Department of Agricultural and Resource Economics

Ubiquitous mobile phone ownership makes phone surveying an attractive method of low-cost data collection. We explore differences between in-person and phone survey measures of agricultural production collected for an impact evaluation in India. Phone responses have greater mean and variance, a difference that persists even within a subset of respondents that answered the same question over both modes. Treatment effect estimation remains stable across survey mode, but estimates are less precise when using phone data. These patterns are informative for cost and sample size considerations in study design and for aggregating evidence across study sites or time periods.

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.

Subsidies are widely criticized in fisheries management for promoting global fishing capacity growth and overharvesting. Scientists worldwide have thus called for a ban on "harmful" subsidies that artificially increase fishing profits, resulting in the recent agreement among members of the World Trade Organization to eliminate such subsidies. The argument for banning harmful subsidies relies on the assumption that fishing will be unprofitable after eliminating subsidies, incentivizing some fishermen to exit and others to refrain from entering. These arguments follow from open-access governance regimes where entry has driven profits to zero. Yet many modern-day fisheries are conducted under limited-access regimes that limit capacity and maintain economic profits, even without subsidies. In these settings, subsidy removal will reduce profits but perhaps without any discernable effect on capacity. Importantly, until now, there have been no empirical studies of subsidy reductions to inform us about their likely quantitative impacts. In this paper, we evaluate a policy reform that reduced fisheries subsidies in China. We find that China's subsidy reductions accelerated the rate at which fishermen retired their vessels, resulting in reduced fleet capacity, particularly among older and smaller vessels. Notably, the reduction of harmful subsidies was only partly responsible for reducing fleet capacity; an increase in vessel retirement subsidies was also a necessary driver of capacity reduction. Our study demonstrates that the efficacy of removing harmful subsidies depends on the policy environment in which removals occur.

Ninety-seven percent of Sierra Leonean households prepare food over wood or charcoal, a practice that leads to adverse health and environmental consequences. In this pilot study, we introduced ethanol cookstoves to households in Bo, Sierra Leone. We assessed their potential as an alternative to biomass fuels and the only existing improved cookstove, butane gas. Ethanol cookstoves were economically competitive with butane stoves, but could not outcompete biomass fuel (wood and charcoal). The cookstoves displayed significant benefits to women in time savings and comfort, but raised concerns around alcoholism, unequal access to technologies, and other gendered constraints in the cultural context.

Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. agricultural yield, along with a higher-frequency regressor, e.g. daily temperature. Applied researchers then face a problem of selecting a model to characterize the nonlinear relationship between the outcome and the high-frequency regressor to make a policy recommendation based on the model-implied damage function. We show that existing model selection criteria are only suitable for the policy objective if one of the models under consideration nests the true model. If all models are seen as imperfect approximations of the true nonlinear relationship, the model that performs well in the historical climate conditions is not guaranteed to perform well at the projected climate. We therefore propose a new criterion, the proximity-weighted mean squared error (PWMSE) that directly targets precision of the damage function at the projected future climate. To make this criterion feasible, we assign higher weights to historical years that can serve as “weather analogs” to the projected future climate when evaluating competing models using the PWMSE. We show that our approach selects the best approximate regression model that has the smallest weighted squared error of predicted impacts for a projected future climate. A simulation study and an application revisiting the impact of climate change on agricultural production illustrate the empirical relevance of our theoretical analysis.

Protected areas (PAs) can help address biodiversity loss by promoting conservation while fostering economic development through sustainable tourism. Nature-based tourism can generate economic benefits for communities in and around PAs; however, its impacts do not lend themselves to conventional impact evaluation tools. We utilize a Monte Carlo simulation approach with econometric estimations using microdata to estimate the full economic impact of nature-based tourism on the economies surrounding three terrestrial and two marine PAs. Simulations suggest that nature-based tourism creates significant economic benefits for communities around PAs, including the poorest households, and many of these benefits are indirect, via income and production spillovers. An additional tourist increases annual real income in communities near the PAs by US$169-$2,400, significantly more than the average tourist's expenditure. Conversely, lost tourism due to the COVID-19 pandemic and economic costs of human-wildlife conflict have disproportionately large negative impacts on local incomes.

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.

Governments, researchers, and developers emphasize creating trustworthy AI, defined as AI that prevents bias, ensures data privacy, and generates reliable results that perform as expected. However, in some cases problems arise not when AI is not trustworthy, technologically, but when it is. This article focuses on such problems in the food system. AI technologies facilitate the generation of masses of data that may illuminate existing food-safety and employee-safety risks. These systems may collect incidental data that could be used, or may be designed specifically, to assess and manage risks. The predictions and knowledge generated by these data and technologies may increase company liability and expense, and discourage adoption of these predictive technologies. Such problems may extend beyond the food system to other industries. Based on interviews and literature, this article discusses vulnerabilities to liability and obstacles to technology adoption that arise, arguing that trustworthy AI cannot be achieved through technology alone, but requires social, cultural, political, as well as technical cooperation. Implications for law and further research are also discussed.

Transformation toward a sustainable future requires an earth stewardship approach to shift society from its current goal of increasing material wealth to a vision of sustaining built, natural, human, and social capital-equitably distributed across society, within and among nations. Widespread concern about earth's current trajectory and support for actions that would foster more sustainable pathways suggests potential social tipping points in public demand for an earth stewardship vision. Here, we draw on empirical studies and theory to show that movement toward a stewardship vision can be facilitated by changes in either policy incentives or social norms. Our novel contribution is to point out that both norms and incentives must change and can do so interactively. This can be facilitated through leverage points and complementarities across policy areas, based on values, system design, and agency. Potential catalysts include novel democratic institutions and engagement of non-governmental actors, such as businesses, civic leaders, and social movements as agents for redistribution of power. Because no single intervention will transform the world, a key challenge is to align actions to be synergistic, persistent, and scalable.