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Widespread acceptance of COVID-19 vaccines is crucial for achieving sufficient immunization coverage to end the global pandemic, yet few studies have investigated COVID-19 vaccination attitudes in lower-income countries, where large-scale vaccination is just beginning. We analyze COVID-19 vaccine acceptance across 15 survey samples covering 10 low- and middle-income countries (LMICs) in Asia, Africa and South America, Russia (an upper-middle-income country) and the United States, including a total of 44,260 individuals. We find considerably higher willingness to take a COVID-19 vaccine in our LMIC samples (mean 80.3%; median 78%; range 30.1 percentage points) compared with the United States (mean 64.6%) and Russia (mean 30.4%). Vaccine acceptance in LMICs is primarily explained by an interest in personal protection against COVID-19, while concern about side effects is the most common reason for hesitancy. Health workers are the most trusted sources of guidance about COVID-19 vaccines. Evidence from this sample of LMICs suggests that prioritizing vaccine distribution to the Global South should yield high returns in advancing global immunization coverage. Vaccination campaigns should focus on translating the high levels of stated acceptance into actual uptake. Messages highlighting vaccine efficacy and safety, delivered by healthcare workers, could be effective for addressing any remaining hesitancy in the analyzed LMICs., Survey data collected across ten low-income and middle-income countries (LMICs) in Asia, Africa and South America compared with surveys from Russia and the United States reveal heterogeneity in vaccine confidence in LMICs, with healthcare providers being trusted sources of information, as well as greater levels of vaccine acceptance in these countries than in Russia and the United States.

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.

Attempts to curb illegal activity by enforcing regulations gets complicated when agents react to the new regulatory regime in unanticipated ways to circumvent enforcement. We present a research strategy that uncovers such reactions, and permits program evaluation net of such adaptive behaviors. Our interventions were designed to reduce over-fishing of the critically endangered Pacific hake by either (a) monitoring and penalizing vendors that sell illegal fish or (b) discouraging consumers from purchasing using an information campaign. Vendors attempt to circumvent the ban through hidden sales and other means, which we track using mystery shoppers. Instituting random monitoring visits are much more effective in reducing true hake availability by limiting such cheating, compared to visits that occur on a predictable schedule. Monitoring at higher frequency (designed to limit temporal displacement of illegal sales) backfires, because targeted agents learn faster, and cheat more effectively. Sophisticated policy design is therefore crucial for determining the sustained, longer-term effects of enforcement. Data collected from fishermen, vendors, and consumers allow us to document the upstream, downstream, spillover, and equilibrium effects of enforcement on the entire supply chain. The consumer information campaign generates two-thirds of the gains compared to random monitoring, but is simpler for the government to implement and almost as cost-effective., 63 pages; Keywords: Enforcement, Regulation, Law and Economics, Fisheries; JEL Classification Codes: K42, O1, L51

In order to induce farmers to adopt a productive new agricultural technology, we apply simple and complex contagion diffusion models on rich social network data from 200 villages in Malawi to identify seed farmers to target and train on the new technology. A randomized controlled trial compares these theory-driven network targeting approaches to simpler strategies that either rely on a government extension worker or an easily measurable proxy for the social network (geographic distance between households) to identify seed farmers. Our results indicate that technology diffusion is characterized by a complex contagion learning environment in which most farmers need to learn from multiple people before they adopt themselves. Network theory based targeting can out-perform traditional approaches to extension, and we identify methods to realize these gains at low cost to policymakers. Keywords: Social Learning, Agricultural Technology Adoption, Complex Contagion, Malawi JEL Classification Codes: O16, O13, 61 pages

Hunger during pre-harvest lean seasons is widespread in the agrarian areas of Asia and Sub-Saharan Africa. We randomly assign an $8.50 incentive to households in rural Bangladesh to temporarily out-migrate during the lean season. The incentive induces 22% of households to send a seasonal migrant, their consumption at the origin increases significantly, and treated households are 8–10 percentage points more likely to re-migrate 1 and 3 years after the incentive is removed. These facts can be explained qualitatively by a model in which migration is risky, mitigating risk requires individual-specific learning, and some migrants are sufficiently close to subsistence that failed migration is very costly. We document evidence consistent with this model using heterogeneity analysis and additional experimental variation, but calibrations with forward-looking households that can save up to migrate suggest that it is difficult for the model to quantitatively match the data. We conclude with extensions to the model that could provide a better quantitative accounting of the behavior.