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January 2010
How accurate is ‘caste’ as an indicator for measuring economic backwardness? Rural poverty mapping experiences from Sikkim, India Sandeep Tambea*, S. Anbalagana, M.L.Arrawatiaß and Sonam Dhondup? a Department of Rural Management and Development, Government of Sikkim, Gram Vikas Bhawan, Near Tashiling Secretariat, Gangtok – 737101, Sikkim, India ß Department of Science and Technology, Government of Sikkim ? Directorate of Economics, Statistics, Monitoring and Evaluation, Government of Sikkim, * Corresponding author Telephone: +913592-203852 (o), Cell: +919474059791 Fax: +913592-203852 Email: sandeep_tambe@yahoo.com Abstract In India, the “concentration of scheduled caste and scheduled tribe population”, is a commonly used surrogate indicator for measuring economic backwardness. We undertook a poverty mapping study in the state of Sikkim using scientific economic criteria, to identify the poor households. This extensive household census covering all the 905 villages and 93,463 rural households, identified 19,235 poor households. The village poverty rate showed a large variation from 0 to 72% (20.58+ 13.85%). No significant correlation (r = – 0.05%, n = 905) could be found between the village poverty rate and the percentage of scheduled population. Hence we propose using a scientific economic criteria rather than a caste based criteria to measure poverty. 1 Introduction The concept of development has been evolving to encompass several definitions ranging from freedom (Sen 1999) to gross national happiness (Ura and Galay 2004) to a conventional welfare state and millennium development goals (UNDP 2003). Most of these definitions have the same underlying basis of promoting equitable growth and reducing poverty. Poverty is defined variously as hunger, lack of opportunity, lack of education, health, and productive assets and susceptibility to risks and vulnerability. Every welfare state has this basic goal of reducing poverty and attaining development. Development which is inclusive and equitable is been increasing stressed nowadays (World Bank 2005). World Bank (2000) views poverty to pertain to the lack of four attributes: opportunity, empowerment, security and capabilities. Vulnerability defined as the lack of people’s capacity to withstand shock (DFID 2000) is considered a basic feature of poverty. Poverty maps depict the spatial variation of indicators of human well-being across geographically disaggregated units (Henninger 1998; Henninger and Mathilde 2002; Davis 2003; World Bank 2004; Amarasinghe et al. 2005). Poverty maps indicate poverty correlates such as remoteness, droughts etc. However, poverty maps do not identify causes of poverty, therefore they can help policy prioritization but not actual policy design (Vishwanath 2006). In the face of rising public deficits and shrinking public resources, geographical targeting may be a viable way to allocate resources for poverty alleviation in developing countries. Efficiency can be increased and leakage to the nonpoor reduced substantially by targeting increasingly smaller areas (World Bank 2002; Bigman and Fofack 2000). In order to achieve the millennium development goals, countries need to attack pockets of deep poverty by mitigating domestic structural inequalities, targeting policy interventions at groups most vulnerable or marginalized, whether due to gender, ethnicity, religion or geography (Lama 2001; Huddleston et al. 2003; UNDP 2003; Chaudhuri and Ravaillon 2007). Sikkim is a small north-eastern Indian state that lies between 27o 04’ 46” to 28o 07’ 48” N latitude and 88o 00’ 58” and 88o 55’ 25” E longitude. It is strategically located and shares international borders with the countries of Bhutan, China and Nepal. The elevation ranges from 300 to 8586 m, with the most prominent feature being Mt. Khangchendzonga (8586 m), the third highest peak in the world and the highest in the country. The total geographical area of the state is only 7096 km2, and it is the highest and steepest terrain in the country. It was ruled by a monarchy, before its merger with India in the year 1975. As per Census of India (2001) the total population of Sikkim stood at 0.54 million which accounts for barely 0.05 per cent of the total population of the country. Culturally it is a multi-ethnic state, with more than ten different ethnic groups, with Hinduism and Buddhism being the dominant faiths. It is a fast developing state with a double digit Gross Domestic Product (GDP) growth (Lama 2007). Development in the state is characterized by high development indices, high social sector spending, along with high incidence of poverty (Papola 2005; IIPS and Macro International 2008). Inaccessibility, marginality, fragility in physical terms lead to a limited base for sustaining livelihoods, but more importantly, result in a higher degree of vulnerability, risks and uncertainty in realizing the outcomes of livelihood activities. Though per capita resource is high, productive resource or asset base is very limited. Mountain states are also predominantly land-locked reducing opportunities of trade and commerce (Papola 2002). Article 341 and 342 of the constitution of India, includes a list of scheduled caste and scheduled tribes, for whom priority is given to uplift them from continued backwardness in society because of caste structure. These castes are identified state wise and provided special benefits through reservations in educational institutions, employment, subsidized food, political representation in legislatures and the parliament (Srinivasan & Kumar 1999). The four main scheduled castes recognized in Sikkim state are Kami, Damai, Sarki and Majhi who practiced traditional occupation viz. blacksmithy, tailoring, tanning and fishing respectively. The scheduled tribes of Sikkim constitute Lepchas and Bhutias, with the Limboos and Tamangs included recently in the year 2005. Of the total rural population of 483118, the population of scheduled caste is 30837 (6.38%) and that of scheduled tribe is 188378 (38.99%). The human development status of mountain states regularly escapes appropriate assessment, and it is difficult to substantiate their position within nation-states (Kreutzmann 2001). Though Sikkim is geopolitically a strategic state, sharing its border with three countries (Nepal, Bhutan and China), it has been relatively less studied and for the first time a detailed poverty mapping study was undertaken, using scientific techniques to identify the BPL households, ascertain the level of poverty in the villages and its spatial distribution pattern. The goal of the study (United Nations Statistics Divisions, 2004) was to answer the following four questions:
2 Materials and Methods 2.1 Criteria for identifying economic backwardness The economic dimension of poverty was used for defining a BPL household. A multi-dimensional, negative criterion was used for defining a poor household. Any household meeting one or more of these conditions was identified as a non-poor household. So a poor household was identified as one which did not meet even one of these criteria.
In the year 2005, a household census was undertaken on the behest of the state government of Sikkim, using structured interviews for identifying BPL households. The Directorate of Economics, Statistics, Monitoring and Evaluation, Government of Sikkim (DESME) undertook this task of planning this census and conducting it with the support of the various departments of the state government. The census involved collecting information pertaining to parameters like housing, drinking water source, sanitation, education, health, employment, caste, assets, incomes, expenditure etc at the household level. Government employees mostly teachers were trained to take up this task of filling up the household questionnaire forms by interviewing the individual households. Verification of the data so collected was done before entering it into a database. The population size of this census which covered all the 905 households of the state of Sikkim was 93,463 households. This household data was then aggregated at the village level (panchayat ward level) and further at district and state level. 2.3 Data integration and analysis The developmental units in the state comprise of 905 Gram Pancahyat Wards (GPW), 163 Gram Panchayat Units (GPU), 24 developmental blocks and 4 districts namely North, East, South and West. The village is defined as the GPW, and its administrative boundaries were marked on the Survey of India 1:25000 scale topographical sheets. These boundaries for all the 905 gram panchayat wards were then digitized and the village maps prepared. The census data was integrated on a Geographic Information Systems (GIS) platform. ArcGIS software (version 9) was used for integration of the various layers on a GIS platform. Statistical Package for Social Sciences (SPSS) software (version 8) was used for the statistical analysis. The exchange rate of 50 Indian Rupees is equivalent to USD 1 was used. 3 Results Of the 93,451 households surveyed in rural Sikkim, 19,235 (20.58%) households were found to be BPL. Of these 1,054 (5%) are in North district and the remaining 18,181 are more or less equally distributed in the remaining three districts of East, South and West. In terms of poverty level, the South and West districts form one cluster, with 24% and 26% of the households being BPL; While North and East districts form the other cluster with 17% and 16% poverty level respectively (Table 1, Figure 1).   Figure 1: Map showing the spatial variation of poverty level measured as percentage of BPL households across A) Districts B) Villages The poverty level of the 905 gram panchayat wards in the state showed a large variation from 0% (Theng, Choten, Thingshim, Upper Tathangchen, Chongthang, Lower Likship, Lower ralong, Upper Karchi, Soreng, Bhareng) to 72% (Manghim and Beng) with the mean being 20.58% (+13.85). Out of the 905 gram panchayat wards having 19,235 poor households, 11 panchayat wards (0 poor households) had 0% poverty incidence, 389 panchayat wards (5158 poor households) had 0-20% poverty incidence, 383 panchayat wards had 20-40% poverty incidence, 111 panchayat wards (3854 poor households) had 40-60% poverty incidence, and 11 panchayat wards (487 poor households) had 60-80% poverty incidence. Of these 122 gram panchayat wards with greater than 40% poverty level, 9 are in north district, 15 in east district, 44 in south district and 54 in west district (Table 1, Figure 1). The percentage of scheduled caste and tribe population was highest in North district (85%), followed by West district (51%), East district (41%) and was least for South district (37%) (Table 1, Figure 2). The coefficient of correlation (r) at the village level, between the percentage of scheduled caste and scheduled tribe population, and the poverty level was then calculated. It was found that r was 0.21 (n=103) for North district, -0.02 (n=273) for East district, -0.05 (n=255) for South district and 0.01 (n=274) for West district, while for the state it was -0.05 (n=905).  Figure 2: Map showing the spatial variation of percentage of scheduled caste and scheduled tribe population, a commonly used indicator of backwardness across A) Districts B) Villages 4 Discussions A significantly large variation in the village poverty level was observed within all the districts. Though the South and West districts are poorer than the North and East districts, pockets of deep poverty exist in all the districts. The 122 villages with greater than 40% households as BPL, need to be recognized as backward villages and targeted in the ongoing social sector programs. The concentration of scheduled caste and tribes is the highest at 85% for North district, 51% for West district, 41% for East district and 37% for South district. Consequently, North district is always given higher priority in developmental planning compared to the other districts. In the year 2006, North district was chosen as the first phase district, to launch the National Rural Employment Guarantee Act (NREGA) (http://nrega.nic.in/) – the national flagship program for poverty alleviation. Again in the year 2007, North district was selected as the most backward district of the State, to implement the national program - Backward Regions Grant Fund (BRGF) (http://brgf.gov.in/) which is specially designed to redress regional imbalances in development. Concentration of scheduled caste and scheduled tribe population is commonly used as a surrogate indicator for measuring economic backwardness in India (Planning commission 2005). Though this proxy indicator may generally hold true for others states of the country, in the context of Sikkim, no significant correlation was found to exist between the poverty level and the percentage of scheduled caste and scheduled tribe population. Other studies have also raised doubts on whether caste should be the basis for recognizing backwardness in the country (Sivanandan 1976; Desai 1984, Srinivasan & Kumar 2004). Also Shah (1991) argues that the potential of a caste based reservation policy to sustainably empower the deprived classes is also limited and not automatic. 5 Recommendations Concerns have been raised on the ‘invisibility’ of mountain states in national policy, possibly because they are considered as an insignificant part of the total territory, or seen as too sparsely populated to be of political importance (Browne et al. 2004). Based on the findings of this study, we propose that firstly, the economically backward 122 villages should be targeted in the ongoing developmental programs, secondly the criteria and indicators used for measuring backwardness should be regionally customized, to ensure a more universal applicability especially for mountain states. Also along with proxy indicators, possibilities of using scientific, economic criteria for measuring poverty and backwardness should also be explored. Resource allocation of poverty alleviation programs which use a scientific economic criteria rather than a caste based criteria for defining poverty and backwardness will be more effective. Thirdly instead of selecting district as the planning unit for social sector programs, targeting smaller areas i.e. blocks or villages, will help in an increased coverage of the poor, and reducing leakage to the non poor. Further studies are needed to identify the determinants of poverty, which will aid in the scientific designing of poverty alleviation programs. References
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