Centre for Development Economics
Department of Economics, Delhi School of Economics


 Economic Geography of Contagion: A Study on COVID 19 Outbreak in India


Anirban Mukherjee (Calcutta University)

This is a joint work with Tanika Chakraborty (IIM Calcutta)

Thursday, 8 April 2021 at 3:00 PM IST

 Covid-19 contagion which, as of February, 2021, infected more than 100 million people and claimed two and half million lives worldwide is being labelled as the worst epidemic since the Spanish flu of 1918. In spite of its global spread, we observe considerable heterogeneity across countries; the epidemic created a greater impact on the European and American countries compared to the Asian and African ones. Such heterogeneity does not only exist across countries as we observe considerable within-country variation in the number of infections and deaths. For example, at any given point of time, the total number of Covid-19 patients in India seem to be largely driven by 4-5 out of the 28 states and 8 union territories. In this paper, we propose a regional inequality-based mechanism that explains such heterogeneity and provide supporting evidence using the Indian data. We base our theoretical argument on a well-established economic geography theory which predicts that the process of development leads to regional inequality. Specifically, it creates a core-periphery pattern where the industrially developed core supplies goods and services to the underdeveloped periphery. We argue that if an outbreak takes place in an area(state) characterized by the core-periphery structure, it becomes difficult to isolate peripheries(districts) from the core(districts) and contain the outbreak in one region. In an area(state) consisting of a few similarly developed sub-regions (districts), on the other hand, one sub-region is less dependent on another and can function in autarky. Hence, in the event of an outbreak in one of these sub-regions, the infected region can be easily disconnected from the rest of the regions. Following our argument above, we expect the contagion will be less in Indian states consisting of similarly developed districts and high in states characterized by a core-periphery structure. In order to test our hypothesis, we use the nightlight data to measure the degree of nightlight-inequality at the state level and estimate its impact on COVID-19 infection rate. We find that the disease spreads more in states characterized by greater regional inequality in nightlights. However, the state level analysis does not allow us to address state level unobserved heterogeneity and nightlights is just one indicator of development. Hence, we re-examine our hypothesis using two alternate approaches. First, we measure nightlight-inequality at the level of district-neighbourhoods which exploits variation in regional inequality within a state. Second, we measure inequality using industrial heterogeneity in a district. We find that the results are consistent with our theoretical hypotheses. Finally, we investigate how the effect of regional inequality on the Covid-19 contagion varies with different degrees of movement restrictions using the different phases of lockdown and unlock in India. We find that regional inequality has a stronger impact on Covid-19 infection rate during the phases of unlock when the movement restrictions were considerably relaxed.

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