Answer:
Anti-poverty programmes in the past have been based on National Sample Surveys, covering 1,50,000 households nationally. However, reliability and utility of even such a large survey declines as one moves down administrative levels. The programmes and targets have been based on national averages, which have less relevance in the local context. Further, these have been based on household’s consumption expenditure and sources of livelihood, and do not consider asset ownership pattern, which is also an important determinant of design of poverty alleviation program.
The SECC data, on the other hand addresses multi-dimensionality of poverty by identifying indicators of deprivation and consequent inclusion or exclusion of households from poverty alleviation programmes. It divides the total rural households (17.91 Cr) into three categories:
♤ Automatically included: Based on fulfilling any of the 5 of the criteria viz. Primitive tribal Groups, Released bonded labour, those living on alms, manual scavengers or households without shelter.
♤ Automatically excluded: Based on fulfilling any of the 14 parameters of exclusion such as motorised vehicle/fishing boat, mechanised agricultural equipment, KCC limit more than Rs. 50,000, etc.
♤ Households based on 7 markers of deprivation:
o Households with Kutchha house
o No adult member in working age
o Household headed by female and no working age male member
o Household with handicapped members and no able bodied adult
o Household with no literate over 25 years
o Landless households engaged in manual labour
o SC/ST households.
Households of third category show poverty on some markers, even though the depth of poverty may not be enough to categorise them as absolute poor. Rather than using income as a sole determinant, SECC data addresses this multidimensionality of poverty. It provides information to assess dimensions of poverty in a household – income, literacy, disease, social or gender inequality, indebtness, exploitation and landlessness.
The data can be utilised for a convergent, evidence based planning with Gram Panchayat as a unit. It provides for criteria based selection, prioritisation and targeting of beneficiaries in different programmes. For example, 2.34 Crore households with one room or less and kutchha house should be the first claimants of any rural housing scheme when targeted properly. Instead of extending monetary help to a homeless family, the government should be providing them with a house under one of its schemes and use the funds for sustaining livelihood through skill development, MGNREGS, etc.
The data shows inter-state and intra-state variations in states that were presumed similar – Bihar has much higher landlessness (51%) than UP (31%); Tamil Nadu (56%) is more than Karnataka (22%). It will help governments at all levels to delve into variations across regions, identify the causes of deprivation and design differentiated approaches to tackle poverty.
Used effectively, the SECC data can be leveraged to combine economies of scale with benefits of precision targeting. With use of technology, progress can be monitored on real time basis and targets and targeted groups updated regularly.