Market Liberalism,Marginalised Citizens and Countermovements in India

How has neo-liberalism transformed the economic structure and policies of India? And what are the politico-economic implications of such policies for marginalised populations? Following Karl Polanyi’s theory of “double movement”, this paper argues that while market liberalism has helped India overcome the slow so-called “Hindu rate of growth”, it has adversely affected the economic interests of the poor. It further argues that the expansion of the market (first movement) has led to various social dislocations in the lives of the poor. Such dislocations have generated several countermovements (second movement), which have found expressions not just in electoral politics but also in various grassroots movements. While it may be true that such countermovements have not always been successful in overturning the tide of neo-liberalism, they have certainly influenced the policy priorities of the state in favour of the poor and the marginalised in India.     

The Spatial Association of Demographic and Population Health Characteristics with COVID-19 Prevalence Across Districts in India

In less-developed countries, the lack of granular data limits the researcher's ability to study the spatial interaction of different factors on the COVID-19 pandemic. This study designs a novel database to examine the spatial effects of demographic and population health factors on COVID-19 prevalence across 640 districts in India. The goal is to provide a robust understanding of how spatial associations and the interconnections between places influence disease spread. In addition to the linear Ordinary Least Square regression model, three spatial regression models—Spatial Lag Model, Spatial Error Model, and Geographically Weighted Regression are employed to study and compare the variables explanatory power in shaping geographic variations in the COVID-19 prevalence. We found that the local GWR model is more robust and effective at predicting spatial relationships. The findings indicate that among the demographic factors, a high share of the population living in slums is positively associated with a higher incidence of COVID-19 across districts. The spatial variations in COVID-19 deaths were explained by obesity and high blood sugar, indicating a strong association between pre-existing health conditions and COVID-19 fatalities. The study brings forth the critical factors that expose the poor and vulnerable populations to severe public health risks and highlight the application of geographical analysis vis-a-vis spatial regression models to help explain those associations.