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  1. Column: DBT stuck in implementation

Column: DBT stuck in implementation

If firming up the BPL list is taking a long time, how can the DBT work?

By: | Published: March 16, 2015 12:53 AM

In the first half of May, out of the 18-plus population, 786 million have got Aadhaar numbers. The largest absolute numbers are in Maharashtra, UP and West Bengal. There are two broad channels for Aadhaar enrolment—the Unique Identification Authority of India (UIDAI) itself and the Registrar General of India (RGI). For instance, in Lakshadweep, Dadra & Nagar Haveli, Tamil Nadu, West Bengal, Odisha, Nagaland, Manipur, J&K, Mizoram, Arunachal, Meghalaya and Assam, the enrolment responsibility is with RGI. The UIDAI is responsible for other geographical areas. One test of how enrolment is progressing is to gauge what percentage of the population has been covered. If you want to be statistically correct, this isn’t that simple. The distribution of the 18-plus population is available for Census 2011, not 2015. To get the denominator, one can extrapolate those numbers. Alternatively, one can use 2011 numbers as a base, recognising that population growth will mean one may get more than 100% enrolment. If one is interested in trends, extrapolation seems unnecessary. One may as well use 2011 Census numbers. By the way, all enrolment numbers are public domain information on the UIDAI website. For the record, there is another comparability issue, that of comparing residents versus citizens. But that may not be quantitatively that important.

For UIDAI-driven enrolment states/UTs, 86% of the target population has then been covered, with a range from 127.6% in Delhi to 46.3% in Bihar. For RGI-driven enrolment States/UTs, 82% of the target population has been covered, with a range from 108% in Lakshadweep to 1.1% in Assam. At that broad-brush level, the UIDAI track record is better than RGI’s. One can make that out from average daily enrolments too, running at about 9,40,000 for UIDAI and 65,000 for RGI. There are around 111 UIDAI districts where enrolment is still less than 50% and 206 RGI districts where enrolment is less than 50%. But these are stock numbers. The increments or flows are more important. The story there is a sharp increase in enrolments in UP and a slowing down in Maharashtra, Karnataka, Rajasthan, MP and Gujarat, especially in the more difficult districts. Once people have got Aadhaar numbers, what next? Aadhaar and biometry only ensures there aren’t two individuals with identical names and identical biometry at the same address. It prevents multiplicity. Thereafter, it is a question of what use one makes of Aadhaar. To use technical jargon, Aadhaar must be seeded into assorted programmes. This means: (a) Beneficiary data must be digitised; (b) If beneficiaries don’t possess Aadhaar numbers, they must be enrolled; (c) If they possess Aadhaar numbers, this must be matched with beneficiary data-bases. So far, the primary success has been in seeding bank accounts (old or new) with Aadhaar numbers.

The next step, which is where beneficiaries come in, is direct benefit transfers (DBTs). In August 2014, a study was undertaken by what was then the Planning Commission. In 300 districts, this matched beneficiary data for 5 schemes (post-matric scholarships for SC/ST/minorities, pensions, MGNREGA, PDS, subsidised LPG connections) with UIDAI numbers. That exercise is a bit old, so one needn’t state the precise findings. Suffice it to say, there were major problems with (a). To the extent (a) existed, there were problems with (b) and (c). I am not aware of any robust studies done thereafter, in government or outside it. Anecdotally, that digitisation of beneficiary database seems to be working better for LPG connections than for scholarships, pensions, MGNREGA or PDS. Yet another issue makes it worse. UIDAI is a tool. That tool isn’t going to help identify BPL (below the poverty line) households, those who are beneficiaries of subsidies. This is a socio-economic issue and is important in segments like LPG connections or PDS, where “poor” aren’t self-identified. NSS (National Sample Survey) and poverty estimates based on NSS can’t work. NSS is a survey, not a Census. It can tell us what percentage of the population is below the poverty line in say, Maharashtra, regardless of how the poverty line is defined. It can’t tell us whether a specific “household” is poor or not.

That apart, NSS large-samples generally surface at intervals of five years. However, we have the Socio Economic and Caste Census (SECC) of 2011, driven by the rural development ministry. One of its three objectives is, “To enable households to be ranked, based on their Socio-Economic status. State governments can then prepare a list of families living below the poverty line.” There can be errors of omission (excluding those who are poor) and commission (including those who are not poor). Therefore, suppressing the religion and caste bit, the rest of the information will be put up in (1) the panchayat office; (2) another prominent location in the panchayat; (3) office of the BDO. That allows for errors of omission and commission to be taken care of, through objections. Hence, there is a draft list. After objections are taken care of, there is a final list. Note that, “No changes would be allowed in the data for one year following the publication of the Final List.” That may be operationally necessary. However, in March 2015, final lists are available for only 118 out of 640 districts. Draft lists are available in 541 out of 640 districts. If it takes such a long time to firm up BPL lists, how can the DBT idea work? Does a household’s status remain invariant for 4 years and more? None of Tamil Nadu’s districts figure in either the draft or final lists. But the process was supposed to be completed in June 2013, with initial enumeration over by September 2012. Tamil Nadu isn’t the only state with such a time-lag. There are others too. An inherently good idea (DBT) is still partly stuck in the pipeline of implementation.

The author is Member, Niti Aayog. Views are personal

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