Spotlight on Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS)

The State of Aadhaar Report 2017-18 highlights key lessons regarding the use of Aadhaar for service delivery in the Public Distribution System. The survey we conducted also collected data on the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS). While the survey results do not fully assess the impact of Aadhaar on MGNREGS, our results provide insight into the integration of Aadhaar within the system.

MGNREGS is a significant social protection programme of the Government of India, which aims to provide at least 100 days of guaranteed wage employment in a financial year to every rural resident who demands work.[1] Last year the Government of India allocated ₹48,000 crore ($6.9 billion) to this programme.[2] The programme implementation has often been criticized for problems such as exclusion errors in the muster rolls (or labour attendance registers), payment delays, payment failure, and low completion rates.[3] Similar to other welfare programmes, Aadhaar was introduced to try to prevent corruption, curb leakages, and to better target beneficiaries.

For the State of Aadhaar Report 2017-18, we surveyed nearly 3,000 households in rural areas of Andhra Pradesh, Rajasthan, and West Bengal. The survey also collected data on MGNREGS across 1,379 households in the three states. Our data accompanied by administrative data from the MIS (Management Information System) portal and work done by other researchers enables a better understanding of the role of Aadhaar within MGNREGS.

The objective of better targeting MGNREGS beneficiaries using Aadhaar can be met in three ways:

  1. Seeding/linking of Aadhaar numbers to beneficiaries’ job cards, which allows for de-duplication or removal of ghost beneficiaries in the MGNREGS database. The process is intended to reduce leakages stemming from siphoning off benefits to non-existent beneficiaries.
  2. Seeding of Aadhaar numbers to beneficiaries’ bank accounts, which enables the government to use Aadhaar Payment Bridge System (APBS). Within such a system, a beneficiary’s wages are meant to be transferred directly into a bank account mapped to their Aadhaar number.
  3. Withdrawal of cash using Aadhaar-based biometric authentication to ensure that wages are withdrawn by the intended beneficiary. Aadhaar’s authentication ability (the third use listed in Figure 1) is currently used for disbursing benefits in cash in Andhra Pradesh.

For this blog post we examine all three of these uses and highlight areas for further research.

Aadhaar Seeding

Nearly 89 percent of the active MGNREGS workers have been seeded to Aadhaar as per the MIS portal.[4] We aim to answer two key questions to better understand the effectiveness of seeding:

  • Benefit analysis: Did Aadhaar seeding lead to the removal of duplicates and ghost beneficiaries?
  • Cost analysis: Did Aadhaar seeding lead to the exclusion of genuine beneficiaries?

While uniqueness of Aadhaar numbers can be used to remove fake/duplicate job cards from the database, its role in removing ineligible/migrated beneficiaries remains limited. A Right to Information (RTI) request filed by Jean Drèze, a Visiting Professor at the Department of Economics at Ranchi University, revealed that of the 9.4 million job cards deleted in 2016-17, only about 12.6 percent job cards were deleted because it was fake (4.0 percent) or duplicate (8.6 percent).[5] The remaining 87.4 percent were deleted due to other reasons such as migration or that the individual no longer wanted a job card.

As per the latest figures released on the DBT portal, upward of ₹16,000 crore ($2.5 billion) has been saved under the MGNREGS through March 2018 due to Direct Benefit Transfers (DBT) and other governance reforms that have enabled targeting of genuine beneficiaries. Of this amount, the government’s calculations estimate 10 percent of these savings can be attributed to savings on payment of wages to duplicate, non-existent, and ineligible beneficiaries.[6] However, of this only a fraction can be attributed to Aadhaar itself on account of deletion of fakes and duplicates. The exact fraction remains unclear due to unavailability of data on the breakdown of the types of duplicates.

The State of Aadhaar survey 2017-18 also found that about 2 percent of the respondents with a job card stated that their name had been removed from the beneficiary list as a result of Aadhaar seeding. Extrapolating to the rural populations of the three survey states using MGNREGS MIS data, this represents about 670,000 individuals of the 32 million active workers across our three states. The survey evidence aligns with newspaper reports highlighting problems in implementation with respect to Aadhaar seeding such as deletions due to unavailability of Aadhaar number.[7] This leads us to conclude that issues in seeding of Aadhaar exist and has led to exclusion of genuine MGNREGS beneficiaries at the time of implementation. It should be noted that this experience is not limited to MGNREGS.

Aadhaar Payment Bridge System

The introduction of Direct Benefit Transfers (DBTs) through the Aadhaar Payment Bridge System (APBS) has often been cited as a source of increased efficiency in the programme.[8] Data collected through our survey indicates that more than 82 percent of the beneficiaries found the DBT system of receiving wages in their bank account, “easy.”[9] However, in understanding the gains in efficiency by moving from NEFT to APBS, the specific benefits for Aadhaar in facilitating such payments remains unclear.

Delayed wages still remain a concern within the programme. While workers are entitled to receive payments within 15 working days from the completion of work, a 2017 study found that wage payment, on average, is delayed by 94 days after approval from the Funds Transfer Officer (FTO) in the study’s ten sampled states.[10] The problem of payment delays has decreased considerably from nearly 57 percent in 2016-17 to 8 percent in 2018-19 so far; however, t is uncertain whether any of this can be attributed to Aadhaar.[11] Other researchers in the field believe that the new Aadhaar-based system has only added more layers of issues such as rejected payments, diverted payments, and locked payments.[12]

Aadhaar authentication for wages in cash

The third use for Aadhaar encompasses its biometric authentication feature for withdrawal of wages in cash. According to our survey, around 51 percent of the beneficiaries in Andhra Pradesh had used Aadhaar-based microATMs to withdraw MGNREGS wages in cash in the last nine months.

One proxy for better understanding ‘ease of use’ of this feature is authentication failure. Data from the Andhra Pradesh disbursement portal indicates that 12 percent of the beneficiaries faced authentication failure in December 2017. Whether authentication failure translated to denial of service remains unknown. Data from the State of Aadhaar survey allows us to estimate the proportion of beneficiaries who faced authentication failure and were also subsequently excluded from receiving their wages. Of those who had used microATMs in Andhra Pradesh, 2.5 percent of the beneficiaries were unable to withdraw their wages in cash.


In conclusion, seeding of Aadhaar to job cards has de-facto led to exclusion of genuine beneficiaries; little evidence exists to understand the effectiveness of DBTs using APBS to process wage payments; and biometric authentication failures at the time of cash withdrawal can create further delays for the scheme’s beneficiaries.

The implementation of the MGNREGS programme has seen gradual improvement over the years but there are still areas where the programme can be improved. The low completion rate, the high amount of unpaid and delayed compensation, and cases of exclusion still need to be addressed. However, the role for Aadhaar in mitigating these issues remains unclear.

Further research is required to isolate which point(s) of failure Aadhaar can address without leading to serious unintended consequences such as exclusion. For this purpose, we need more data to determine: 1) What is the extent of removal of duplicates versus unintended exclusion due to Aadhaar seeding? 2) Can the Aadhaar Based Payment System address payment delays and payment failure? And 3) Has withdrawals of Aadhaar-based payment using micro-ATMs enhanced user experience?



[1] The Mahatma Gandhi National Rural Employment Guarantee Act. 2005.

[2] Open Budgets India. “Union Budget (2017-18) – Department of Rural Development.” Dataset. Department of Rural Development, Government of India. Accessed August 1, 2018.

[3] “Budget Brief: Mahatma Gandhi National Rural Employment Guarantee Scheme.” Centre for Policy Research: Accountability Initiative. Accessed August 1, 2018.

[4] “MNREGA Dashboard: At a Glance.” Ministry of Rural Development, Government of India Accessed August 1, 2018.

[5] Khera, Reetika. 2017. “Impact of Aadhaar on Welfare Programmes.” Economic & Political Weekly Vol LII No 50 (December).

[6] “Estimated Gains.” n.d. Direct Benefit Transfer, Government of India.

[7] Khera, Reetika. 2016. “For NREGA, Tamil Nadu Is The Only Hope.” NDTV, February 1, 2016.

[8]  APBS Banks FAQs. Accessed August 2018.

[9] The survey question we asked for this data point was: “Overall, how easy or difficult do you find the process of receiving your benefits directly in your bank account?” The enumerator read out all options (easy, neutral, or difficult) for this question.

[10] Rajendran Narayanan, Sakina Dhorajiwala and Rajesh Golani. 2017. “With Shifting Accountability on NREGA Payment Delays, Workers Continue to Be Denied Their Due.” The Wire, December 5, 2017.

[11] “At a Glance.” n.d. The Mahatma Gandhi National Rural Employment Guarantee Act 2005. Accessed August 24, 2018.

[12] Drèze, Jean. 2018. “Hollowing out a Promise.” The Indian Express, July 13, 2018.