No abstract

The main theme of this paper is to revisit the question of caste and the politics of/on traditions. We have explored the questions of mythology; how ‘we’, the lower caste people associate and (re)interpret the mythical characters as a process of social upward mobility. So, is it the invention of tradition or is it the inversion of tradition or both? Interestingly, we could be able to locate a distinct regional pattern in this case. Thus, we argue that in the Northern part of India, specifically in Uttar Pradesh, though it is the process called the invention of tradition, it can be framed as a little tradition under the grand Hindu tradition. On the other hand, in the Southern part of the country specifically in Tamil Nadu, it is rather the process called the inversion of tradition which is much more radically grounded in sub-national ethos. Based on these premises, this article further argues that Uttar Pradesh’s caste politics is based on the invention of a tradition model which can incorporate the lower castes’ little traditions within the larger ambit of the Hindu grand narratives. Thus, new Hindutva politics has easily appropriated them within their polemic. On the contrary, in the South, due to the inversion of the tradition model embedded in a pre-existing political tradition and sub-national ethos, Hindutva failed to get a proper hold in recent times.

For a long time, studies in social sciences focused on various dimensions but somehow talked less about the spatial dimension of inequality as well as the spatial dimension of quality of living in India. Therefore the primary objective of this study is modelling the spatial dependence of quality of living (QOL) across Indian districts using various spatial econometric tools. First of all we calculated quality of living index by using principal component analysis. Then we calculated descriptive statistics and used Choropleth mapping to understand the nature of variables distribution. After that, we used Moran’s I statistics and LISA statistics to understand global and local spatial dependence. Lastly, we used spatial error model to understand the spatial dependence with covariates. The high Moran’s I value suggests that the clustered nature of QOL across districts. LISA mapping reflects the localized nature of spatial clustering of QOL. It is evident from the SEM model that level of urbanization, workforce in service sector, female literacy rate and higher education have significantly positive impact on QOL. On the contrary, QOL decreased with the increasing concentration of SCs and STs population. It can be argued that geography plays a vital role in determining the spatial patterning of QOL of the districts of India. From the perspective of policy implication, spatially targeted policies and programmes are required. In this context, spreading economic development and the growth outcome to the districts having low QOL should be a primary step in the policy response.