A number of controversies are raging concurrently on size and nature of poverty and the ongoing unprecedented (food) inflation is adding fuel to this fury.
The official poverty estimate has gone through change due to change in methodology and cut offs for the second time in last 12 years or so. Be aware, therefore, it not now easy to compare poverty estimates over time and therefore all are in dark as to how many of we Indians are poor and who are they? Should one continue with the original definitions of poverty and methodology set in mid-1970s, there would not be many poor left in India! Therefore, the recent revision of the poverty line aligned with the urban levels of consumption is the saving grace; and rural poverty is estimated to be about 37.2% for the year 2004-5. The official poverty line is the rupee value which can purchase the minimum benchmarked amount of calories needed for sustenance which is in fact higher for rural and lower for urban areas. However, the anomaly is that it takes relatively more amount of rupees to buy the same amount of calories in urban area possibly due to price difference. It is not clear if any adjustments for these differences are made in the new poverty line suggested by Tendulkar Committee. Yet when the food intake (which is the primary basis for fixing the poverty line) and associated calories are measured using the same data source (NSSO) up to 70% of the population is estimated to be vulnerable and in a number states such as Orissa, Bihar, Jharkhand and so on this proportion can be as high as 90%. Thus it is not only difficult but impossible to understand the objective criteria used to benchmark the poverty estimates without an empirical multi-vulnerability analysis.
A as a matter of routine the state governments in association with the national ministry of rural development undertake ‘below poverty line’ (BPL) surveys at about 5 years of interval to identify deserving households for service delivery. Earlier these surveys used a list of 13 characteristics which highlight vulnerability and arrived at estimates comparable to those based on calorie intake deficiencies.
Currently, efforts are on through a committee to refine these variables using the ‘exclusionary’ and ‘inclusionary’ criteria. This refinement seems necessary firstly to remove the deficiency of the13 variable surveys and secondly to arrive at a compromise level of poverty estimates between the official national and state estimates. While a refinement in this method is essential what is debatable is the subjective elements inherent in identifying a set of variables for exclusion and another set for inclusion. Further score values are assigned to the variables of inclusion with little empirical basis however. Unlike in the past now the local level functionaries have to grapple with not one but two lists, one of exclusion and another of inclusion and then assigned scores, all at the local level. Further, during direct questioning for the sake of BPL identification will be highly politicised, contentious and problematic. For example, it will not be easy to find out who are the income tax payers are as this information not public knowledge; similarly identifying ‘person owning a fire arm’ will be as difficult as facing one who owns it. It is argued that many of the variables listed including the two mentioned above are not expected to be statistically significant and therefore empirically irrelevant relevant should the identification of the variables is undertaken using time tested and transparent methods. The subjective variable scoring scheme will be politically not acceptable either.
There is another factors relevant in this methodology is capping of poverty level, arbitrarily at 50% level. As is well known a cap at the policy level is always taken forward upto the block, panchayat and even village levels and thus whole communities will be at risk of being eliminated from being identified as the poor; not because of any defect but just by a sheer application of quota and the overriding execution of local community level politics which can easily exclude the marginal groups such as the SCs, STs, Muslims and other minorities. The proposed methods also ignore the fact that the variation in poverty extends upto the village and even sub-village levels due to unique residential pattern in which the marginalized are forced to live in peripheral locations around the village. Often such localities are entirely excluded from the ambit of program implementation on various pretexts by the local functionaries and supportive political system.
Is it possible to put forth an empirical methods which can identify dispassionately using location (geographic identity) specific factors that can be used a check list. A larger survey data can be used to identify a list of factors and this list can be used as a check list during the village level surveys. This method removes subjectivity at another level; at the level of identifying the target household. Since the check list is predetermined at say each district or taluka level, the identification errors will be minimal.
A multi-vulnerability identification exercise for rural India provides the following list of variables which exhibit source of opulence or vulnerability accordingly. These variables are empirically identified as the dominant factors with various degree of vulnerability. Using this as a check list one can with a high degree of certainty and in a dispassionate manner estimate a value to each household, and also arrange in an order of priority. Thus all such households within in a defined area can be stacked one over another depending upon the total severity of vulnerability and thus easy to identify who of the two households deserve social services, a very important attribute if limited resources are to be distributed. This kind of assigning of intensity of vulnerability is impossible in the method currently being considered. However, the alternate empirical approach in fact helps in placing a given household at an appropriate location in the ‘economic ladder’ and helps clearly identify who are the at the bottom, middle and at the top end of the economic wellbeing. Thus it eliminates an important defect in present system of categorising households only in two groups namely the poor and the rich.
The functionaries, therefore, can prepare location specific common list for practically all such programs which are intend to help the deprived and poor households, and programs can be aligned with commonly accepted benchmarks in terms of the quantum of services either in cash or in kind. In fact one can also conceive a gradation of such services depending upon the severity of the household as expressed in the value of vulnerability estimated in this method. Given a baseline survey, such as the NSSO, the NFHS and Human Development Surveys, it is easy to give the list of these variables upto the district and even lower administrative levels.
Exclusionary Attributes:
HH who receive income from regular salaried and organised sector employment;
Residential Living Density – more than 0.5 room per person (excluding kitchen)
HH Owning any of these items : – tube well, electric/diesel pump / tractor / biogas plant
HH owing a diesel / petrol driven vehicles such as car, motor cycle, scooter
HH who owns a Refrigerator or Colour Television
Inclusionary Attributes:
Household Head not educated upto 8th standard
SCs, STs and BC-Muslim households
HHs not using LPG, Kerosene, Cooking coal for cooking
Households having MG-NREGA job cards
HH with a sick person as indicated in a pre-approved list
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