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Vulnerable Groups and the COVID-19: The Indian Case


The current unraveling COVID-19 pandemic poses severe challenges not only in the realm of health, but also for economic and social systems around the globe. It can exacerbate longstanding inequities between groups and leave marginalized groups in a vulnerable position; preliminary evidence from the UK and US suggest that racial and ethnic minorities are more likely to be affected negatively, both on the health, as well as the economic dimension. In the context of India, we believe the pandemic particularly endangers individuals belonging to the traditionally socioeconomically disadvantaged groups.

We focus on one such group called the Scheduled Castes (SC). The SC are vulnerable for three main reasons. First, their low social status forces them to live in conditions that put them at high risk of acquiring COVID-19 and other diseases; second, their poor economic status makes them particularly vulnerable to the economic crisis induced by the pandemic; third, the atmosphere of uncertainty and fear stemming from COVID-19 may lead to a surge in stigma and mistrust, aggravating existing discrimination against such groups.

We first present descriptive evidence highlighting the disadvantage faced by the SC in the above three realms – living conditions, economic status and prejudice – by comparing SC to ‘the General Category’; the collection of groups that are top of the caste hierarchy. For this purpose, we use data from the most recent round of the National Family Health Survey (NFHS-IV 2015-16) and the Indian Human Development Survey (IHDS-II 2011-12). Subsequently, we discuss how human capital provides an important coping mechanism and how caste-based discrimination may stymie its potential through initial disparities, and the further widening of these in face of the pandemic. We conclude by highlighting how our research aims at understanding the motives behind caste-based disparities and how this improved understanding can help guide policies to reduce group disparities.

Authors: Rajesh Ramachandran, Devesh Rustagi, and Emilia Soldani

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