wordpress blog stats
Connect with us

Hi, what are you looking for?

Government’s claims of Aadhaar savings for the MNREGA scheme are overstated


By Anand Venkatanarayanan

This is Part 4 of a series of analysis by Anand Venkatanarayanan related to claims made about Aadhaar. Read Part 1 and Part 2, which were written in response to statements made by former UIDAI head Nandan Nilekani, and Part 3, which is an analysis of the claims related to LPG savings.

The Government of India has argued in the Supreme Court and Parliament that its primary purpose in promoting Aadhaar is to improve the efficiency of social schemes, and that the use of Aadhaar has generated huge savings since 2014.
The UIDAI (in a statement), its CEO Dr AB Pandey (in a column) and the Attorney General of India (in the Supreme Court) have claimed that use of Aadhaar since 2014 has generated huge savings. This claim has been repeated frequently by Nandan Nilekani (here, here and here). Of this figure ₹7,633 crore (around 14%) is attributed (Rajya Sabha Starred Question 384, dated 07.04.17) to savings from MNREGA (Mahatma Gandhi National Rural Employment Guarantee Act) scheme .

Understanding the MNREGA Scheme

MNREGA is a right to work scheme that aims to provide livelihood security in rural areas by providing at least 100 days of wage employment in a financial year to every household whose adult members volunteer to do unskilled manual work.

One of the most common criticism of the implementation of the program is that it enables the routing of payments to ghost beneficiaries (ghost here means non-existent). There had been instances where corruption has been reduced through other means such as social audits and muster roll verification. The linkage of Aadhaar with job cards was one such method to detect ghost beneficiaries.

The Government of India claims that linking beneficiary accounts with their Unique ID (Aadhaar number) helps in weeding out ghost beneficiaries and once a ghost beneficiary is identified, their job card can be cancelled or deleted which prevents fund diversion. The fund diversion, thus prevented is referred as savings enabled by Aadhaar in MNREGA scheme.

Understanding the impact of the Aadhaar on the MNREGA scheme

As per Government of India, the savings that can be attributed in NREGA via Aadhaar is shown below:

However an RTI query on how much Aadhaar contributed to this savings and the methodology used to arrive at the assessment did not provide any useful data.

Since Government of India has neither published any methodology used to estimate the savings claimed nor has provided data points used in the methodology, it renders the above claim unverifiable. However it is possible to provide a credible estimate of Aadhaar savings on MNREGA from publicly available data using a methodology that does not require any special skills to comprehend the figures presented.

How to calculate Aadhaar savings in NREGA

We can calculate Aadhaar savings in NREGA using the following methodology:

  1. Linking Aadhaar numbers with MNREGA job cards can be used to detect duplicates and non-existent persons, which can then be deleted. The ratio of deleted cards to seeded cards called “blocking efficiency”.
  2. Aadhaar card seeding has been a continuous process and monthly reports on seeding data is available at the Direct Benefits Transfer (DBT) website (See DBT Reports). Hence we have to use monthly tables to calculate savings data and sum them up for the appropriate year.
  3. It is possible to obtain only average monthly wage per job card across the entire country from public sources (specified elsewhere in this article). We can then use the formula:Monthly Savings = Seeded Job Cards x Blocking Efficiency x Average Monthly Wage

Blocking efficiency

The first data point that we need is the blocking efficiency of Aadhaar in the NREGA scheme (i.e.) Ratio of Duplicates detected via Aadhaar Seeding to Aadhaar seeded Job cards.

Duplicate Job cards on 05.03.2015 (Lok Sabha Unstarred question 1826) is 63,493 across all districts. However it does not mention the number of seeded cards on that date, and we have to use a bit more ingenuity for deriving it, from other public data sources.

The number of job cards seeded with Aadhaar on 21st October, 2014 was 2.06 Crores (Source) as shown below.

From the above table, we can understand that on average, we can use the ratio of 1.045 to convert seeded cards in only DBT trial districts to seeded cards across whole of india.

The second seeding datapoint is available from the finance ministry (Deleted now but archived by wayback machine) and is shown below:

Using the conversion ratio of 1.045 from 300 DBT districts to all districts in the first table, we can now calculate the job cards seeded on 31/03/2015 as shown below:

Using linear extrapolation between these two days, we can show that the job cards seeded on 5th March 2015 was 3,14,85,164 (See excel worksheet ‘Deduplication rate’ for details)

Hence the Blocking efficiency for Aadhaar in NREGA is 63,493 cards blocked for 3,14,85,164 seeded connections, which is equal to 0.2030893%.

Number of NREGA Job Cards and Monthly Wage Rate

The next data point that we are looking for is the number of job cards issued on different years and the wage expenditure incurred on those job cards during those years. Fortunately Lok Sabha Unstarred Question 1196 (09.02.2017) and Lok Sabha Unstarred Question 2246 (29.06.2016) provides these data points and are summarized in the table shown below:

Analysing 2014–15 Savings

It is now possible to calculate NREGA savings for every month using the formula:

Seeded Job Cards x Blocking Efficiency x Monthly Wage Rate

We will be using the seeding rate of 80,064.34 cards per day, to compute the seeded job cards for months where seeding data is not available publicly, which are April — October 2014 and November 2014 — Feb 2015.

Analysing 2015–16 Savings

We will use the same methodology outlined above for 2015–16, but will use the appropriate data set for the year. Government Of India regularly publishes Aadhaar seeding data from September 2015 (See monthly DBT reports) onwards for all DBT schemes, which makes it easier to do savings calculation without linear extrapolation. However for the months in between (April 2015 — August 2015), we still have to do linear extrapolation to calculate Aadhaar seeded job cards as shown in the table below.

We can now complete the analysis for 2015–16, as we have all the required data.


On the basis of the calculations provided above, the actual gross saving generated by Aadhaar in NREGA from April 1, 2014 to March 31, 2016 is ₹30.03 Crores without including any of the costs of implementing and operating Aadhaar or the DBTL program.

List of Assumptions in this calculation

  1. Calculations are based on publicly available data provided by Government of India in the parliament. Hence accuracy of the calculations are dependent on the data provided by the government of india.
  2. The deduplication rate of 0.20% is a single number. Unlike the LPG calculations, where there were multiple estimates available for the deduplication rate because of various trials conducted by Government of India, all we have for NREGA is a single data point as we have limited ourselves to using publicly available data only.
  3. The LPG example has shown us that deduplication rate tends to be within a very tight range as the number of seeded connections increase.


Data from the twitter handle India subsidy data.


Anand V is a Senior engineer at NetApp. Views expressed in this article are personal and does not reflect the views of his employer.

This article is released under CC-BY license. You may repost this article in entirety as long as the above disclaimer is mentioned with attribution to the author and a link to the original article.

Crossposted from here.

Written By

MediaNama’s mission is to help build a digital ecosystem which is open, fair, global and competitive.



Factors like Indus not charging developers any commission for in-app payments and antitrust orders issued by India's competition regulator against Google could contribute to...


Is open-sourcing of AI, and the use cases that come with it, a good starting point to discuss the responsibility and liability of AI?...


RBI Deputy Governor Rabi Shankar called for self-regulation in the fintech sector, but here's why we disagree with his stance.


Both the IT Minister and the IT Minister of State have chosen to avoid the actual concerns raised, and have instead defended against lesser...


The Central Board of Film Certification found power outside the Cinematograph Act and came to be known as the Censor Board. Are OTT self-regulating...

You May Also Like


Google has released a Google Travel Trends Report which states that branded budget hotel search queries grew 179% year over year (YOY) in India, in...


135 job openings in over 60 companies are listed at our free Digital and Mobile Job Board: If you’re looking for a job, or...


By Aroon Deep and Aditya Chunduru You’re reading it here first: Twitter has complied with government requests to censor 52 tweets that mostly criticised...


Rajesh Kumar* doesn’t have many enemies in life. But, Uber, for which he drives a cab everyday, is starting to look like one, he...

MediaNama is the premier source of information and analysis on Technology Policy in India. More about MediaNama, and contact information, here.

© 2008-2021 Mixed Bag Media Pvt. Ltd. Developed By PixelVJ

Subscribe to our daily newsletter
Your email address:*
Please enter all required fields Click to hide
Correct invalid entries Click to hide

© 2008-2021 Mixed Bag Media Pvt. Ltd. Developed By PixelVJ