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Identifying the ‘Poor’ and ‘Backward’

Rohit Mutatkar (rohit.mutatkar@gmail.com, rohit@tiss.edu) is associate professor, Centre for Study of Social Exclusion and Inclusive Policies, Tata Institute of Social Sciences, Mumbai.

There have been various aspects involved in identifying the “poor” and “backward” in India for the purpose of targeted public policies. This article provides an overview of these aspects, so as to lead to an integrated perspective.

The author is thankful to the referee for comments and suggestions.

 

In the public policy discourse in India, “poor” and “backward” are widely used terms. These terms have been used largely with respect to three levels, namely household; group or community; and at a spatial or regional level. However, there have been various aspects involved in identifying the “poor” and “backward” in India for the purpose of targeted public policies. These have particularly come into focus since the past decade or so, in the context of the public policy discourse on “inclusion,” and the reports of various committees and commissions in India relating to poverty, social disadvantaged groups and regional disparities. The purpose of this article is to provide an overview of these aspects, so as to lead to an integrated perspective.

Poverty and Backwardness

At a household level, the issue of identifying the poor has had two aspects in India, namely estimation of poverty based on the consumption expenditure surveys of the National Sample Survey Office (NSSO) and the identification of the poor through the below poverty line (BPL) census. Estimation of poverty in India has been an issue of much debate and has focused on the issue of what should be the poverty line, defined in terms of a monthly per capita expenditure level. While the Lakdawala Committee methodology and poverty lines (Government of India [GoI] 1993) formed the basis of the official poverty estimates at an all-India level and across states for the year 2004–05, since then there has been no consensus on what are the official estimates of poverty in India (GoI 2016). Estimation of poverty is required for counting the number of the poor, and the statewise and regional estimates of poverty have also been used in the development planning and resource allocation mechanisms. However, what matters for a needy household is whether it is classified as BPL or above poverty line (APL) by the BPL census carried out by the government. Being classified as BPL makes the household eligible for a range of government social assistance programmes targeted at the poor, while being classified as APL implies that the household would only be eligible for those programmes, which are universal in design.

A BPL census to identify the rural poor was first initiated by the government in 1992, but the recommended methodology for identification has kept changing substantially for every BPL census since then (GoI 2012), which reflects a lack of clarity and consistency regarding the concept of poverty underlying this exercise. The estimates of poverty based on the NSSO data were so far also being used to put a cap on the number of people identified as poor from the BPL census, even though the criteria for identification have been different in both these exercises. This arbitrary practice is now reported to be discontinued (Saxena 2015). The issue of identification of the poor was further carried forward through the Socio-Economic and Caste Census (SECC), which was initiated by the government in 2011. The census was to be used for identification of the poor as well caste-wise population enumeration and knowing the socio-economic level of different castes in the country. However, this data is yet to be used for identification of the poor and the credibility of the data has also been questioned (Saxena 2015). The data for the urban component as well as the caste census component are also yet to be released. Given the highly sensitive nature of caste-wise data and identity politics relating to caste in India, whether a caste census is an appropriate mechanism for devising social policies is a debatable issue.

The socially disadvantaged categories as mandated by the Constitution for the purpose of targeted public policy interventions are the Scheduled Castes (SCs), Scheduled Tribes (STs), and other socially and educationally backward class of citizens. These categories ha ve, however, nowhere been defined in the Constitution, though there have been various criteria followed by the government regarding their identification. The SCs correspond to those castes, which historically suffered from the practice of untouchability. However, only those ex-untouchables who are either Hindu, Sikh or Buddhist by religion are eligible for being regarded as SC and not those who are either Muslim or Christian. There are no such religion restrictions for the STs and Other Backward Classes (OBCs) category. The STs are supposed to be identified on the criteria of indications of primitive traits, distinctive culture, geographical isolation, shyness of contact and backwardness, but there is no clarity on the methodology for implementing these criteria.

The OBC is a large and heterogeneous category, and broadly corresponds to caste groups in the Shudra varna, though there has been much debate regarding their identification. The basis for their identification has been caste, though the nomenclature is “class.” The “creamy layer” which constitutes the basis for exclusion from the OBC category (for the purpose of being eligible for reservation benefits) is based on the economic criterion of annual household income level, which is periodically revised. The present level is at ₹8 lakh per year, suggesting that only the higher income households among the OBCs are sought to be excluded. Thus, though group is the unit of inclusion in the OBC category, household is the unit of exclusion. The basis for the creamy layer economic criterion and its implementation, however, remains unclear, in the context of various conceptual, methodological and administrative issues in gathering data on income in India. The creamy layer does not as yet apply to the SC and ST categories.

The decennial census of India gathers data on the SC and ST population, but not on OBC population. This has also been one of the rationale put forth for the SECC. The SC, ST and OBC are social group categories, and themselves comprise within them many ethnic groups respectively. For example, the SC and ST together constitute about a quarter of India’s population, and comprise within them more than 400 caste and tribal groups respectively. There are known to be socio-economic disparities even among these ethnic groups, though data on ethnic groups within social group categories is not collected by large sample surveys such as National Sample Survey and National Family Health Survey. Among STs, the government has created the subcategory of particularly vulnerable tribal group (PVTG), which is supposed to be identified on the basis of a pre-agricultural stage of living, low literacy rates and a stagnant or declining population. Among SCs, some state governments have created subcategories such as “Mahadalit” in Bihar, though no such subcategory has as yet been adopted at an all-India level. For the OBC category, the National Commission for Backward Classes has recommended the creation of three groups or subcategories, namely extremely backward classes, more backward classes, and backward classes (GoI 2015). This has raised issues of defining the unit of action for public policy, as well as the criteria to identify the more deprived communities within the social group categories.

Social and Spatial Categories

The Sachar Committee report (GoI 2006) brought in religion as a unit of analysis for the first time in the poverty discourse in India, in the context of Muslims. The Sachar Committee report indicated that the level of poverty among Muslims is next only to the SCs and STs. However, its comparison was with the combined category of SC/ST rather than these categories individually. This is in line with a tendency in the public policy discourse to combine SC and ST into one category, which is misleading as the nature and causes of deprivation among the SCs and STs are very different. The Sachar Committee report also mentioned the aspect of caste and socio-economic differentiation among Muslims into Ashraf, Ajlaf and Arzal communities, with the latter two classified as OBC Muslims by the government, and with higher estimates of poverty as compared to the non-OBC Muslims. The committee mentions in this context that the Arzals, who are the ex-untouchable groups within Muslims are at the bottom of the social hierarchy and need special treatment through inclusion in the SC list or inclusion in the “Most Backward Classes” category within OBC. This also raises the issue of understanding which aspects of poverty and backwardness among Muslims as a category are due to their caste identity and which are due to their religious identity.

Besides SCs, STs, OBCs and Muslims, the other social group category which have been the focus of public policy discourse in India are the Denotified and Nomadic tribes (DNT) or communities. The denotified communities comprise the ex-criminal tribes, while the nomadic communities comprise those whose traditional livelihoods involved moving around as nomads, such as pastorals and hunter-gatherers, goods and service nomads, entertainers and religious performers (Bokil 2002). These communities are among the most socially disadvantaged in India, but the DNT are not recognised as a constitutional category, unlike the SCs and STs. While in Maharashtra, there are reservations for these communities through the separate categories of “Vimukta Jati” and “Nomadic Tribe,” in most other states these communities are classified under the OBC category. The National Commission for Denotified, Nomadic and Semi-Nomadic Tribes (NCDNT) notes in its report (GoI 2008) that the issue of identification of the DNT is complex, and particularly the problem of defining the nomadic and semi-nomadic tribes requires greater attention. The NCDNT has also recommended that there is a need to have an authentic estimate of the population of the DNT.

There are known to be anomalies in the lists of SC, ST, OBC and DNT communities across states. In recent years, there have also been demands for reservations or inclusion in particular “backward” categories, by various communities across India. While in some cases such demands have come from the politically dominant communities in their respective states, in other cases there have been demands for inclusion in a particular category on the basis of similar nomenclature. Such demands and claims may only be expected to increase in future years putting further burden on the state and judicial mechanisms to examine these claims and also giving rise to further identity-based mobilisations.

The public policy discourse on backwardness has also focused on the spatial aspect, largely in the context of addressing regional disparities. The spatial aspect, in turn, may be understood at various levels such as state, regions within a state, district, block and particular pockets of deprivation. The policy discourse on this has involved various aspects such as the appropriate level of spatial unit, the criteria and indicators of backwardness, the weighting scheme if these indicators are to be combined into a composite indicator or index, as also a cut-off level or categorisation of identifying some spatial units as backward. The issue of backwardness at the state level was sought to be examined in recent years by the Raghuram Rajan Committee report (GoI 2013), which suggested criteria for ranking states according to their level of development through a composite index of states. One of the issues of debate in the report has been the inclusion of percentage of SC/ST population in the state as a criterion of backwardness.

This has been contested by one of the members of the committee raising the question of whether this is an outcome variable or a process variable already reflected in other indicators. There are also regional disparities within many states, with the indicators and criteria for defining backwardness of a region having been debated in many government policy reports (GoI 2005). In this regard, the percentage of SC and ST population in a region has often been regarded as a proxy for regional backwardness (GoI 2005). However, this may be misleading as while the SC live in mainstream society in proximity to the Hindu caste peasantry, tribal areas are often distinct being located in hilly and forest regions. Thus, while a tribal area may be associated with backwardness of the region, composition of SC population may not have any relation to regional backwardness. This is also applicable in the context of identifying backward districts. For example, it may not be a coincidence that the lowest ranked district in terms of the human development index (HDI) in Maharashtra has been Nandurbar, which is a tribal district. This also raises the issue of whether backwardness of a spatial unit should be understood in terms of its geographical and economic features, or also in terms of composition of its population. In the context of tribal areas, it may also be noted that in states such as Maharashtra, about 50% of the ST population in the state is living outside the designated tribal areas. Thus, while tribal areas may be associated with regional backwardness, there may also be a sizeable tribal population living in non-tribal areas.

While district is an administrative unit for implementation of government programmes, attempts have also been made to have criteria or indicators for identification of backward blocks. In the absence of good quality statistical data at the block level, there are various data constraints to have reliable block-level development indicators, but an attempt was made as part of the preparation of the Maharashtra Human Development Report 2012 to compute a taluka-/block-level development index. The robustness of this index needs to be examined, but its initial findings indicated that among a ranking of the 356 talukas in Maharashtra, according to the index, the bottom 15 are all tribal-majority talukas. In the undivided Thane district, there were five talukas in the top 15-ranked talukas in the state, while the adjoining five talukas in the same district, which were tribal talukas, were among the bottom 15-ranked talukas in the state (Government of Maharashtra [GoM] 2013). The spatial aspect of backwardness has also sought to be analysed at even more disaggregated levels such as ward level in a city. For example, the Mumbai Human Development Report (GoM 2009) prepared a HDI at the ward level, which was used to rank the 24 wards in the city accordingly. The bottom-ranked ward according to this report was M–East ward, which is a predominantly slum area with large pockets of Muslim population. Here again the question arises as to whether the low levels of human development in M–East ward should be viewed only as an urban poverty issue, or also as an issue of poverty and deprivation among Muslims.

Policy Challenges

There are three key aspects of targeted public policies in India where the issue of identification is involved. These are identifying the “poor,” for the purpose of government social assistance programmes, the “backward” communities for the purpose of affirmative action policies and the “backward” regions for addressing regional disparities. The discussion in this article has sought to provide an overview of the complex issues involved in all these aspects.

While there is no clarity within the government itself on the official estimates of poverty, India continues to be among the poorest countries in the world according to various global poverty estimates and also ranked among the bottom countries in terms of various human development indicators and indices. In this context, a periodic multidimensional poverty assessment survey may be considered by the government, which may gather information on the multiple dimensions of poverty and deprivation and be relevant for focusing directly on the individual socio-economic indicators in policy planning and interventions.

Identifying the poor through a BPL census is as much an administrative issue as an issue of an appropriate methodology and indicators. Any indicators to be applied uniformly in this context are bound to involve various subjectivities. A watertight categorisation of BPL/APL across a range of programmes and schemes should therefore be critically evaluated by the government. Self-targeting schemes such as the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) have done well in reaching out to the most deprived sections of society, including the SC and ST, and also addressing both chronic and transient poverty. An urban counterpart of MGNREGS as recommended by the National Commission for Enterprises in the Unorganised Sector may be considered by the government, so as to also benefit the urban poor.

Socially disadvantaged groups in India, such as SCs, STs, OBCs, DNTs and Muslims suffer from a different typology of exclusion and it is necessary to recognise their distinct nature and causes of deprivation and also ethnic group disparities within these categories. The focus of affirmative action in India has been on reservation policies, but the core issue is addressing poverty and deprivation among these groups in their multiple dimensions. In this context, the report of the Justice Chinnappa Reddy Commission (1990) in Karnataka has noted that

the real key towards the solution of the problem of backwardness are the eradication of poverty and removal of illiteracy and not the mere reservation of a few seats in professional colleges and a few posts in government service. (as cited in GoM 1993: 17)

With an increasing demand for reservations from various communities in the country, a focus on poverty issues cutting across communities will also contribute towards shifting the political discourse away from identity politics to developmental politics.

Issues of regional backwardness would need to take into account the backwardness or deprivation of people living in the region. For example, tribal development would require not only an area development approach, but a broader human development approach. A strong decentralised statistical system would also enable identification of pockets of deprivation for more informed interventions. This would need to be accompanied by more political and financial powers to the local bodies in rural, urban and tribal areas. However, categorisation of spatial units into backward/not backward is bound to raise various policy concerns. For example, the NITI Aayog has proposed that

the issue of restricting MGNREGA to around half of the poorest blocks (or equivalent administrative unit) nationwide may be considered. An expert committee could be appointed to develop the exclusion, inclusion and deprivation criteria along the lines suggested in the SECC booklet to select the beneficiary blocks. (Goi 2016: 29)

This proposed policy is bound to be arbitrary in identifying the “poorest” blocks, besides drastically weakening the all-India coverage of MGNREGS, which is a self-targeting and demand-driven scheme.

The nomenclature of backward, whether for communities or regions should be revised in the public policy discourse in India and may be substituted with terms such as deprived or disadvantaged. The overlapping disadvantages relating to identifying poverty and backwardness in India need to be recognised. Thus, a disabled girl child/widow from a poor household, belonging to one of the lowest castes, and living in one of the most backward regions of the country, would be considered as an example of a most deprived individual and most in need of social assistance for her survival. Merely classifying a household as “poor” and a group, community or region as “backward” would not be enough and the focus should be on interventions so as to enable the most deprived people in India to come out of poverty and deprivation, in its multiple dimensions. What the poor require are relief interventions to address their immediate survival concerns and sustainable development interventions that will help them to come out of poverty and lead to a reduced dependence on relief interventions. With some of the worst indicators of human development in the world, it would be important for policymakers in India to formulate and implement more universal and self-targeting programmes, which would also avoid the problems of identification inherent in any programmes based on a targeting approach.

References

Bokil, Milind (2002): “Denotified and Nomadic Tribes: A Perspective,” Economic & Political Weekly, Vol 37, No 2, pp 148–54.

Government of India (1980): Report of the Backward Classes Commission.

— (1993): Report of the Expert Group on Estimation of Proportion and Number of Poor, Planning Commission.

— (2005): Report of the Inter-Ministry Task Group on Redressing Growing Regional Imbalances, Planning Commission.

— (2006): Social, Economic and Educational Status of the Muslim Community of India, Cabinet Secretariat.

— (2008): Report of the National Commission for Denotified, Nomadic and Semi-Nomadic Tribes, Ministry of Social Justice and Empowerment.

— (2012): Report of the Expert Group to Recommend the Detailed Methodology for Identification of Families Living Below Poverty Line in the Urban Areas, Planning Commission.

— (2013): Report of the Committee for Evolving a Composite Development Index of States, Ministry of Finance.

— (2015): Report Relating to the Proposal for the Sub-Categorisation within the Other Backward Classes, National Commission for Backward Classes.

— (2016): “Eliminating Poverty: Creating Jobs and Strengthening Social Programmes,” Occasional Paper No 2, NITI Aayog.

Government of Maharashtra (1993): Report of the Expert Committee on Inclusion of Communities in the List of Scheduled Castes, Vimukta Jatis, Nomadic Tribes and Other Backward Classes in the State of Maharashtra.

— (2009): Mumbai Human Development Report, Oxford University Press.

— (2013): Report of the High Level Committee on Balanced Regional Development Issues in Maharashtra, Planning Department.

Saxena, N C (2015): “Socio-economic Caste Census,” Economic & Political Weekly, Vol 50, No 30, pp 14–17.

Updated On : 9th Oct, 2018

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