Socioeconomic vulnerability to COVID-19 assessment framework for farm-based livelihoods in India

Abstract

COVID-19 caused unprecedented disruption globally, infecting approximately 40 million individuals in India and claiming more than 500,000 lives. India’s gross domestic product (GDP) contracted by a record 23.9% in the quarter ending June 2020 as a result of prolonged shutdowns and restrictions on transportation and mobility. Rural and farm-based livelihoods, already under multiple biotic and abiotic pressures, were severely impacted. To assess the socioeconomic vulnerability—encompassing exposure, sensitivity, and adaptive capacity—of farm-based livelihoods affected by COVID-19, a composite livelihood vulnerability index (LVI) was developed using the Intergovernmental Panel on Climate Change (IPCC) framework. The study adopted a concurrent mixed-methods design. Secondary cross-sectional quantitative data from government sources were complemented by qualitative data gathered through in-depth interviews (IDIs) and focus group discussions (FGDs) conducted with farming households across the sampled districts. The quantitative component covered a randomly selected sample of 20% of the rural districts from the five highest COVID-19-affected Indian states: Uttar Pradesh (Northern), Karnataka (Southern), West Bengal (Eastern), Maharashtra (Western), and Assam (North-Eastern). The adaptive capacity component of the framework includes natural, physical, financial, human, social, and emotional capital. The results of the study suggested that the average composite vulnerability index value was 0.104. Of the 30 districts assessed, 26 (86.66%) were found to be vulnerable. The farming population (7.1%), the literacy rate (6.8%), and the rural population (3.04%) were the strongest contributors to adaptive capacity. West Bengal required improvements in physical, financial, and emotional capital. Assam reported the lowest levels of physical, financial, and human capital. Maharashtra exhibited the highest level of exposure, while Uttar Pradesh reported the highest level of sensitivity due to extreme climatic events, pest burden, and low crop diversity. In conclusion, it is suggested to institutionalize the longitudinal monitoring of both quantitative vulnerability indices and qualitative community well-being indicators across these states, integrating climate vulnerability models for proactive shock-proofing of farm livelihoods.