegration
was widely questioned (Bhatty and Sinha 2017; Drèze
2017b; Khera 2017b, 2017c). The only possible justifi cation for
using ABBA in the MDM scheme is to check “infl ated attendance,”
a form of “identity fraud.” If attendance infl ation exists, it could
be for two reasons: to make up for the shortfall in allocations to be
able to provide food according to the menu or to siphon off funds.
In such cases, administrators who fudge records should be
punished, not children. Moreover, there are better options to prevent
attendance infl ation. For instance, teachers can be instructed
to SMS the number of children each day, and the block offi ce can
make surprise checks to a handful of schools to verify this.
What is the evidence on infl ated attendance in the MDM
scheme? Monika Yadav (2017) matched the number of children
that are recorded in the government database with those reporting
having MDM in the IHDS in 2011–12. Government records
report that out of 143 million children enrolled in school, 105
million had been served school meals. Using IHDS data, Yadav
(2017) fi nds that between 100 million and 107 million children
report enjoying school meals. This means that the number of
children for whom expenditure is being claimed (roughly)
matches the number of children who actually enjoy school meals.
A glimpse of future disruption: Currently, the government
only wants to ensure that children enrol for Aadhaar by making
it compulsory for midday meals. Even the push towards
enrolment is likely to be hugely disruptive—it will likely derail
not just the mid-day meal programme, but also educational
activities in schools. Teachers and (over-stretched) school administration
will be forced to make arrangements for Aadhaar
enrolment. Once that is done, Aadhaar-seeding will waste
their time (Khera 2017b).
ABBA in schools has not yet been fully operationalised. If the
government proposes to move towards daily ABBA before serving
meals in the future, the move will cause further damage. The
technology failures discussed earlier (connectivity, authentication
failures) can arise here too resulting in a waste of time
as well as exclusion.
Some glimpses of what lies ahead were visible in a residential
school for tribal children in Jharkhand, where ABBA had started.
Out of 232 enrolled children, 190 were Aadhaar-linked. Out of
these though, while the online real-time portal was showing
that only 132 students were present, in fact, a headcount at the
school resulted in about 230 children being counted. Thus, out
of the 190 who were registered, not all were being recognised
by the machine. Similar numbers came up at other residential
schools in the area. In yet others, due to lack of electricity the
machine was not being used, teachers complained of time
wasted due to the slowness of the system, etc (Khera 2017d).
Other Aadhaar-related Claims
There are several other claims that do not stand up to scrutiny.
Some of these are briefl y discussed below.
LPG subsidy: The government’s boldest claims with respect to
savings due to Aadhaar relate to the LPG subsidy. In 2013–14,

the government initiated a “Direct Benefi t Transfer” (DBT) pilot
in 300 districts, whereby the subsidy would be credited into a
person’s Aadhaar-seeded bank account. Barnwal (2015) uses
data from this pilot to estimate potential savings, using per
unit subsidy and the reduction in LPG purchase that followed
the DBT. He admits that the reduction might be due not only
to lower leakages but also to “shrinkage:” that is, low UID penetration
and poor access to banks which ends up “completely
excluding genuine benefi ciaries” (Barnwal 2015: 19). Taking
his work further, George and Subramanian (2015) claim that
$2 million, that is, approximately 14,000 crore had been
saved due to Aadhaar-integration.18
According to Clarke (2016), however, George and Subramanian
(2015) misattribute savings to Aadhaar: the period under study
saw three major policy initiatives in LPG and a “connection
regularisation initiative” was the main reason for removal of
invalid connections, not Aadhaar-seeding. Similarly, LPG savings
after 2014 result from other government initiatives (including
PAHAL, GiveItUp, etc) and a reduction in international
oil prices (CAG 2016).
There are two further questions about the reliability of
these estimates. First, cabinet secretariat minutes for November
2015 record a savings of merely
91 crore due to Aadhaarintegration.
Two, after questions were raised by Misra (2015),
Sethi (2016) and Zhong (2016), George and Subramanian (2016)
themselves clarifi ed that what they were referring to was
“potential” savings rather than “actual” savings.19 In short,
there is absolutely no evidence that actual Aadhaar-enabled
savings in the LPG subsidy are anywhere near the initial projections,
still routinely quoted by government sources.
Biometrics and uniqueness: A crucial premise of the
Aadhaar project was that the use biometrics would ensure
that each person was issued a unique number. This assumes
that biometrics can work for such a large population, that the
inherently probabilistic nature of biometric technology would
not impede the programme in any way and that enrolment
procedures would not be violated ensuring the sanctity of the
database. There is no systematic data on this, but there is
enough to raise questions about this. In 2012, when 2.29 crore
Aadhaar numbers had been generated, 3.84 lakh had to
be cancelled as they were “fake” (Chauhan 2012; GoI 2013).
This appears to have been because of violation of due process
during the enrolment.

Elimination of ‘middlemen’: Among the early stated aims of
the UID project was also to eliminate “middlemen” or intermediaries
who were seen as the main source of corruption. For
instance, this referred to ration dealers in the case of the PDS,
or the postman who delivered money orders to pensioners. As
noted earlier, the corruption of the intermediary in these schemes
is being dealt with by other means (for instance, through
bringing greater accountability in the system).
Meanwhile, the Aadhaar project has spawned its own new
army of intermediaries, some of whom are also corrupt. This
includes enrolment agents, seeding agents, persons managing

kiosks (such as E-Mitra in Rajasthan, Pragya Kendras in
Jharkhand), data-entry operators in government offi ces and so on.
Role of Propaganda
The earlier sections demonstrate how the government’s “calculations”
of savings due to Aadhaar are more fi ction than
fact. Quite likely, a large part of what is passed off as savings
is, in fact, exclusion.
Despite mounting evidence to the contrary, the impression
that Aadhaar is helping the poor persists. The solution to this puzzle
lies in the fact that the Aadhaar project has relied—very
heavily—on propaganda. From the very beginning, perception
management through advertisements, branding, etc, has been a
part of the project’s strategy (GoI 2010a, 2010b). This short section
documents some of the strategies used to manage perceptions.
Favourable media stories: Convenient favourable stories at
crucial junctures is an oft-used strategy. Close on the heels of
important government announcements on Aadhaar-linking
(for example, on MDMs), stories of “fake” enrolments discovered
due to Aadhaar appear to create the impression that the decision
was justifi ed (Das Gupta and Pandey 2017; Kumar 2017).
Similarly, Chatterji (2017) reported deletion of nearly 1 crore
“fake” job cards. Follow-up investigations reveal that there
is little substance in these stories (Drèze 2017b on MDM and
Table 1 for MGNREGA).
During the hearings on mandatory linking of Aadhaar with
Permanent Account Number (PAN) cards in the Supreme Court,
reports on Aadhaar “data leaks” were emerging by the dozens.
Suddenly, there was a “feel good” heart-warming story of Aadhaar
helping pensioners in Rajasthan (Singh 2017).
Manipulative headlines: The reports on the discovery of
“fake” job cards and children show that actual events may not
match the facts on the ground. At other times, even when the
actual news is in sync with the news report, the headline can
be used to create a different impression. For instance, Parliament
questions clearly state that ration cards were deleted on
account of being ineligible/bogus. Yet government press releases
based on these answers drop “ineligible” and only use
“bogus” (see for instance, Financial Express 2016; Chatterji
2017; Parashar 2017). This creates the impression that Aadhaar
has helped weed out “ghost” or “fake” cards and has led to big
savings. Another commonly used trick is to put the cumulative
fi gure for several years in the headline (Das 2016, 2017).
Following widespread criticism of the move to make Aadhaar
mandatory for MDM, the government resorted to packaging
the move as “voluntary.” A news report headlined “Govt Relaxes
Aadhaar Norm, Makes Other IDs Acceptable for Subsidy
Schemes” suggested that the government had relaxed the
mandatory condition, even though there was no relaxation.
Labelling: “Labelling” is another way to shape perceptions. In
the early years, those who questioned the Aadhaar project were
labelled as “anti-technology” or Luddites (Joseph 2017). In a
case of the pot calling the kettle black, those who raise diffi cult

questions have been called “vested interests” by the very people
who need to refl ect on their own confl ict of interest. In desperation,
the head of ISpirt (an association of private entities building
businesses using Aadhaar) resorted to setting up anonymous
twitter handles to troll critics, even calling them “ISI agents.”
Media diplomacy: The media strategy to create Aadhaar’s brand
image includes “damage control.” After the Hindustan Times
published a critical opinion in 2010, the editor received a phone
call from the chairperson of the UIDAI expressing reservations
about its publication. Sometimes, newspapers “make amends”
by publishing gushing editorials soon after (Drèze 2010).
The Wall Street Journal questioned estimates on LPG savings
(21 March 2016). This forced a response: on 2 April, a clarifi cation
appeared in the Indian Express (George and Subramanian 2016).
On 6 April, an episode of “Walk the Talk” with Nandan Nilekani
(former UIDAI chief) appeared. The interview completely
ignores the “retraction” on savings estimates, as if there was no
controversy around them, and provides a platform to repeat the
discredited fi gures. The interview itself looked suspiciously like
an attempt to whitewash any damage from the retraction.
‘Sponsored studies’: Another element that has fuelled the
propaganda is “sponsored studies” (Drèze 2016a). The chief
executive offi cer (UIDAI) cited a potential savings of $11 billion
due to Aadhaar on the basis of a World Bank report (Pandey
2017). This so-called estimate was called out in 2016. The
World Bank report in question “mistakes” the Indian government’s
total budget ($11 billion) on cash transfer programmes,
as “savings” (Khera 2016b).20
Several such reports are likely in the pipeline. Philanthropists
such as Omidyar Network, with known business interests in
fi ntech, are funding large research projects on Aadhaar (Solodkiy
2016). Fintech will likely receive a big boost due to Aadhaar
(Ranade 2017). A “State of Aadhaar” report prepared by
IDInsight provides a glimpse into what might be in the pipeline
(Abraham et al 2017). Discussing data accuracy, it uses data
from 2012, ignoring a recently reported fi gure (in Parliament)
that 49,000 enrolment agencies were blacklisted for fraudulent
practices (Abraham et al 2017: 18–19).
The Aadhaar project started in an evidential vacuum. Over
time, their initial assumptions are getting invalidated. Confronted
with this embarrassing situation, the response has
been to set in motion an elaborate propaganda machinery to

fabricate a different reality. Given the concerted “media strategy”
of the UIDAI since its inception, the favourable impression
in people’s minds is hard to dislodge, in spite of the growing
evidence of exclusion, denial and hardship.
Concluding Remarks
The Aadhaar project began without much understanding of
the problem that it was expected to solve. In spite of the early
warnings about the possible damage it might cause, the project
has been scaled up and in the offi cial narrative, it is a great
success (“shallow draughts intoxicate the brain”). This paper is
an attempt to “drink deep” and sober us again.
Available evidence does not substantiate any signifi cant gains
from Aadhaar-integration in welfare programmes. On the
contrary, it has infl icted considerable pain. Apart from (supposedly)
one-time costs of enrolment and Aadhaar-seeding, people
are now faced with higher transaction costs on a monthly
basis (in pensions and the PDS for instance), and in a signifi cant
minority of cases, also face exclusion and denial. Even when it
works, people suffer from considerable indignities.
Aadhaar-integration has also facilitated over-centralisation of
administrative controls. If a person does not get authenticated,
there is no easy or accessible redress available. Even with the
best of intentions, PoS machine operators may not be able to
ascertain the meaning of a particular error message (there are
over 50 error codes!) and guide affected persons on what to do. An
error in the system (wrongly entered entitlements for instance)
can only be corrected at far-off servers. In this and other ways,
Aadhaar-integration has reduced transparency and accountability
in the system and added to a sense of disempowerment.
Another damage is the “displacement” effect. Privileging
Aadhaar over all other technologies, which have a proven record
of improving administration, displaces efforts to scale up those
technologies. For instance, in Bihar, the uploading of NFSA ration
card lists (very important to enhance transparency and reduce
the arbitrary power of PDS dealers) was delayed. Under pressure
from the central government, the short-staffed food department
had to focus on Aadhaar seeding rather than uploading
NFSA lists. Abandoning experiments with smart cards, in lieu of
ABBA, for last-mile authentication is another example.
The use of Aadhaar is an admission of “governance” failure.
The government has failed to hold to account the minority who
indulged in corrupt practices. Instead, by deploying untested and
fragile technology, the victims of corruption are paying the price.

 

NOTES

1 This section follows up on Khera (2015) with
updates.
2 See Indian Express (2014), for instance. Low
rates of birth registration would be mentioned
in passing, but according to the Civil Registration
System data (GoI 2017e), in 2009 just over
80% of births of children under fi ve years
were registered. According to National Family
Health Survey data the corresponding fi gure
for 2015–16 is 80% (GoI 2017d).
3 The full list includes the documents that can be
produced for children (GoI no date).
4 On 19 March 2015, in Rajya Sabha it was stated
that “more than 9 crore enrolment packets
have been rejected so far, that did not meet the
quality and de-duplication criteria,” due to suspected
fraud, duplication, etc (GoI 2015).
5 The issue of caps, and a move towards relaxation,
has been discussed in Drèze and Khera (2017).
6 This section builds on the discussion in Khera
(2011).
7 This is not always the case, for example, labourers
may not always be given their passbooks,
overburdened banks may rely on “middlemen”
(such as sarpanches or panchayat secretaries) to
facilitate bulk withdrawals on behalf of workers,
etc. These loopholes are discussed later.
8 The reduction in leakages in these states
before the NFSA was largely driven by PDS
reforms (see the literature cited in Drèze and
Khera 2017).
9 According to Government of India (2017a), 73%
Aadhaar seeding had been achieved in the
PDS, but there are some questions about this as
well (Hindu BusinessLine 2016).
10 An earlier news report (after a government review
in May 2016) claimed savings of `10,000
crore in the PDS from deletion of 1.6 crore bogus
cards (Indian Express 2016).
11 Leftover rations refer to that part of the stock
that remains unsold at the end of the month
because cardholders did not claim it. In many
states, there is a “carryover” facility, that is,
benefi ciaries could claim missed rations in

subsequent months, so quite likely the share of
leftovers was quite small.
12 However, the Jharkhand experience suggests
that because of the way in which it was rolled
out, it is the ration card benefi ciaries who are
paying the price, not the dealers.
13 This, however, contradicts fi eld reports and
subsequently, a Government of Rajasthan circular
dated 24 March, withdrew the OTP facility
(Thomas 2017).
14 Aadhaar-integration was supposed to bring
greater transparency and provide real time access
to data, but this promise has not been fulfi
lled in any meaningful manner (more on this
below).
15 There was tremendous pressure to achieve 100%
Aadhaar-seeding in the MGNREGA MIS. In order
to meet these targets, anecdotal evidence suggests
that fi eld-level functionaries simply deleted
job cards of those who failed to give Aadhaar
numbers (Khera 2016a; Aggarwal 2016).
16 On these issues in the payment system, see
Aggarwal (2016, 2017), Dhorajiwala and Narayanan
(2016), Dhorajiwala et al (2017); Sabhikhi
(2017); Sen (2017b).
17 This section builds on Khera (2017b) and Khera
(2017c).
18 This fi gure is also in Government of India (2017c).
19 Alternate estimates are more conservative:
only 3% reduction, compared with the government’s
claim of 24% (Clarke 2016; Lahoti 2016;
and Venkatanarayanan 2017). Interestingly,
when government estimates were challenged
in the Supreme Court (in Shantha Sinha petition),
the government chose to ignore these
questions.
20 The World Bank relies on Banerjee (2015) for
the “savings estimate,” but she only reports the
total budget of India’s cash transfer programmes
(NREGA, pensions, etc): “The value of these
transfers is estimated to be `70,000 crores
($11.3 billion) per annum.”
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SOURCE- DECEMBER 16, 2017 vol liI no 50 EPW Economic & Political Weekly

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