Inequity and marginalisation of tribes in Sub- himalayan West Bengal

Abstract

As agrarian changes take place rural communities adapt themselves. India Rural Development Report 2013-14 identified that excluding the northern states of India, technology and diversification in rural economy have contributed around 40 per cent of agricultural growth in 2000s. Marginal farmers are now diversifying themselves and also include animal husbandry that acts as insurance during crop failures and provides income when needed. The report also reveals that farmer with insufficient income resort to leasing-in land to increase their income. With this background, the present study has tried to identified the diversification among tribal communities in sub-Himalayan West Bengal through micro-level studies. The present study was performed in two districts of Sub-Himalayan region of West Bengal i.e., Alipurduar and Jalpaiguri districts. We surveyed 400 Scheduled Tribe households using structured questionnaires and also conducted Focused Grouped Discussion in each of the village surveyed. The sample size was selected with 5 per cent margin of errors. The study covered two villages from each district and from each village 100 Scheduled Tribe households were surveyed. The data obtained was then examined using various statistical techniques with the help of M.S. Excel and STATA version 17 software. Based on theoretical and empirical literature four micro-econometric models were developed to examine the proposed hypothesis. First, a binary logistic model was constructed to identify the factors that influences agricultural productivity of ST households to support the hypothesis that there is direct relationship between ST agricultural productivity and access to capital and to make comparison with the non-ST (Others) households. This hypothesis was examined using the unit-level data of NSS 77th round Land & Livestock Holdings at all India level (2019) for 9780 ST households with agricultural landholdings and 11938 non-ST households with agricultural landholdings. The results from the analysis are described below. The outcome of the study was that accessibility to physical capital i.e., landholdings and irrigation facilities, human capital i.e., household size, and age of head of household were found statistically significant in case of 9780 ST households. For 11938 non-ST, the results obtained was that accessibility to physical capital i.e., landholdings and irrigation facilities; human capital i.e., household size, agricultural training and age of head of household were found statistically significant to determine odds of the high agricultural productivity. When the results of two social groups was considered, the study identified that agricultural training program that was found statistically significant in case of non-ST, it was insignificant for ST households. When comparing the value of odds ratio for common significant independent variables found in case of two social groups, the use of irrigation and household were found to increase the agricultural productivity more in case of non-ST households compared to ST households. The other two independent variables age of head of household and landholdings have almost similar impact. Second, a logistic regression model was constructed to examine and identify the factors that influences rural livelihood diversification to support the hypothesis that there is relationship between rural ST livelihood diversification and its socio-economic factors. This hypothesis was examined using the field survey data for 400 ST households conducted in two different eco-regions of sub-Himalayan West Bengal. The results obtained for 200 ST households residing in forest-riverine eco-region of Alipurduar district was that WPR, landholdings and non-farm income were found statistically significant. But the results for 200 ST households residing in Teesta-Alluvial eco-regions of Jalpaiguri district was that only non-farm income was found statistically significant. Third, an ordered logistic model was constructed to identify the factors influences the level of vulnerability to support the hypothesis that there is relationship between poverty of ST and their socio-economic factors. Using the filed survey data of 400 ST households, the hypothesis was examined. The factors like WPR, landholdings, education of head of household and total income from both farm and non-farm were found statistically significant to influence the level of vulnerability. Finally, multi regression model was constructed to identify the factors influencing rural non-farm wages to support the hypothesis that there is relationship between rural non-farm wages and knowledge and skill formation of ST households. This hypothesis was being examined using the field survey data of 400 ST households in the sub-Himalayan regions of West Bengal. These results showed that years of schooling of household members, WPR and landholdings were statistically significant to influence the rural non-farm wages.