Aug 1, 2020
Improving Beneficiary Identification for Welfare Delivery
In this blog, I have discussed the various approaches for identifying beneficiaries and how we are aiming to solve the related problems with the help of a Recommendation Tool
Refer to Original Blog Series on https://www.vaktavya.co.in/
“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 the fulfillment of scheme-specific socio-economic eligibility criteria. Income Certificate and Socio-Economic Caste Census (SECC) remain two main data sources used in the identification of beneficiaries under the Targeted Approach in India.
With an increasing focus on welfare schemes by governments, the pertinent question that remains in the 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:
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 the 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:
- Issuance without verification: In most 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 the issuance of the certificate. Many citizens provide false declarations to become eligible for welfare benefits.
- Lack of timely update: 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.
- 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 the 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 that is transforming fast, whilst some people have become economically strong others have fallen on hard times.
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 :
- The 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.
- 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.
The inherent challenges in any targeting exercise suggest 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 that 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.
1. https://www.cprindia.org/policy-challenge/7950/economy-%26-the-welfare-state
Originally published at https://www.vaktavya.co.in on August 1, 2020.
