The Urbanization-Migrant Labour-COVID-19 Trifecta: Regression Analysis and Policy Implications for Indian Urban Centres

Abstract

This study examines the complex relationship between urbanization, migrant labour and the spread of COVID-19 in India, highlighting the vulnerabilities faced by migrant labours during the pandemic. Rapid urbanization has changed the demographic and economic landscape of India, attracting millions of people to the urban areas which led to unplanned development, overpopulation and the emergence of informal settlements, which have increased inequality, put the urban poor at greater risk in times of crisis and COVID-19 has highlighted this vulnerability. Drawing from comprehensive datasets obtained from the Census of India, supplemented by information gathered from the official COVID-19 databases spanning various states, union territories, districts, and cities, the research investigates the nuanced relationships between urbanization patterns, migrant labour trends, and pandemic dynamics. Both descriptive and inferential statistics have been used to show the relationship between urbanization and COVID-19. The findings show that districts with higher urban populations, a greater number of towns, and a larger urban working population experienced higher COVID-19 prevalence. For validating the fact, both Poisson and Negative Binomial regression models were used among which, the Negative Binomial model was found to be more suitable due to its ability to handle overdispersion in the data. The study also reviews policy measures by Government of India like the One Nation One Ration (ONOR) scheme, Atmanirbhar Bharat Rozgar Yojana (ABRY) which aimed to support migrant workers during the pandemic. The analysis reveals the critical importance of policy interventions in addressing the challenges faced by urban migrant populations. The findings of this study can be used to inform policies and interventions to improve the urbanization and migrant labour scenario in India.