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
Showing posts with label India Poverty Debate. Show all posts
Showing posts with label India Poverty Debate. Show all posts
Friday, January 21, 2011
Friday, January 15, 2010
Poverty Reduction and Poverty Creation in India
Do they happen concurrently and for what reason?
Summary of a recent paper entitled 'The Irrelevance of National Strategies:
Rural Poverty Creation and Reduction in States of India' jointly written with
Anirudh Krishna of the Duke University, USA
There are only a few controversial and somewhat puzzling economic dimensions which are academically unresolved and subject to serious debates. While there is recognition about the speed and direction of growth scenario suggesting India’s arrival on the global economic scene, what is puzzling at the same time is not only the continued prevalence of poverty but also a possible increase over the past decade, at least in the rural hinterland. Poverty measurement is an unsettled issue, both conceptually and methodologically. In fact, since poverty is a process as well as an outcome; many come out of it while others may be falling into it. The net effect of these two parallel processes is a proportion commonly identified as the ’head count ratio’, but these ratios hide the fundamental dynamism that characterizes poverty in practice. Note that the most recent poverty re-estimates using a supposedly refined methodology by a Government of India expert group has also missed this crucial dynamism (Report of the Expert Group to Review the Methodology for Estimation of Poverty, 2009. New Delhi: Government of India, Planning Commission). Studies carried out by one of the authors of this article, in parts of Andhra Pradesh, Gujarat, and Rajasthan have, however, helped bring to light the essential fact of poverty dynamics: which is simultaneously both created and reduced. Some households fall into poverty, becoming the new poor; other households concurrently escape from poverty.
Following up on these insights, we examined, for the first time in India, a nationally-representative panel data set for more than 13,000 households studied in 1993-94 and re-interviewed in 2004-05. We found that while 18.2 percent of the rural population moved out of poverty, another 22.1 per cent fell into it over this twelve-year period. This net increase of four percentage points was found to have a considerable variation across states and regions. In states, such as Himachal Pradesh, Kerala, Rajasthan and West Bengal, where more people moved out of poverty than fell into poverty, there has been an overall decline in rural poverty. Conversely, rural poverty increased in Andhra Pradesh, Bihar, Gujarat, Haryana, Maharashtra, Madhya Pradesh, Orissa, Tamil Nadu, and Uttar Pradesh, where descents into poverty were more numerous than escapes from poverty.
States – as well as regions within states – differ from one another. Some have high descent rates but low escape rates; in a few other states, both descent and escape rates are low. Different combinations of poverty policies will be required in states that are characterized by different patterns of escaping poverty and falling into poverty. A typology emerging from our analysis is presented below:
Consider the upper-left cell which lists regions that have most successfully reduced poverty over the 12-year reference period, because a high escape rate went together with a low descent rate. Two small states, Kerala and Himachal Pradesh, and another group of states (Assam and the Northeast) fall within this high-performing group. A poor person in India is best-off living within some region of this cell: the probability is highest that her circumstances will improve over time. For a contrasting situation, consider the bottom-right cell, characterized by low escape rates and high descent rates. Compared to other regions in India, the prospect for poor people in these regions is bleak; chances for further impoverishment are the highest. Future efforts in this region will do well to concentrate, first, on lowering the high rate of descent, and second, on ramping up the low escape rate.
A more nuanced prognosis emerges for regions included within the off-diagonal cells. Consider, for example, the two regions belonging to the bottom-left cell (Karnataka- Inland Southern, and Madhya Pradesh-Vindhya). A high escape rate within these regions has been compromised by a concurrently high descent rate. Future poverty reduction efforts in these regions should focus primarily on reducing the high rate of descent into poverty. It makes greater sense to raise the escape rate only after the high risk of falling into poverty has been brought under control. The opposite policy prescription seems appropriate for regions of the top-right cell. In Maharashtra-Eastern and Haryana-Western, additional resources should be deployed primarily for raising poor people’s chances of escape.
Thus, different policy mixes, combining different elements of prevention (against descents into poverty) and support (for escaping poverty), are required in diverse regions and states. A uniform national poverty policy will not be effective for entire states, far less for the entire country.
A Typology of Poverty of States and Regions in India
Escape Rate
High (43.0-21.3) Medium (21.2-15.9) Low (15.8-4.4)
Descent Rate Low (18.7-9.1) Assam & Northeast AP-Coastal Maharashtra -Eastern
Karnataka - Coastal, Ghats, & Inland Eastern Punjab-Northern Haryana-Western
Kerala AP-Inland Southern
WB-Himalayan
WB-Central Plains
Himachal Pradesh
Rajasthan-Western
Rajasthan-North-Eastern
Bihar-Jharkhand
West Bengal-Eastern Plains
Medium (24.7-18.8) Gujarat-Plains Northern UP-Western MP-South Western
TN-Coastal & Coastal Northern TN-Southern Orissa-Coastal & Southern
Rajasthan-Southern & South-Eastern UP-Uttaranchal Gujarat-Saurashtra & Dry areas
Bihar-Central Maharashtra-Inland Western & Coastal
MP-Chhattisgarh
Punjab-Southern
MP-South
Gujarat-Eastern & Plains Southern
Haryana-Eastern
High (47.4-24.8) Karnataka -Inland Southern UP-Eastern & Central Orissa-Northern
MP-Vindhya Maharashtra-Inland Eastern Maharashtra-Inland Central
Bihar-Northern TN-Inland
Karnataka-Inland Northern AP-Inland Northern
MP-Malwa
Maharashtra-Inland Northern
MP-Northern
AP-South Western
MP-Central
What kinds of policies and programs will help prevent descents into poverty? And what kinds of programs and policies are required to promote escapes from poverty? We examined the escapes and the descents that have occurred in rural India between 1993 and 2005, and we identified the factors that mattered for each of these trends. A few factors are involved with both escapes and descents, but there is also another group of factors that affect only escape or only descent.
Factors significantly associated both with escape and descent : Age of household head, household size, household composition (reflected by the variable “male advantage”), households with telephones (land lines or mobile), change in the share of rural non-farm income (RNFY), remittances, women’s media exposure, and loan taken in last five years.
Factors associated with escape but not with descent: A few other factors influence escapes but do not matter much for descents. These factors include being a member of ‘minority other than Muslim’, presence of adult son during the previous period (1993), and that the household was located within 5 km of nearest town with the availability of bus stop. Sickness within households had an adverse effect.
Factors associated with descent but not with escape: A household belonging to SC, ST or OBC faced a significantly greater risk of descent into poverty, whereas having the head of the household educated to secondary level or higher reduced the risk of descent. Possession of land and other assets reduced the risk of descent. Significantly, such rural assets were not germane to escapes from poverty.
States with high and low growth rates have variously experienced high and low rates of escape and descent. No clear correlation exists at the level of states and regions between high growth rates and higher poverty reduction. Thus, to claim that “growth of aggregate consumption/income is a sufficient condition for poverty reduction,” as one influential government document does, is hardly an appropriate or adequate policy prescription. Rather than waiting for growth to occur and work its putative magic, direct actions to reduce poverty are necessary.
Action along two fronts is simultaneously required: descents into poverty must be prevented using context-specific measures even as escapes from poverty are promoted vigorously. Different escape and descent rates characterize diverse states and separate regions within states. The reasons that matter for escape and descent also differ considerably across and within states. Considering only the aggregate results obscures these important differences. Diverse policy designs are required in order to deal with these different trends. Any uniform national policy does not, therefore, represent the best use of resources. State- and region-specific threats and opportunities must be separately identified and directly addressed.
Summary of a recent paper entitled 'The Irrelevance of National Strategies:
Rural Poverty Creation and Reduction in States of India' jointly written with
Anirudh Krishna of the Duke University, USA
There are only a few controversial and somewhat puzzling economic dimensions which are academically unresolved and subject to serious debates. While there is recognition about the speed and direction of growth scenario suggesting India’s arrival on the global economic scene, what is puzzling at the same time is not only the continued prevalence of poverty but also a possible increase over the past decade, at least in the rural hinterland. Poverty measurement is an unsettled issue, both conceptually and methodologically. In fact, since poverty is a process as well as an outcome; many come out of it while others may be falling into it. The net effect of these two parallel processes is a proportion commonly identified as the ’head count ratio’, but these ratios hide the fundamental dynamism that characterizes poverty in practice. Note that the most recent poverty re-estimates using a supposedly refined methodology by a Government of India expert group has also missed this crucial dynamism (Report of the Expert Group to Review the Methodology for Estimation of Poverty, 2009. New Delhi: Government of India, Planning Commission). Studies carried out by one of the authors of this article, in parts of Andhra Pradesh, Gujarat, and Rajasthan have, however, helped bring to light the essential fact of poverty dynamics: which is simultaneously both created and reduced. Some households fall into poverty, becoming the new poor; other households concurrently escape from poverty.
Following up on these insights, we examined, for the first time in India, a nationally-representative panel data set for more than 13,000 households studied in 1993-94 and re-interviewed in 2004-05. We found that while 18.2 percent of the rural population moved out of poverty, another 22.1 per cent fell into it over this twelve-year period. This net increase of four percentage points was found to have a considerable variation across states and regions. In states, such as Himachal Pradesh, Kerala, Rajasthan and West Bengal, where more people moved out of poverty than fell into poverty, there has been an overall decline in rural poverty. Conversely, rural poverty increased in Andhra Pradesh, Bihar, Gujarat, Haryana, Maharashtra, Madhya Pradesh, Orissa, Tamil Nadu, and Uttar Pradesh, where descents into poverty were more numerous than escapes from poverty.
States – as well as regions within states – differ from one another. Some have high descent rates but low escape rates; in a few other states, both descent and escape rates are low. Different combinations of poverty policies will be required in states that are characterized by different patterns of escaping poverty and falling into poverty. A typology emerging from our analysis is presented below:
Consider the upper-left cell which lists regions that have most successfully reduced poverty over the 12-year reference period, because a high escape rate went together with a low descent rate. Two small states, Kerala and Himachal Pradesh, and another group of states (Assam and the Northeast) fall within this high-performing group. A poor person in India is best-off living within some region of this cell: the probability is highest that her circumstances will improve over time. For a contrasting situation, consider the bottom-right cell, characterized by low escape rates and high descent rates. Compared to other regions in India, the prospect for poor people in these regions is bleak; chances for further impoverishment are the highest. Future efforts in this region will do well to concentrate, first, on lowering the high rate of descent, and second, on ramping up the low escape rate.
A more nuanced prognosis emerges for regions included within the off-diagonal cells. Consider, for example, the two regions belonging to the bottom-left cell (Karnataka- Inland Southern, and Madhya Pradesh-Vindhya). A high escape rate within these regions has been compromised by a concurrently high descent rate. Future poverty reduction efforts in these regions should focus primarily on reducing the high rate of descent into poverty. It makes greater sense to raise the escape rate only after the high risk of falling into poverty has been brought under control. The opposite policy prescription seems appropriate for regions of the top-right cell. In Maharashtra-Eastern and Haryana-Western, additional resources should be deployed primarily for raising poor people’s chances of escape.
Thus, different policy mixes, combining different elements of prevention (against descents into poverty) and support (for escaping poverty), are required in diverse regions and states. A uniform national poverty policy will not be effective for entire states, far less for the entire country.
A Typology of Poverty of States and Regions in India
Escape Rate
High (43.0-21.3) Medium (21.2-15.9) Low (15.8-4.4)
Descent Rate Low (18.7-9.1) Assam & Northeast AP-Coastal Maharashtra -Eastern
Karnataka - Coastal, Ghats, & Inland Eastern Punjab-Northern Haryana-Western
Kerala AP-Inland Southern
WB-Himalayan
WB-Central Plains
Himachal Pradesh
Rajasthan-Western
Rajasthan-North-Eastern
Bihar-Jharkhand
West Bengal-Eastern Plains
Medium (24.7-18.8) Gujarat-Plains Northern UP-Western MP-South Western
TN-Coastal & Coastal Northern TN-Southern Orissa-Coastal & Southern
Rajasthan-Southern & South-Eastern UP-Uttaranchal Gujarat-Saurashtra & Dry areas
Bihar-Central Maharashtra-Inland Western & Coastal
MP-Chhattisgarh
Punjab-Southern
MP-South
Gujarat-Eastern & Plains Southern
Haryana-Eastern
High (47.4-24.8) Karnataka -Inland Southern UP-Eastern & Central Orissa-Northern
MP-Vindhya Maharashtra-Inland Eastern Maharashtra-Inland Central
Bihar-Northern TN-Inland
Karnataka-Inland Northern AP-Inland Northern
MP-Malwa
Maharashtra-Inland Northern
MP-Northern
AP-South Western
MP-Central
What kinds of policies and programs will help prevent descents into poverty? And what kinds of programs and policies are required to promote escapes from poverty? We examined the escapes and the descents that have occurred in rural India between 1993 and 2005, and we identified the factors that mattered for each of these trends. A few factors are involved with both escapes and descents, but there is also another group of factors that affect only escape or only descent.
Factors significantly associated both with escape and descent : Age of household head, household size, household composition (reflected by the variable “male advantage”), households with telephones (land lines or mobile), change in the share of rural non-farm income (RNFY), remittances, women’s media exposure, and loan taken in last five years.
Factors associated with escape but not with descent: A few other factors influence escapes but do not matter much for descents. These factors include being a member of ‘minority other than Muslim’, presence of adult son during the previous period (1993), and that the household was located within 5 km of nearest town with the availability of bus stop. Sickness within households had an adverse effect.
Factors associated with descent but not with escape: A household belonging to SC, ST or OBC faced a significantly greater risk of descent into poverty, whereas having the head of the household educated to secondary level or higher reduced the risk of descent. Possession of land and other assets reduced the risk of descent. Significantly, such rural assets were not germane to escapes from poverty.
States with high and low growth rates have variously experienced high and low rates of escape and descent. No clear correlation exists at the level of states and regions between high growth rates and higher poverty reduction. Thus, to claim that “growth of aggregate consumption/income is a sufficient condition for poverty reduction,” as one influential government document does, is hardly an appropriate or adequate policy prescription. Rather than waiting for growth to occur and work its putative magic, direct actions to reduce poverty are necessary.
Action along two fronts is simultaneously required: descents into poverty must be prevented using context-specific measures even as escapes from poverty are promoted vigorously. Different escape and descent rates characterize diverse states and separate regions within states. The reasons that matter for escape and descent also differ considerably across and within states. Considering only the aggregate results obscures these important differences. Diverse policy designs are required in order to deal with these different trends. Any uniform national policy does not, therefore, represent the best use of resources. State- and region-specific threats and opportunities must be separately identified and directly addressed.
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