Interstate Migration in India During the COVID-19 Pandemic: An Analysis Based on Mobile Visitor Location Register and Roaming Data

Abstract

In migration and mobility studies, the availability of scientifically reliable data remains a persistent challenge. The recent move towards harnessing mobile and big data has also been unable to resolve the data issues due to accessibility, privacy, as well as ethical and methodological intricacies involved with such data sets. In this paper, we explore a new set of data known as visitor location register (VLR) and roaming data, which is recorded and reported by mobile service providers. The reporting model of VLR data used and presented in this paper is not only free from privacy and ethical concerns but also methodologically sound and simple to compute as compared to any previous approaches. Drawing on VLR data, this paper finds direct evidence of unusually high interstate net reverse migration during the first and second COVID-19 lockdowns in India (44.13 and 26.3 million, respectively), and thereafter quick return migration back to cities during unlocks. The findings from this paper also provide insights into evolving migration directions, precarity, pockets of origin and destination and state policies in containing reverse migration during lockdowns in India. We anticipate that the data presented in the paper have the potential to fill a major data gap in migration and mobility studies in other countries too if VLR and roaming data are made available at the required spatial and temporal levels.

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