In March 2020, migration patterns that had taken root over decades in South Asia were dramatically reversed. Spurred by job losses and the anticipation of lockdowns in response to the COVID-19 pandemic, a mass movement of migrants rushed to get home. Migrants flew home from Europe, the Gulf, North America, and elsewhere in Asia. Domestic migrants took trains from cities back to the towns and villages where they had been raised.
We may look back at this moment as The Great Reverse Migration. In South Asia, the movement has been described as the largest mass migration since the 1947 partition of India. The reverse migration immediately brought fear that the travelers would spread COVID-19 as they journeyed home. Migrants were welcomed warily, and sometimes shunned.
What role did the movement of migrants play in the diffusion of the pandemic in South Asia?
To study this question, we extracted daily data on district-level cases of COVID-19 in Bangladesh, India, and Pakistan. We matched those data with labor force surveys and household-level economic surveys from prior years to measure the extent of migration outflow, or outmigration, for each of 755 districts in the three countries. Matching the scraped data with the surveys’ data allowed us to distinguish between international and domestic migration from the districts—an important distinction given that COVID-19 infections were, at least at first, coming from abroad.
To answer this question, we regressed data on coronavirus cases across districts on measures of district-level outmigration. Data on international and domestic migration by district for Bangladesh were computed from the 2016 Household Income and Expenditure Survey (HIES), for India from the 2007-8 round of India’s National Sample Survey (NSS), and for Pakistan from the Pakistan Social and Living Standards (PSLM) 2014-2015 survey and the Pakistan Labour Force Survey (LFS) 2007-2008.
The results show that international migration is a surprisingly strong predictor of the spread of COVID-19 in countries. The data establishes that international migration predicts the spread of the coronavirus across and within districts in India and Pakistan. In the 45 days following the first 100th case in India, for example, a 1 standard deviation (SD) increase in international out-migration from a given district (measured as the number of out-migrants per capita) predicts, on average, a 25% higher probability that the district reported any COVID-19 cases. A 1 SD increase in prior international out-migration in a given Indian district is also associated with a 48% increase in the number of cases per capita in those 45 days (March 15-April 28, 2020). In Pakistan, similarly, international out-migration predicts a 48% increase in the number of cases per capita in a district (March 16-April 27, 2020).
The number of migrants who left a district to work within the country—domestic migrants—is a weaker and less consistent predictor of contagion. Domestic migration has been a focus of local fears and national policy debate, but the detectable effects of domestic migration are low in Bangladesh (a 3% increase in the probability of any COVID-19 cases is predicted by a 1 SD increase in domestic migration in the average district) and negative in Pakistan. In India, where debate has been most vocal, a 1 SD increase in domestic migration in a given district predicts a 11% increase in whether or not a district reports any cases of COVID-19 during the study period, but predictions of the number of COVID-19 cases are imprecisely measured.
Tracking the evolution and diffusion of COVID-19 over time shows that the predictive power of the measures of international migration to explain the number of COVID-19 cases is rising in the three countries, while predictions are flat or falling with respect to measures of domestic migration.
The correlations are large, but most of the spread of COVID-19 comes from a combination of forces. The pattern of coronavirus cases is affected by demographics, climate, the stringency of lockdowns, the nature of the initial spread, and other factors. In our empirical models, broad temporal patterns are controlled for with day fixed effects, but the estimates are not causal parameters and the results necessarily reflect complicated interactions of biology, policy, and human behavior.
This post presents main findings of the authors’ published paper “Migration, externalities, and the diffusion of COVID-19 in South Asia.”