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This study explores the relationship between migration and household resilience during a global crisis that eliminated the option to migrate. We link prior data from four populations in Bangladesh and Nepal to new phone surveys conducted during the early months of the COVID-19 pandemic. While earnings fell universally, pandemic-induced declines were 14%–25% greater among previously migration-dependent households and urban migrant workers, with household remittance losses far exceeding official statistics. Heightened economic exposure during the pandemic erased prior gains achieved by transnational migrants and caused fourfold greater prevalence of food insecurity among domestic subsistence migrants. Economic distress spilled over onto non-migrants in high-migration villages and labor markets. We show that migration contributed to economic contagion independent of its role in disease transmission. Losing the option to migrate differentially increased the vulnerability of migration-dependent households during a crisis.

75% of the world's poor reside in rural areas where the local economy is tied to agriculture. We interpret new panel data on COVID-19 from Nepal and Bangladesh in relation to agricultural seasonality. Conditions in April–June 2020 were comparable to a typical lean season even though the pandemic arrived at harvest time. Income losses stem from both depressed local employment as well as lower migration and remittances. We also document indirect adverse health impacts on nutrition and mental health. Findings are specific to the nature of economic activity at harvest, and effective pandemic policy must evolve with the agricultural season.

The COVID-19 pandemic has upended health and living standards around the world. This article provides an interim overview of these effects, with a particular focus on low- and middle-income countries (LMICs). Economists have explained how the pandemic is likely to have different consequences for LMICs and demands distinct policy responses compared to those of rich countries. We survey the rapidly expanding body of empirical research that documents the pandemic's many adverse economic and noneconomic effects in terms of living standards, education, health, and gender equality, which appear to be unprecedented in scope and scale. We also review research on successful and failed policy responses, including the failure to ensure widespread vaccine coverage in many LMICs, which is needed to end the pandemic. We close with a discussion of implications for public policy in LMICs and for the institutions of international governance, given the likelihood of future pandemics and other major shocks (e.g., climate).

The COVID-19 pandemic has upended health and living standards around the world. This article provides an interim overview of these effects, with a particular focus on low- and middle-income countries (LMICs). Economists have explained how the pandemic is likely to have differential consequences for LMICs, and demand distinct policy responses, compared to rich countries. We survey the rapidly expanding body of empirical research that documents its many adverse economic and non-economic effects in terms of living standards, education, health, and gender equality, which appear to be unprecedented in depth and scale. We also review research on successful and failed policy responses, including the failure to ensure widespread vaccine coverage in LMICs, which is needed to end the pandemic. We close with a discussion of implications for public policy in LMICs, and for the institutions of international governance, given the likelihood of future pandemics and other major shocks (e.g., climate).

Recent studies find that observational returns to rural-urban migration are near zero in three developing countries. We revisit this result using panel tracking surveys from six countries, finding higher returns on average. We then interpret these returns in a multi-region Roy model with heterogeneity in migration costs. In the model, the observational return to migration confounds the urban premium and the individual benefits of migrants, and is not directly informative about the welfare gain from lowering migration costs. Patterns of regional heterogeneity in returns, and a comparison of experimental to observational returns, are consistent with the model’s predictions.

Background The Coronavirus Disease 2019 (COVID-19) pandemic and associated mitigation policies created a global economic and health crisis of unprecedented depth and scale, raising the estimated prevalence of depression by more than a quarter in high-income countries. Low- and middle-income countries (LMICs) suffered the negative effects on living standards the most severely. However, the consequences of the pandemic for mental health in LMICs have received less attention. Therefore, this study assesses the association between the COVID-19 crisis and mental health in 8 LMICs. Methods and findings We conducted a prospective cohort study to examine the correlation between the COVID-19 pandemic and mental health in 10 populations from 8 LMICs in Asia, Africa, and South America. The analysis included 21,162 individuals (mean age 38.01 years, 64% female) who were interviewed at least once pre- as well as post-pandemic. The total number of survey waves ranged from 2 to 17 (mean 7.1). Our individual-level primary outcome measure was based on validated screening tools for depression and a weighted index of depression questions, dependent on the sample. Sample-specific estimates and 95% confidence intervals (CIs) for the association between COVID-19 periods and mental health were estimated using linear regressions with individual fixed effects, controlling for independent time trends and seasonal variation in mental health where possible. In addition, a regression discontinuity design was used for the samples with multiple surveys conducted just before and after the onset of the pandemic. We aggregated sample-specific coefficients using a random-effects model, distinguishing between estimates for the short (0 to 4 months) and longer term (4+ months). The random-effects aggregation showed that depression symptoms are associated with a increase by 0.29 standard deviations (SDs) (95% CI [-.47, -.11], p-value = 0.002) in the 4 months following the onset of the pandemic. This change was equivalent to moving from the 50th to the 63rd percentile in our median sample. Although aggregate depression is correlated with a decline to 0.21 SD (95% CI [-0.07, -.34], p-value = 0.003) in the period thereafter, the average recovery of 0.07 SD (95% CI [-0.09, .22], p-value = 0.41) was not statistically significant. The observed trends were consistent across countries and robust to alternative specifications. Two limitations of our study are that not all samples are representative of the national population, and the mental health measures differ across samples. Conclusions Controlling for seasonality, we documented a large, significant, negative association of the pandemic on mental health, especially during the early months of lockdown. The magnitude is comparable (but opposite) to the effects of cash transfers and multifaceted antipoverty programs on mental health in LMICs. Absent policy interventions, the pandemic could be associated with a lasting legacy of depression, particularly in settings with limited mental health support services, such as in many LMICs. We also demonstrated that mental health fluctuates with agricultural crop cycles, deteriorating during 'lean', pre-harvest periods and recovering thereafter. Ignoring such seasonal variations in mental health may lead to unreliable inferences about the association between the pandemic and mental health., Author(s): Nursena Aksunger 1, Corey Vernot 2, Rebecca Littman 3, Maarten Voors 4, Niccolò F. Meriggi 5, Amanuel Abajobir 6, Bernd Beber 7, Katherine Dai 8, Dennis Egger 9, Asad [...]

Reliable testing data for new infectious diseases like COVID‐19 is scarce in developing countries making it difficult to rapidly diagnose spatial disease transmission and identify at‐risk areas. We propose a method that uses readily available data on bi‐lateral migration channels combined with COVID‐19 cases at respective migrant destinations to construct a spatially oriented risk index. We find significant and consistent association between our measure and various types of outcomes including actual COVID‐19 cases and deaths, indices of government policy responses, and community mobility patterns. Results suggest that future pandemic models should incorporate migration‐linkages to predict regional socio‐economic and health risk exposure. [ABSTRACT FROM AUTHOR]