Research

Mask usage remains low across many parts of the world during the COVID-19 pandemic, and strategies to increase mask-wearing remain untested. Our objectives were to identify strategies that can persistently increase mask-wearing and assess the impact of increasing mask-wearing on symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections.

We study optimal dynamic lockdowns against COVID-19 within a commuting network. Our framework integrates canonical spatial epidemiology and trade models and is applied to cities with varying initial viral spread: Seoul, Daegu, and the New York City metropolitan area (NYM). Spatial lockdowns achieve substantially smaller income losses than uniform lockdowns. In the NYM and Daegu—with large initial shocks—the optimal lockdown restricts inflows to central districts before gradual relaxation, while in Seoul it imposes low temporal but large spatial variation. Actual commuting reductions were too weak in central locations in Daegu and the NYM and too strong across Seoul.

We use a two-stage experiment to study how a short-term subsidy for a new product affects uptake, usage, and future demand for the same product (a new solar lamp). We use an auction design to gauge willingness-to-pay, and randomly vary the strike price across villages to create random variation in purchase prices and uptake across villages. Our main results are that subsidies do not adversely affect subsequent product use, but stimulate uptake. If subsidies depress future willingness-to-pay, then this effect is outweighed by additional learning about the benefits of the new product. The net effect is that short-term subsidies increase future willingness-to-pay. However; prices play an important allocative role, and lowering prices via subsidies encourages uptake by households with low use intensity. We do not find any evidence supporting social learning and anchoring beyond the initial sample of beneficiaries.

We exploit recent molecular genetics evidence on the genetic basis of arsenic excretion and unique information on family links among respondents living in different environments from a large panel survey to uncover the hidden costs of arsenic poisoning in Bangladesh. We provide for the first time estimates of the effects of the ingestion and retention of inorganic arsenic on direct measures of cognitive and physical capabilities as well as on the schooling attainment, occupational structure, entrepreneurship, and incomes of the rural Bangladesh population. We also provide new estimates of the effects of the consumption of foods grown and cooked in arsenic-contaminated water on individual arsenic concentrations. The estimates are based on arsenic biomarkers obtained from a sample of members of rural households in Bangladesh who are participants in a long-term panel survey following respondents and their coresident household members over a period of 26 years.

We propose a methodology for defining urban markets based on builtup landcover classified from daytime satellite imagery. Compared to markets defined using minimum thresholds for nighttime light intensity, daytime imagery identify an order of magnitude more markets, capture more of India’s urban population, are more realistically jagged in shape, and reveal more variation in the spatial distribution of economic activity. We conclude that daytime satellite data are a promising source for the study of urban forms.

Occasional widely publicized controversies have led to the perception that growth statistics from developing countries are not to be trusted. Based on the comparison of several data sources and analysis of novel IMF audit data, we find no support for the view that growth is on average measured less accurately or manipulated more in developing than in developed countries. While developing countries face many challenges in measuring growth, so do higher-income countries, especially those with complex and sometimes rapidly changing economic structures. However, we find consistently higher dispersion of growth estimates from developing countries, lending support to the view that classical measurement error is more problematic in poorer countries and that a few outliers may have had a disproportionate effect on (mis)measurement perceptions. We identify several measurement challenges that are specific to poorer countries, namely limited statistical capacity, the use of outdated data and methods, the large share of the agricultural sector, the informal economy, and limited price data. We show that growth measurement based on the System of National Accounts (SNA) can be improved if supplemented with information from other data sources (for example, satellite-based data on vegetation yields) that address some of the limitations of SNA.

Widespread social distancing and lockdowns of everyday activity have been the primary policy prescription across many countries throughout the coronavirus disease 2019 (COVID-19) pandemic. Despite their uniformity, these measures may be differentially valuable for different countries. We use a compartmental epidemiological model to project the spread of COVID-19 across policy scenarios in high- and low-income countries. We embed estimates of the welfare value of disease avoidance into the epidemiological projections to estimate the return to more stringent lockdown policies.

Can targeting information to network-central farmers induce more adoption of a new agricultural technology? By combining social network data and a field experiment in 200 villages in Malawi, we find that targeting central farmers is important to spur the diffusion process. We also provide evidence of one explanation for why centrality matters: a diffusion process governed by complex contagion. Our results are consistent with a model in which many farmers need to learn from multiple people before they adopt themselves. This means that without proper targeting of information, the diffusion process can stall and technology adoption remains perpetually low.

Rural isolation can limit access to basic services and income-generating opportunities. Among some communities, rainfall induced flooding can cause increased uncertainty where first-mile transportation infrastructure is limited. In Rwanda, this challenge is apparent, where 90% of the population below the poverty line live in rural areas that are typically mountainous with frequent flooding - events that may be increasing in frequency and severity as the climate changes. To reduce these transportation barriers, the non-profit organization Bridges to Prosperity (B2P) plans to construct hundreds of trailbridges in Rwanda between 2018 and 2023. This scale of rural infrastructure services presents an opportunity for experimental investigation of the effects of these new trailbridges on economic, health, agricultural and education outcomes in rural communities. In this paper, we present a cohort study evaluating the potential community benefits of rural trailbridges - including economic, health and social outcomes for Rwandan communities experiencing environmental change. We examined households living near 12 trailbridge sites and 12 comparison sites over February 2019–March 2020. We found that labor market income increased by 25% attributable to the trailbridges. We did not observe any significant effects on agricultural income, education or health outcomes, however given the small sample and short duration of this study we anticipate observing additional outcomes within the recently started 200 site, 4 year trial.

The integration of markets may improve efficiency by lowering costs or reducing local market power. India, seeking to reduce electricity shortages, set up a new power market, in which transmission constraints sharply limit trade between regions. During congested hours, measures of market competitiveness fall and firms raise bid prices. I use confidential bidding data to estimate the costs of power supply and simulate market outcomes with more transmission capacity. Counterfactual simulations show that transmission expansion increases market surplus by 22 percent, enough to justify the investment. One-third of this gain is due to sellers' response to a more integrated grid.

Human capital—that is, resources associated with the knowledge and skills of individuals—is a critical component of economic development. Learning metrics that are comparable for countries globally are necessary to understand and track the formation of human capital. The increasing use of international achievement tests is an important step in this direction. However, such tests are administered primarily in developed countries, limiting our ability to analyse learning patterns in developing countries that may have the most to gain from the formation of human capital. Here we bridge this gap by constructing a globally comparable database of 164 countries from 2000 to 2017. The data represent 98% of the global population and developing economies comprise two-thirds of the included countries. Using this dataset, we show that global progress in learning—a priority Sustainable Development Goal—has been limited, despite increasing enrolment in primary and secondary education.

Despite numerous journalistic accounts, systematic quantitative evidence on economic conditions during the ongoing COVID-19 pandemic remains scarce for most low- and middle-income countries, partly due to limitations of official economic statistics in environments with large informal sectors and subsistence agriculture. We assemble evidence from over 30,000 respondents in 16 original household surveys from nine countries in Africa (Burkina Faso, Ghana, Kenya, Rwanda, Sierra Leone), Asia (Bangladesh, Nepal, Philippines), and Latin America (Colombia). We document declines in employment and income in all settings beginning March 2020. The share of households experiencing an income drop ranges from 8 to 87% (median, 68%). Household coping strategies and government assistance were insufficient to sustain precrisis living standards, resulting in widespread food insecurity and dire economic conditions even 3 months into the crisis. We discuss promising policy responses and speculate about the risk of persistent adverse effects, especially among children and other vulnerable groups.

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.

This paper uses a dataset from Tanzania with information on consumption, income, and income shocks within and across family networks. Crucially and uniquely, it also contains data on the degree of information existing between each pair of households within family networks. We use these data to construct a novel measure of the quality of information both at the level of household pairs and at the level of the network. We also note that the individual level measures can be interpreted as measures of network centrality. We study risk sharing within these networks and explore whether the rejection of perfect risk sharing that we observe can be related to our measures of information quality. We show that households within family networks with better information are less vulnerable to idiosyncratic shocks. Furthermore, we show that more central households within networks are less vulnerable to idiosyncratic shocks. These results have important implications for the characterisation of the empirical failure of the perfect risk-sharing hypothesis and point to the importance of information frictions.

Even with comparable innate ability and performance, women may be subject to discrimination. We run a field experiment across 143 Malawian villages in which either men or women were assigned the task of learning about a new agricultural technology, and then communicating it to others to convince them to adopt. Objective measures of these communicators’ knowledge and adoption of the new technology show no gender gap in their ability to acquire, retain and use the information. Yet, micro-data on individual interactions from 6500 farmers show that other farmers are less willing to learn from female communicators, whom they perceive not to be as good at farming as their male counterparts. In spite of this, other farmers learn just as much about the technology when the communicator role is reserved for women, and they experience similar farm yields.