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Beneficiary Identification (part 1 of quad-blog series)

Journey Towards Effective Targeting & Delivery of Welfare Services


In this series of blogs, we will discuss the various approaches followed for identification and targeting of beneficiaries for the delivery of welfare and their respective merits and demerits. We will also discuss how moving from an individual centric welfare system to a family centric welfare system can make poverty alleviation interventions more effective.

Finally, we will discuss how EasyGov is aiming to solve these problems with the help of our Artificial Intelligence/Machine Learning enabled “Family Centric Progressive Welfare Recommendation Tool”. We will discuss how we arrived at this solution and why we believe our tool has the potential to change the course of welfare delivery not only in India but globally.

In this Part-1 of the series we will discuss the current approaches to identify beneficiaries and the shortcomings of these approaches.


“Benefits meant exclusively for the poor often end up being poor benefits.” – Amartya Sen. Over the last few years, India has taken important steps toward significantly reforming its welfare architecture including JAM (Jan Dhan, Aadhaar and Mobile) for monetary direct benefit transfers. This has significantly reduced the leakages in welfare delivery. However, beneficiary identification remains the biggest challenge in welfare delivery. The system suffers from significant exclusion and inclusion errors.

For the delivery of welfare services to the needy, India currently follows the “Targeted Approach”. In the Targeted Approach welfare transfers are made to selected beneficiaries based on fulfillment of scheme specific socio-economic eligibility criteria. Income Certificate and Socio Economic Caste Census (SECC) remain two main data sources used in identification of beneficiaries under the Targeted Approach in India.


The Limitations of Current Targeted Approach:

With an increasing focus on welfare schemes by governments, the pertinent question that remains in Indian welfare-ecosystem is who should be targeted and how should they be targeted.

Let us analyze challenges associated with two of the data sources most widely used with targeted approaches:

Income Certificate

The beneficiary’s eligibility is usually based on self declared income by an individual. However, in a country like India, where the income data is inaccurate and the majority of population is not included in the tax system, there is a high probability of incorrect data on income certificates. The common reasons for inaccurate income certificates are:

  1. Issuance without verification : In most of Indian states, a mere verbal declaration of self income is enough to get the income certificate. There is no mechanism to verify the information provided prior to issuance of the certificate. Many citizens provide false declarations to become eligible for welfare benefit.

  2. Lack of timely updation : There is no set procedure to update the income certificates after a set period. Citizens are often not aware of the need to renew the income certificate thus their welfare applications are likely to be rejected.

  3. Fake or forged Certificates : In the absence of standardized and authenticated certificates, there is a possibility of forgery of certificates.

Socio- Economic Caste Census (SECC) Database :

1. Discrepancies in the set of data : A comparison with data from the 2011 census and National Family Health Survey (NFHS 2015-16) suggest that there are considerable differences when it comes to identification of the most backward districts. This was established by a study whose findings are summarized below.


A ranking exercise was undertaken according to the asset ownership data from the Census, and the percentage of households identified as poor on the basis of the Multidimensional Poverty Index (MPI), created by the Oxford Poverty and Human Development Initiative, based on the NFHS data 2015-16). All districts were ranked in five quintiles according to the percentage of deprived households. The following table shows the percentage distribution of districts according to SECC, Census and MPI in different quintiles, with the bottom quintile being most deprived and fifth quintile being least deprived.

Of all the districts classified as the most deprived (Bottom Quintile) by the MPI, 48% of them are found to be the most deprived according to the census. However, the match between the MPI and the SECC is a dismal 25%.

Conclusion : There is considerable overlap between MPI and Census although they were conducted five years apart. In contrast, there is a smaller overlap between the SECC and the census, which were both conducted in the same year.This shows large data discrepancies.


2. SECC database is not current : SECC database is already nine years old in an economy which is transforming fast, whilst some people have become economically strong others have fallen on hard times.


Beneficiary Identification Approaches in the Developing World

Most developing countries rely on proxy-means tests. Proxy mean tests are periodic censuses that classify households based on observable and verifiable information on households’ assets, amenities.

The first step is the choice of poorest localities to participate in the program. After selecting localities, a census takes place to capture information on a households’ main socioeconomic characteristics. Government enumerators go door-to-door, often visiting millions of households. They ask about assets, all of which are easy to observe directly. Example: televisions, refrigerators, number of rooms in one’s house, roof material used and so on. Using data from this census, households are classified as “eligible” or “non‐eligible” for the program. Other forms of means‐test are the unverified means test used in Brazil and the verified means test, the gold standard of the means‐test, used in the United States.

Challenges with Proxy-Mean Tests :

  1. Exact formula to calculate the proxy-means score is often kept secret because if it is known, households may strategically misreport or hide assets to make sure they fall under the cutoff.

  2. Applying such secret formulae robs the process of transparency, and may invite charges of political favouritism in a keenly-contested democracy such as ours.

Target government assistance in India is based on self-reported and unverified income, but this is the exception, not the rule, because people can lie if there is no way of verifying it.


Conclusion :

The inherent challenges in any targeting exercise suggests that schemes with simple exclusion criteria based on regular and professionally conducted censuses may be better for a country such as India. However, due to the inherent challenges in collecting data from over 300 million families on a periodic basis is a mammoth task which is not practical. Additionally, incorrect data given by citizens compounds the problem. Technology tools on large data sets can help in refining proxy mean tests.

Please read part two of the blog to understand how EasyGov solves the problem of beneficiary identification using machine learning with the data available.

To be continued…







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