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Grim picture: What the SECC data reveals about India’s poor


Grim picture: What the SECC data reveals about IndiaSAUMALYA GHOSH/GETTY IMAGES


Grim picture: What the SECC data reveals about India’s poor

Shock revelations

  • SECC data paints a despondent picture of rural areas.
  • Less than 10% rural households have a salaried member.
  • In 75% households, the highest earner brings less than Rs 5,000 a month.

Plus points

  • Most objective census of the poor so far.
  • It collected exhaustive details, even on the homeless.
  • Data will allow the state to better target welfare schemes.

Key concerns

  • Data won’t be of much use if verification is delayed.
  • Proposal for linking SECC to Aadhar raises privacy issues.
  • Part of the SECC data on castes is still under wraps.

Amid much ceremony, the government earlier this week released some of the data from the Socio Economic Caste Census. The part of the data on castes is still under wraps for obvious reasons.

It’s quite revealing and adds weight to what many people have been saying for a while now: that rural India is not really ‘shining’ and the benefits of high economic growth have bypassed a large section of the population.

Nearly half of all rural households are deprived by one or more indicators. Few households have a regular job, an insignificant number are tax-paying and more than a quarter don’t have a single literate adult member.

Only 7.3% of SC households, and 9.7% of all rural households, have at least one member with a salaried job. In 75% of all rural households and 84% of SC households, the highest earning member brings in less than Rs 5,000 a month. And just 27% SC households satisfy even one of the criterion for exclusion from entitlements.

This is a wake-up call that rural India can’t be neglected any longer.

Casting a wide net

The SECC is an improvement over previous attempts to measure poverty. It collects data on several indicators – assets owned, nature of employment, education, social profile, etc. – so that the government, when targeting entitlements, can rank households on different parameters rather than impose to a one-size-fits-all cut-off.

Since the SECC has documented beneficiaries of the National Food Security Act, Indira Awas Yojana, and old age, disability and widow pensions, states can identify precisely which families are most eligible for a host of government schemes.

In 84% of SC households, the highest earning member brings in less than Rs 5,000 a month

The census has even recorded the homeless and people living in kaccha mud houses, not just as a figure but with details of where they live and what they do. The SECC is a mine of information on every household in rural India.

Several attempts have been made to accurately identify the poor, particularly for the purpose of targeting welfare programmes, but most of them focused on income. Since most Indians work in the informal sector, income is not a reliable and objectively verifiable criterion. There is, therefore, a need for proxy indicators that can be easily collected and have minimum inclusion and exclusion errors. The SECC is a positive step in that direction.

Functionally too, the credibility of the SECC data is bolstered by the fact that it’s finalised after a round of verification at the household level. As of now, the verification exercise is complete in only 281 of the country’s 640 districts, covering less than 40% of the population, though the initial data was collected three years back.

Denied their due

Before the National Food Security Act, 2013 was passed, the number of below-poverty-line households in each state was fixed on the basis of the poverty ratio declared by the Planning Commission. The poor households were then identified through a BPL census that drew on a 2002 criteria of 13 indicators.

This method was widely criticised as it left a large number of the poor out of BPL lists. In fact, every other poor person was mistakenly excluded and a considerable number of rich wrongly included.

Such errors in the BPL census resulted from two reasons. First, the cap on BPL households was set externally and second, the criteria were faulty and not objectively verifiable. For instance, people were asked how many pairs of clothes they owned or how often they went hungry to decide if they were poor.

Some criteria also provided perverse incentives; sending children to school or having access to a latrine could keep families off the BPL list. Such distortion of data in the field, coupled with an inadequate procedure to raise claims and objections filled the lists with errors.

Mending the method

Now, the government, on the recommendation of the NC Saxena Committee, has proposed a new method to identify the poor.

In this method, households are classified as ‘automatically excluded’, ‘automatically included’ or ranked on their deprivation levels. Although the government has listed 14 exclusion criteria that could automatically exclude households from entitlements, it does not mean all would be necessarily used at once. Still, it’s an indicative list that state governments can use at their discretion.

However, the claim that this method will exclude 40% of India’s deprived from entitlements is exaggerated. Under the National Food Security Act, for instance, 75% of rural households are to be covered. This means at most only a fourth of all households can be excluded.

That said, there needs to be a wider discussion on the exclusion criteria. Rather than having a uniform land ownership cut-off across India, the state governments should take, as the Saxena committee has suggested, the district average as an indicator.

A hectare of land in the Gangetic Plains of western Uttar Pradesh is much more valuable than 20 hectares of fallow desert land in Rajasthan. How can the economic health of two families from the two states be compared on the ownership of a government-fixed amount of land?

The indicators in the ‘automatic exclusion’ too call for some discussion. Owning a motorised two-wheeler, for example, is an inaccurate measure of ‘richness’ considering it could have been given as dowry or bought on credit.

The part of the SECC that has been released is provisional as the verification exercise is yet to be completed. Once it is, it will be up to the states to use the laid out criteria to target schemes. Some of them, such as Chhattisgarh and Madhya Pradesh, may not use the SECC data as they have already completed targeting and identification for the Public Distribution System under the Food Security Act.

Bihar, on the other, had for the SECC data on the eastern state early, and put it to immediate use. Large population states such as Uttar Pradesh, Jharkhand, Bengal and Orissa have much scope to fine-tune their targeting of schemes using the SECC.

The pilot BPL census done before the SECC had shown that while it’s relatively easy to identify the well-off rural households that can be automatically excluded as well as the poorest that can be automatically included, the problem arises in ranking those in the middle, which is about half of all rural households.

The best way to ensure that none of the deserving are left out is to include all household that do not fall in the ‘automatically excluded’ category. This, in fact, is what Bihar is doing for identification of priority households under Food Security Act. Any further attempt at targeting is doomed to fail.

Some fears, much hope

At the launch of the SECC, it was proposed that the data should be updated in real time, not once a decade during which time people die, get married, fall into poverty, rise out of it. How can this be done? By linking the SECC data to Aadhar, the proposal went.

This way, the Aadhar number could be used to monitor purchases. So, if yours is a targeted poor household and you buy a car, you could be immediately excluded from state entitlements.

This proposal is dangerous, not least because it treads upon a host of privacy issues. How closely will we be watched? And how much of this recorded information will be available to the world?

Yes, targeting of public services is multidimensional, but services such as nutrition, healthcare and education should be fundamental rights that no change in personal lifestyle can determine the exclusion of.

Instead of generating SECC data for urban areas, the government wants to use it to further its reform agenda

If the Modi government wants to make the SECC data more relevant, it could work on generating this wealth of information for urban areas as well, where the problem of targeting is far more severe and complex.

Instead, the government seems to want to use the data as a launch pad for its reform agenda. “Improving this situation is the number one priority of this government,” Finance Minister Arun Jaitley wrote in a recent Facebook post titled ‘Message of the Socio-Economic and Caste Census’. “Passing the GST and reforming the land law will accelerate this investment turnaround,” he added, stressing the importance of creating conditions for greater private investment. How exactly is this linked to SECC?

It’s important to note that any more delay in the verification and finalisation of SECC data will render it of little use as the states are already identifying priority households under the Food Security Act, undoubtedly the largest social welfare programme this data can be used for. A late release could weaken the SECC’s impact and defeat the very purpose it was envisioned for.


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